Analysis method and devices for same

Abstract

In order to provide a method for predicting process deviations in an industrial-method plant, for example a painting plant, by means of which process deviations are predictable simply and reliably, it is proposed according to the invention that the method should comprise the following: automatic generation of a prediction model; prediction of process deviations during operation of the industrial-method plant, using the prediction model.

Claims

1. A method for predicting process deviations in an industrial-method plant, the method comprising: automatically generating a prediction model, wherein, for generating the prediction model, process values and/or status variables measured by a sensor are stored during operation of the industrial-method plant for a predetermined period, and wherein the predetermined period for which process values and/or status variables are stored during operation of the industrial-method plant is predetermined in dependence at least one of: (i) the industrial-method plant is in an operation-ready state, in particular for a production operation, for at least 60% of the predetermined period, (ii) the industrial-method plant is in a production-ready state for at least 60% of the predetermined period, (iii) a predetermined number of process deviations and/or disruptions in the predetermined period; and predicting process deviations during operation of the industrial-method plant, using the prediction model, wherein the method for predicting process deviations is carried out in an industrial supply air plant, a pre-treatment station, a station for cathodic dip coating and/or a drying station.

2. The method according to claim 1, wherein process deviations of production-critical process values in the industrial-method plant are predicted by the prediction model, on the basis of changing process values during operation of the industrial-method plant.

3. The method according to claim 1, wherein for generating the prediction model, a machine learning method is utilized, and wherein the process values and/or status variables that are stored for the predetermined period are used for generating the prediction model.

4. The method according to claim 3, wherein the machine learning method is carried out on the basis of features that are extracted from the process values and/or status variables stored for the predetermined period.

5. The method according to claim 4, wherein one or more of the following is used for extracting features: statistical key figures; coefficients from a principal component analysis; linear regression coefficients; and dominant frequencies and/or amplitudes from a Fourier spectrum.

6. The method according to claim 1, wherein a selected number of prediction data sets with process deviations and a selected number of prediction data sets with no process deviations are used for training the prediction model.

7. The method according to claim 6, wherein selection of the number of prediction data sets with a process deviation is made on the basis of one or more of: a minimum time interval between two prediction data sets with process deviations; an automatic selection on the basis of defined rules; and a selection by a user.

8. The method according to claim 6, wherein prediction data sets with process deviations are characterised as such if a process deviation occurs within a predetermined time interval.

9. The method according to claim 8, wherein the process values and/or status variables that are stored for the predetermined period are grouped into prediction data sets by pre-processing.

10. The method according to claim 9, wherein the pre-processing includes the following: regularisation of the process values stored for the predetermined period; and grouping the process values and/or status variables into prediction data sets by classifying the process values and/or status variables into time windows with a time offset.

11. The method according to claim 1, further including displaying or providing the process deviations.

12. The method according to claim 11, wherein the process deviations are displayed on a diagnostic window.

13. The method according to claim 1, further including: determining a fault of the industrial-method plant based on the process deviations; and indicating the fault on a diagnostic window.

14. The method according to claim 1, wherein generation of the prediction model includes: determining a time series into time frames, and determining key variables of the time frames.

15. The method according to claim 1, further including: determining a fault cause; and determining relevant process values to be associated with the determined fault cause.

16. A prediction system for predicting process deviations in an industrial-method plant, wherein the prediction system takes a form and is constructed for carrying out the method for predicting process deviations in an industrial-method plant, according to claim 1.

17. An industrial control system that includes the prediction according to claim 16.

18. A system for predicting process deviations in an industrial-method plant, the system comprising: a sensor to measure a process value; and an industrial controller to: generate a prediction model, wherein to generate the prediction model, process values and/or status variables are stored during operation of the industrial-method plant for a predetermined period, and wherein the predetermined period for which process values and/or status variables are stored during operation of the industrial-method plant is predetermined in dependence at least one of: (i) the industrial-method plant is in an operation-ready state, for a production operation, of at least 60% of the predetermined period, (ii) the industrial-method plant is in a production-ready state for at least 60% of the predetermined period, (iii) a predetermined number of process deviations and/or disruptions in the predetermined period; and predict, based on the process value, process deviations during operation of the industrial-method plant, using the prediction model, wherein the system for predicting process deviations is carried out in an industrial supply air plant, a pre-treatment station, a station for cathodic dip coating and/or a drying station.

19. The system according to claim 18, wherein the generated prediction model corresponds to an occurrence probability of respective process values.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 shows a schematic representation of an industrial-method plant and an industrial control system;

(2) FIG. 2 shows a schematic representation of an industrial-method plant, in particular a painting plant;

(3) FIG. 3 shows a schematic representation of an industrial supply air plant;

(4) FIG. 4 shows the schematic representation of the industrial supply air plant from FIG. 3 on the occurrence of a fault situation;

(5) FIG. 5 shows the schematic representation of the industrial supply air plant from FIG. 3 on the occurrence of a further fault situation;

(6) FIG. 6 shows the schematic representation of the industrial supply air plant from FIG. 3 on the occurrence of a further fault situation;

(7) FIG. 7 shows a further schematic representation of an industrial supply air plant;

(8) FIG. 8 shows the schematic representation of the industrial supply air plant from FIG. 7 in an operating state with no process deviation;

(9) FIG. 9 shows the schematic representation of the industrial supply air plant from FIG. 7 in an operating state with a process deviation as a result of changing ambient conditions;

(10) FIG. 10 shows the schematic representation of the industrial supply air plant from FIG. 7 in an operating state with a process deviation as a result of switching on a heat recovery system;

(11) FIG. 11 shows the schematic representation of the industrial supply air plant from FIG. 7 in an operating state with a process deviation resulting from the failure of a valve;

(12) FIG. 12 shows a schematic representation of process values that are grouped into prediction data sets;

(13) FIG. 13 shows a schematic representation of the prediction data sets from FIG. 12, which are labelled as prediction data sets with process deviations and prediction data sets with no process deviations;

(14) FIG. 14 shows a schematic representation of a pre-treatment station;

(15) FIG. 15 shows a schematic representation of method steps for generating an anomaly and/or fault model of the pre-treatment station;

(16) FIG. 16 shows a schematic representation of a graph having a process structure derived from the pre-treatment station from FIG. 14;

(17) FIG. 17 shows a clique of a factor graph;

(18) FIG. 18 shows a model of a functional relationship in the clique from FIG. 17; and

(19) FIG. 19 shows a clique corresponding to the clique from FIG. 17, which has been expanded by one node by allocating a fault cause.

(20) Like or functionally equivalent elements are provided with the same reference numerals in all the Figures.

DETAILED DESCRIPTION OF THE DRAWINGS

(21) FIG. 1 shows an industrial control system that is designated 100 as a whole, for an industrial-method plant 101.

(22) The industrial-method plant 101 is for example a painting plant 104, which is illustrated in particular in FIG. 2.

(23) In particular a method for fault analysis in the industrial-method plant 101, in particular the painting plant 102, is explained with reference to FIGS. 1 to 6.

(24) In particular a method for predicting process deviations in the industrial-method plant 101, in particular the painting plant 102, is explained with reference to FIGS. 1, 2 and 7 to 13.

(25) In particular a method for anomaly and/or fault recognition in the industrial-method plant 101, in particular the painting plant 102, is explained with reference to FIGS. 1, 2 and 14 to 19.

(26) The industrial-method plant 101, in particular the painting plant 102, that is illustrated in FIG. 2 preferably comprises a plurality of treatment stations 104 for treating workpieces 106, in particular for treating vehicle bodies 108.

(27) In the embodiment of the painting plant 102 that is illustrated in FIG. 2, the treatment stations 104 are in particular connected in a sequence and so form a painting line 110.

(28) For the purpose of treating workpieces 106, in particular for the purpose of painting vehicle bodies 108, the workpieces 106 preferably pass through the treatment stations 104 one after the other.

(29) For example, it is conceivable for a workpiece 106 to pass through successive treatment stations 104 in the order indicated.

(30) A workpiece 106 is pre-treated in a pre-treatment station 112 and conveyed from the pre-treatment station 112 to a station for cathodic dip coating 114.

(31) After the application of a coating to the workpiece 106, it is conveyed from the station for cathodic dip coating 114 to a drying station 116 downstream of the station for cathodic dip coating 114.

(32) After drying, in the drying station 116, of the coating that was applied to the workpiece 106 in the station for cathodic dip coating 114, the workpiece 106 is preferably conveyed to a base coat booth 118, in which once again a coating is applied to the workpiece 106.

(33) After the application of the coating in the base coat booth 118, the workpiece 106 is preferably conveyed to a base coat drying station 120.

(34) After drying, in the base coat drying station 120, of the coating that was applied to the workpiece 106 in the base coat booth 118, the workpiece 106 is preferably conveyed to a clear coat booth 122, in which a further coating is applied to the workpiece 106.

(35) After the application of the coating in the clear coat booth 122, the workpiece 106 is preferably conveyed to a clear coat drying station 124.

(36) After drying, in the clear coat drying station 124, of the coating that was applied to the workpiece 106 in the clear coat booth 122, the workpiece 106 is preferably fed to an inspection station 126 at the end of the production process.

(37) In the inspection station 126, a quality inspection is preferably carried out by a quality inspector, for example by means of a visual inspection.

(38) The industrial-method plant 101, in particular the painting plant 102, preferably further comprises an industrial supply air plant 128 for conditioning the air that is supplied for example to the base coat booth 118 and/or the clear coat booth 122.

(39) By means of the industrial supply air plant 128, a temperature and/or relative air humidity of the air supplied to the base coat booth 118 and/or the clear coat booth 122 is preferably adjustable.

(40) By means of the industrial control system 100, preferably a production process, in particular a painting process, is controllable in treatment stations 104 of the industrial-method plant 101, in particular the painting plant 102.

(41) Preferably, for this purpose the industrial control system 100 comprises a process checking system 130.

(42) Further, the industrial control system 100 illustrated in FIG. 1 preferably comprises a database 132.

(43) The database 132 of the industrial control system 100 preferably comprises a process database 134 and a fault database 136.

(44) It may further be favourable if the industrial control system 100 comprises a message system 138 and an analysis system 140.

(45) Further, the industrial control system 100 preferably comprises a display system 142 by means of which information is displayable to a user.

(46) Preferably here, the display system 142 comprises one or more screens on which information is presentable.

(47) The analysis system 140 preferably comprises or is formed by a fault analysis system 144.

(48) It may further be favourable if the message system 138 comprises or is formed by a prediction system 146 for predicting process deviations in the industrial-method plant 101.

(49) As an alternative or in addition, it is conceivable for the message system 138 to comprise an anomaly and/or fault recognition system 148.

(50) The fault analysis system 144 in particular takes a form and is constructed to carry out methods for fault analysis in the industrial-method plant 101, which are explained with reference to FIGS. 1 to 6.

(51) The prediction system 146 in particular takes a form and is constructed to carry out the method for predicting process deviations in the industrial-method plant 101, which are explained with reference to FIGS. 1, 2 and 7 to 13.

(52) The anomaly and/or fault recognition system 148 is in particular constructed to carry out methods for anomaly and/or fault recognition in the industrial-method plant 101, which are explained with reference to FIGS. 1, 2 and 14 to 19.

(53) The industrial supply air plant 128 that is illustrated in FIGS. 3 to 6 preferably comprises a plurality of conditioning modules 150, for example a pre-heating module 154, a cooling module 156, a post-heating module 158 and/or a wetting module 160.

(54) For example, the industrial supply air plant 128 of the painting plant 102 is a functional unit, wherein a conditioning module 150 of the supply air plant 128 is a functional group and a circulation pump 152 of the supply air plant is a functional element (cf. FIGS. 3 to 6).

(55) In addition to the circulation pumps 152 of the pre-heating module, the cooling module 156 and the post-heating module 158, the industrial supply air plant 128 preferably further comprises a wetting pump 153 of the wetting module 160.

(56) It may further be favourable if the supply air plant 128 comprises a ventilator 162.

(57) The supply air plant 128 preferably further comprises a heat recovery system 164 for the purpose of heat recovery.

(58) Preferably, an air stream 165 is suppliable to the supply air plant 128 from an area surrounding it.

(59) An air stream 167 that is conditioned by means of the supply air plant 128 is preferably suppliable to the base coat booth 118 and/or the clear coat booth 122.

(60) Preferably, the supply air plant comprises sensors (not represented in the drawings of the Figures) by means of which process values are detectable.

(61) For example, detectable by means of the sensors are the following process values, which are preferably respectively designated by means of a reference numeral in FIGS. 3 to 6: external temperature 166; external humidity 168; temperature of the air 170 conditioned by means of the industrial supply air plant; humidity of the air 172 conditioned by means of the industrial supply air plant; volumetric flow rates 174, 176, 178 in the conditioning modules 150; valve positions 180, 182, 184 of valves 181, 183, 185 in the conditioning modules 150.

(62) Further, it may be favourable if a rotational frequency 193 of the wetting pump 153 and a rotational frequency 195 of the ventilator 194 are detected.

(63) Preferably, the process values 166 to 184 are stored in the process database 134.

(64) Further, it may be provided for the following status variables to be detected, which are preferably likewise respectively designated by means of a reference numeral in FIGS. 3 to 6: pump status 186, 188, 190 of the circulation pumps 152 of the conditioning modules 150 and pump status 192 of the wetting pump 153 (on/off); valve status 194 (on/off); valve status 196, 198, 200 of the conditioning modules 150 (open/closed); status 202 of the heat recovery system 164 (on/off).

(65) Preferably, the status variables 186 to 202 are also stored in the process database 134.

(66) The method for fault analysis in the industrial-method plant 101 is now explained preferably with reference to FIGS. 3 to 6.

(67) Here, the industrial supply air plant 128 in particular forms the industrial-method plant 101.

(68) Various exemplary situations are described below, from which functioning of the fault analysis system 144 can be seen.

(69) Exemplary Situation 1 (cf. FIG. 4):

(70) (Valve Leak)

(71) A valve leak occurs in the pre-heating module 154. A volumetric flow rate 174 of >0 is measured. The pump status 186 is off and the valve status 196 of the control valve is closed.

(72) The fault situation is stored in the message system as an item of logic (pump status 186=off; volumetric flow rate 174>0; valve status 196 closed). Thus, the message system has preferably stored the process and status values as a prior link.

(73) The fault analysis steps on occurrence of a message as a result of the valve leak are preferably the following: 1) The message is displayed to a user by means of the display system 142, for example by means of a screen of the display system 142. 2) A user wishes to analyse the situation, and opens a diagnostic window. 3) The process value 174 that is linked to the message and the status variables 186 and 196 are displayed to the user in the diagnostic window. Preferably, the fault analysis system 144 receives this information directly from the message system 138. 4) The process values linked to the fault situation are preferably not prioritised, since only the process value 174 is associated with the fault situation. 5) The user stores the fault situation, with the link, in the fault database 136. 6) If the fault situation valve leak occurs again at the same or a comparable valve, the comparable fault situation is preferably displayed to the user.

(74) Exemplary Situation 2 (cf. FIG. 5):

(75) (Excessive Temperature 170 of the Air That is Conditioned by Means of the Industrial Supply Air Plant 128, as a Result of Too High an External Temperature 166)

(76) The temperature 170 of the air that is conditioned by means of the industrial supply air plant 128 is too high, because the external temperature 166 is outside a design window of the industrial supply air plant 128.

(77) When the temperature 170 of the air conditioned by means of the industrial supply air plant 128 departs from a predetermined process window, the message is generated and the message system 138 sends it to the display system 142.

(78) The message is linked to the value of the temperature 170 of the air that is conditioned by means of the industrial supply air plant 128. However, the message is not linked to the external temperature 166.

(79) The fault analysis steps when the message arises as a result of temperature change are preferably the following: 1) The message is displayed to a user by means of the display system 142, for example by means of a screen of the display system 142. 2) A user wishes to analyse the situation, and opens a diagnostic window. 3) The process value 170 that is linked to the message is displayed to the user in the diagnostic window. 4) The process values linked to the fault situation are preferably not prioritised, since only the process value 166 is associated with the fault situation. 5) The following process values are additionally preferably proposed to the user: humidity of the air 172 conditioned by means of the industrial supply air plant (process-critical variable, displays an anomaly in behaviour); external temperature 166 (displays an anomaly in behaviour); external humidity 168 (displays an anomaly in behaviour). 6) The user selects the proposed process values and adds them to the fault situation. 7) The user can capture the cause of the temperature deviation directly from the analysis system 140, in particular from the fault analysis system 144, since the relevant process values are proposed to the user. 8) The user adds documentation to the fault situation, with a proposal for eliminating the fault. 9) The user stores the fault situation, with the link and the documents, in the fault database. 10) In addition to a fault ID, a fault classification (temperature increase), a fault location (exhaust part of the industrial supply air plant 128), the fault analysis system 144 preferably also captures references (IDs) of the process variables 170, 172, 166, 168 in the prioritised order and a quantity of features (such as averages, minimum, maximum, scatter of the process variables during occurrence of the fault situation) and the duration from occurrence of the fault situation until the point in time of storage or the end of the fault situation.

(80) Exemplary Situation 3 (cf. FIG. 5):

(81) (Excessive Temperature 170 of the Air That is Conditioned by Means of the Industrial Supply Air Plant as a Result of Too High an External Temperature)

(82) The temperature 170 of the air that is conditioned by means of the industrial supply air plant is again too high because of the external temperature. The fault pattern is similar to exemplary situation 2.

(83) The analysis steps when the message arises as a result of temperature change are preferably the following: 1) The message is displayed to a user by means of the display system 142, for example by means of a screen of the display system 142. 2) The user wishes to analyse the fault situation, and opens a diagnostic window. 3) The process values 170, 172, 166, 168 that are linked to the message are displayed to the user in the diagnostic window, in the order indicated. 4) A process list and its prioritisation are produced from a similar fault situation. The similarity to the fault situation from exemplary situation 2 is determined by the fault analysis system 144 by a metric matching of the process values. 5) The similar fault situation is displayed to the user. 6) The user can utilise the courses of the process values in the prioritised order shown, and the documents of the similar fault situation displayed, in order to find a solution. 7) The user stores the fault situation.

(84) Exemplary Situation 4 (cf. FIG. 6):

(85) (Disruption in a Supply System)

(86) Too little combustion gas is supplied to a burner in the pre-heating module 154. The volumetric flow rate 174 falls.

(87) In order to receive more combustion gas, the valve 181 of the pre-heating module 154 is opened further and the valve position 180 changes.

(88) The valve 185 of the post-heating module 158 also opens in order to compensate for the disruption in the pre-heating module 154. The valve position 184 changes.

(89) Because of the low external temperature 166, the disruption cannot be compensated, and the temperature 170 of the air conditioned by means of the industrial supply air plant plummets.

(90) The analysis steps when the message arises as a result of the disruption in the supply system are preferably the following: 1) The message is displayed to a user by means of the display system 142, for example by means of a screen of the display system 142. 2) The user wishes to analyse the fault situation, and opens a diagnostic window. 3) The process value 170 that is linked to the message is displayed to the user in the diagnostic window, as a result of prior prioritisation in the message system 138. 4) No prioritisation takes place. 5) The following process values are proposed: humidity of the air 172 conditioned by means of the industrial supply air plant (process-critical variable, displays an anomaly in behaviour); volumetric flow rate 174, valve position 180, valve position 186 (dependent on deviation from normal condition). 6) The fault situations from exemplary situations 2 and 3 are not classified as similar (different signal behaviour because the metric distance between the process values is high). 7) The proposed process values can be added to the fault situation and stored in the fault database.

(91) The method for predicting process deviations in the industrial-method plant 101 is now explained preferably with reference to FIGS. 7 to 13.

(92) If the industrial-method plant 101 is an industrial supply air plant 128, stored process values and/or status variables preferably comprise the following (cf. FIG. 7): target variables 204 of the industrial supply air plant 128, in particular temperature 170 and relative air humidity 172 of the air conditioned by means of the industrial supply air plant 128, in particular at an exhaust part of the industrial supply air plant 128; control variables 206, in particular valve positions 180, 182, 184 of valves of heating and/or cooling modules 154, 156, 158 of the industrial supply air plant 128, rotational frequencies 193 of pumps 152, in particular the wetting pump 153, and/or rotational frequencies 195 of ventilators 162; internal variables 208, in particular supply and/or return flow temperatures 210 in the heating and/or cooling modules 154, 156, 158 of the industrial supply air plant 128 and/or air conditions between conditioning modules 150; measured disruption variables 210, in particular external temperature 166 and/or external relative air humidity 168 at an intake part of the industrial supply air plant 128; unmeasured disruption variables 212; and/or status variables 214, in particular wetting pump 153 (on/off); manual mode for pumps 152, 153 (on/off); feed valves 181, 183, 185, ventilator 162 (on/off).

(93) Various exemplary operating states are described below, from which functioning of the prediction system 146 can be seen.

(94) Exemplary Operating State 1:

(95) (No Deviation)

(96) FIG. 8 shows a first exemplary operating state of the industrial supply air plant 128 with no process deviation. Thus, the first exemplary operating state preferably represents a positive case.

(97) The external temperature 166 and external humidity 168 are not constant. The pre-heating module 154 and wetting module 160 are active.

(98) A control system of the industrial supply air plant 128 keeps the temperature 170 and the relative air humidity 172 of air conditioned by means of the industrial supply air plant 128 at a constant value.

(99) In accordance with the status variables 214 (ventilator 162=on and valve and pump mode=automatic), the industrial supply air plant 128 is operation-ready.

(100) Further, because of the constant temperature 170 and relative air humidity 172 of the air that is conditioned by means of the industrial supply air plant 128, the industrial supply air plant 128 is preferably production-ready.

(101) Exemplary Operating State 2:

(102) (Increase in the External Temperature 166 with Fall in the External Relative Humidity 168, Above the Technical Parameters; Effect of a Measured Disruption Variable 210 on Target Variable 204)

(103) FIG. 9 shows a second exemplary operating state of the industrial supply air plant 128 with an increase in the external temperature 166 at the same time as a fall in the external relative humidity 168 as a result of a sudden change in the weather.

(104) The process values display the following behaviour: a) The increased external temperature 166 after the sudden change in the weather is outside the technical parameters. b) The power of the previously active pre-heater module 154 is reduced by the controller. c) Because the reduction in the heating power is not sufficient, the controller opens the valve 183 of the cooling module 156 and hence increases the cooling power. d) The rotational frequency 193 of the wetting pump 153 is increased by the controller in order to compensate for the falling external humidity 168. e) Because, as a result of the insufficient design of the cooling module 156, the cooling power is not sufficient, there is a deviation in the temperature 170 of the air that is conditioned by means of the industrial supply air plant 128.

(105) A departure from the predetermined process window for the temperature 170 is delayed as a result of the inertia of the industrial supply air plant 128 and compensation by the controller.

(106) Exemplary Operating State 3:

(107) (Changeover to Winter Operation with Heat Recovery; Effect of an Unmeasured Disruption Variable 212 on Target Variable 204)

(108) FIG. 10 shows a third exemplary operating state of the industrial supply air plant 128 when the heat recovery system 164, which uses waste heat to heat the air stream 165 in cold climatic conditions, is switched on.

(109) The heat recovery system 164 is switched on by a manual valve, and for this reason the effect of heat recovery by the heat recovery system 164 is not measurable (unmeasured disruption variable 212).

(110) The process values display the following behaviour: a) The heating power of the heat recovery system 164 is increased; the value is not measurable. b) The valve 181 of the pre-heating module 154 closes because of the increase in heating power. c) In the cooling module 156, the valve 183 opens in order to maintain the temperature by additional cooling power. d) The rotational frequency 193 of the wetting pump 153 is adapted by the controller such that the air humidity 172 of the air conditioned by means of the industrial supply air plant 128 is maintained. e) There is a deviation in the temperature 170 of the air conditioned by means of the industrial supply air plant 128 because the cooling module 156 cannot compensate for the heat supply quickly enough. Because of the inertia of the industrial supply air plant 128 and compensation by the controller, the deviation occurs with a delay.

(111) Exemplary Operating State 4:

(112) (Failure of the Valve 181 of the Pre-Heating Module 154)

(113) FIG. 11 shows a fourth exemplary operating state of the industrial supply air plant 128 when there is a failure of the valve 181 of the pre-heating module 154.

(114) The process values display the following behaviour: a) The valve 181 of the pre-heating module 154 closes because of a device fault, as a result of which the heating power falls. b) The valve 185 of the post-heating module 158 opens in order to compensate for the lacking heating power. c) Because the heating power of the post-heating module 158 is not sufficient, there is a deviation in the temperature 170 of the air conditioned by means of the industrial supply air plant 128.

(115) The method for predicting process deviations in the industrial-method plant 101, in particular in the industrial supply air plant, is explained below with reference to the operating states 1 to 4 described above.

(116) Preferably, the operating states 2 to 4 with process deviations during operation of the industrial supply air plant 128 are predictable by means of the method for predicting process deviations with a prediction horizon 216 of for example approximately 15 minutes.

(117) As the data basis for training a prediction model, a timespan is considered in which the industrial supply air plant 128 runs in normal operation (>80%) in an operating state that is ready for use (cf. exemplary operating state 1).

(118) The recorded data contain the exemplary operating states 2 to 4, preferably in each case multiple times. These may have occurred in ongoing operation, or as an alternative may have been brought about deliberately, for example by closing a valve 181, 183, 185.

(119) The data are preferably then pre-processed and regularised, as can be seen for example from FIG. 12. There, the valve position 180 of the valve 181 of the pre-heating module 154 and the temperature 170 and relative air humidity 172 of the air conditioned by means of the industrial supply air plant 128 are recorded as examples.

(120) The regularised data are divided for example into time windows 218 of 30 minutes, in each case with a time offset of for example 5 minutes.

(121) The data regularised into time windows 218 in particular form prediction data sets, in particular prediction data sets with no process deviations 220 and prediction data sets with a process deviation 222 (cf. FIG. 7).

(122) For the prediction data sets with a process deviation 222, the status variables 214 are used to check whether the industrial-method plant 101 was operation-ready (for example, ventilator 162 on, conditioning modules 150 in automatic mode). If not: corresponding prediction data sets with a process deviation 222 are rejected and are not used for training the prediction model. If it was: corresponding prediction data sets with a process deviation 222 are contenders for training the prediction model.

(123) Because of a minimum time interval of for example one hour, in FIG. 7 only one prediction data set with a process deviation 222 is selected.

(124) Preferably, selection of the prediction data sets with no process deviations 220 is performed analogously to selection of the prediction data sets with a process deviation 222.

(125) With a minimum time interval of for example one hour, only one prediction data set with no process deviations 220 is selected for training the prediction model.

(126) Then, features are preferably extracted from the selected prediction data sets with no process deviations 220 and the selected prediction data sets with process deviations 222.

(127) For the purpose of extracting the features, there are used for example statistical key figures, for example minimum, maximum, median, average and/or standard deviation. It may further be favourable if linear regression coefficients are used for extracting the features.

(128) Preferably, the prediction model is trained on the basis of the extracted features from the selected prediction data sets with no process deviations 220 and on the basis of the selected prediction data sets with process deviations 222, in particular by means of a machine learning method, for example by means of gradient boosting.

(129) Using the trained prediction model, process deviations of production-critical process values in the industrial supply air plant 128 are preferably predicted on the basis of changing process values during operation of the industrial supply air plant 128.

(130) In particular, the prediction model is explained with reference to exemplary operating states 2 to 4:

(131) Exemplary Operating State 2:

(132) The prediction model predicts a process deviation after the occurrence of an increase in temperature. The basis for this is the measured disruption variables 210, in particular external temperature 166 and external humidity 168, the response of the conditioning modules 150, and the course of the temperature 170 of the air conditioned by means of the industrial supply air plant 128 at the exhaust part.

(133) Exemplary Operating State 3:

(134) The prediction model predicts an increase, after the heat recovery system is switched on, in the temperature 170 of the air conditioned by means of the industrial supply air plant 128. The basis is the response of the conditioning modules 150 and the course of the temperature 170 of the air conditioned by means of the industrial supply air plant 128 at the exhaust part.

(135) Exemplary Operating State 4:

(136) The prediction model predicts an increase in the temperature 170 of the air conditioned by means of the industrial supply air plant 128, on the basis of the weather conditions and the valve position 180 of the valve 181 of the pre-heating module 156.

(137) The method for anomaly and/or fault recognition in the industrial-method plant 101 is now explained preferably with reference to FIGS. 14 to 19.

(138) Fault situations, in particular defects and/or failures in components, sensors and/or actuators, are preferably identifiable by means of the method for anomaly and/or fault recognition.

(139) Here, the pre-treatment station 112 for example forms the industrial-method plant 101.

(140) Preferably, the pre-treatment station 112 comprises a pre-treatment tank 224 in which workpieces 106, preferably vehicle bodies 108, are pre-treatable.

(141) Preferably, the pre-treatment station 112 further comprises a first pump 226, a second pump 228, a heat exchanger 230 and a valve 232.

(142) The process values V62dot, S86, T95, T85, T15 and T05 are given their designation on the basis of an unambiguous designation in a numbering system of the industrial-method plant 101.

(143) The process values T95, T85, T15 and T05 represent in particular temperatures within the industrial-method plant 101, in particular within the pre-treatment station 112.

(144) The process value S86 is a valve position of the valve 232.

(145) The process value V62dot is a volumetric flow rate.

(146) Preferably, for the purpose of carrying out the method for anomaly and/or fault recognition, an anomaly and/or fault model 233 of the industrial-method plant 101, in particular the pre-treatment station 112, is generated, comprising information on the occurrence probability of the above-mentioned process values (cf. FIG. 15).

(147) The anomaly and/or fault model 233 is preferably generated as follows:

(148) First, test signals are generated, in particular taking into account technical data 234 in the context of test signal generation 236.

(149) In particular, limits for the test signals are predetermined on the basis of the technical data 234, for example, when predetermining jump functions, a maximum amplitude for control variable jumps.

(150) The technical data 234 comprise for example one or more of the following items of information: sensor type (temperature sensor, throughflow sensor, valve position, pressure sensor, etc.) and/or actuator type (valve, ventilator, damper, electric motor); permissible value ranges of sensors and/or actuators; signal type of sensor and/or actuator (float, integer).

(151) The industrial-method plant 101, in particular the pre-treatment station 112, is preferably activated dynamically by means of the test signals. This is indicated in FIG. 15 by the reference numeral 238. Here, it is conceivable for anomalies and/or fault situations to be generated deliberately on activation with test signals.

(152) During activation of the industrial-method plant 101, in particular the pre-treatment station 112, by test signals, preferably system input signals 240 and system output signals 242 are generated.

(153) The system input signals 240 and system output signals 242 are preferably stored in a test signal database 244.

(154) Then, preferably a structure identification 246 of the industrial-method plant 101, in particular the pre-treatment station 112, is carried out. Here, preferably a structure graph 247 of the industrial-method plant 101, in particular the pre-treatment station 112, is determined (cf. FIG. 16).

(155) The structure identification 246, in particular determination of the structure graph, is preferably performed using a machine learning method, preferably using correlation coefficients by means of which non-linear relationships are reproducible, for example by means of mutual information.

(156) It may further be favourable if, for the purpose of structure identification 246, expert knowledge 248 is used, that is to say in particular knowledge of relationships in the process.

(157) Here, for example edges between nodes of the structure graph that is to be determined can be eliminated by a pre-configuration of the structure graph, by means of information from expert knowledge, known circuit diagrams and/or procedure diagrams 250. In particular here, processing work for determining the structure graph is reducible.

(158) It may further be favourable if the structure graph is determined using the respectively unambiguous designation of the process values by way of a numbering system of the industrial-method plant 101, in particular the pre-treatment station 112, that is to say using semantics 252 of the designation of the process values.

(159) In particular, it is conceivable for the structure graph that is determined by means of the machine learning method to be checked for plausibility by means of expert knowledge 248, known circuit diagrams and/or procedure diagrams 250 and/or the designations in the numbering system of the industrial-method plant 101 (semantics 252).

(160) Preferably, causalities 254 in the determined process structure are then determined, in particular directions marked by arrows in the determined structure graph.

(161) Causalities 254 in the determined process structure are derived for example from system input signals 240 and system output signals 242 of the industrial-method plant 101 that are determined during activation of the industrial-method plant 101 by test signals, for example by way of the respective temporal course of the system input signals 240 and system output signals 242.

(162) As an alternative or in addition, it is conceivable for causalities 254 to be derived from system input signals 240 and system output signals 242 that are determined during activation of the industrial-method plant 101 by test signals, by means of causal inference methods.

(163) For determining the causalities 254, there are further preferably used expert knowledge 248, procedure diagrams 250 and/or the designations in the numbering system of the industrial-method plant 101 (semantics 252).

(164) Preferably, the process values that cause a recognised anomaly are locatable by means of the causalities 254 determined in the determined process structure or in the determined structure graph.

(165) After the structure identification 246 and/or determination of the causalities 254, preferably a structure parameterisation 256 is carried out.

(166) Preferably, the structure identification 246 is configured to facilitate structure parameterisation 256. Preferably, the structure identification 246 is configured to reduce processing work for the structure parameterisation 256.

(167) Preferably, the structure parameterisation 256 is performed using a method for determining probability density functions, in particular using Gaussian mixture models.

(168) The structure parameterisation 256 is carried out for example for the common probability density function f1 of the clique 258 represented in FIG. 17 using Gaussian mixture models (cf. FIG. 18).

(169) Preferably, expert knowledge 248 is likewise used for the structure parameterisation 256.

(170) For the purpose of structure parameterisation 256, for example known physical relationships between process values and/or physical characteristic diagrams of functional elements of the industrial-method plant 101, in particular the pre-treatment station, are used. For example, a characteristic diagram of the valve 232 is used.

(171) It may further be favourable if expert knowledge 248 on fault situations is used for structure parameterisation 256.

(172) For example, a relationship between the valve position S86 and the volumetric flow rate V62dot is describable by means of a known valve characteristic diagram of the valve 232.

(173) Data that are stored in an operations database 260 from regular operation of the industrial-method plant 101, in particular the pre-treatment station 112, and/or data from the test signal database 244 are preferably used for the purpose of structure parameterisation 256 using methods for determining probability density functions, in particular using Gaussian mixture models.

(174) For example, control, measurement and/or regulating variables that are stored in particular in a database 244, 260 are used for the purpose of structure parameterisation 256 using methods for determining probability density functions.

(175) Preferably, for the purpose of structure parameterisation 256 using methods for determining probability density functions, data from ongoing operation of the industrial-method plant 101, in particular the pre-treatment station, are used, and these are stored for a period of at least 2 weeks, preferably at least 4 weeks, for example at least 8 weeks.

(176) The data are preferably pre-processed before the structure parameterisation 256.

(177) During the pre-processing, preferably data from the industrial-method plant 101 that are not associated with operation-ready or production-ready operating states of the industrial-method plant 101 (for example plant switched off, maintenance phases, etc.) are eliminated in particular by way of alarms and status bits that describe the state of the industrial-method plant 101, in particular the pre-treatment station 112.

(178) Further, it may be favourable if data from the industrial-method plant 101 are pre-processed by filtering, for example by means of low-pass filters and/or Butterworth filters.

(179) Preferably, the data are further interpolated at a consistent time interval.

(180) During generation of the anomaly and/or fault model 233, a limit value for the occurrence probability of a process value is preferably established in the context of a limit value optimisation 264.

(181) The limit value for the occurrence probability is preferably established such that if this falls below the limit value an anomaly is recognised.

(182) The limit value is preferably established by means of a non-linear optimisation method, for example by means of the Nelder-Mead method.

(183) As an alternative or in addition, it is conceivable to establish the limit value by means of quantiles.

(184) Limit values for the occurrence probability of the process values are preferably optimisable, for example by predetermining a false-positive rate.

(185) Further, it is conceivable for the limit values to be adapted after the first generation of the anomaly and/or fault model 233, in particular in the event of too high a number of false alarms.

(186) Preferably, anomaly and/or fault recognition is performed using the anomaly and/or fault model 233 as follows:

(187) For example, the valve 232 undergoes valve failure and thus the sensor values deviate from the mapped normal condition in the individual cliques.

(188) The occurrence probabilities of the sensor values in the cliques are evaluated during operation of the industrial-method plant 101, in particular of the pre-treatment station 112, and if they fall below the calculated limit values anomalies are detected in the different cliques.

(189) Valve failure of the valve 232 results initially in an anomaly in the clique 258 of the valve position S86, wherein a message is output by the anomaly and/or fault recognition system 148.

(190) As time continues, as a result of fault propagation further anomalies are produced, which later also affect the process-relevant variable, for example the tank temperature T35 of the tank 224.

(191) Preferably, the message from the anomaly and/or fault recognition system 148 contains one or more of the following items of information: point in time at which the anomaly was detected; clique(s) in which the anomaly occurred, with sensors affected.

(192) As a result of early recognition of the anomaly and the message to the user, with prompt intervention it is preferably possible to prevent deviation of the process-relevant variable, that is to say the tank temperature T35 of the tank 224.

(193) The user can then define a cause of the fault (that is to say the valve failure) for occurrence of the anomaly.

(194) As a result of allocating the fault cause, the clique 258 is expanded by one node 266 and the probability density function of the anomalous data is integrated into the functional relationship (cf. FIG. 19).

(195) After integration of the fault cause, the method for anomaly and/or fault recognition is carried out as before. If an anomaly occurs, the probabilities of the defined fault causes are additionally output.

(196) As a result of the message from the anomaly and/or fault recognition system 148, a user now receives one or more of the following items of information: point in time at which the anomaly was detected; clique(s) in which the anomaly occurred, with sensors affected; probabilities of the defined fault causes.

(197) Particular embodiments are the following:

Embodiment 1

(198) A method for fault analysis in an industrial-method plant (101), for example a painting plant (102), wherein the method comprises the following: in particular automatic recognition of a fault situation in the industrial-method plant (101); storage of a fault situation data set for the respective recognised fault situation, in a fault database (136); automatic determination of a cause of the fault for the fault situation and/or automatic determination of process values that are relevant to the fault situation, on the basis of the fault data set of a respective recognised fault situation.

Embodiment 2

(199) A method according to embodiment 1, characterised in that, for the purpose of automatically determining the fault cause for the fault situation and/or automatically determining the process values relevant to the fault situation, one or more process values are automatically linked to the fault situation on the basis of one or more of the following link criteria: prior linking from a message system; an association of a process value with the same part of the industrial-method plant (101) as that in which the fault situation occurred; linking a process value to a historical fault situation on the basis of active selection by a user; an active selection of the process value by a user.

Embodiment 3

(200) A method according to embodiment 2, characterised in that, for the purpose of automatically determining the fault cause for the fault situation and/or automatically determining the process values relevant to the fault situation, automatic prioritisation of the process values linked to the fault situation is carried out automatically on the basis of one or more of the following prioritisation criteria: a process relevance of the process values; a position of a process value or of a sensor determining the process value within the industrial-method plant (101); an amount by which a process value deviates from a defined process window and/or a normal condition; a prioritisation of historical process values in historical fault situations; by adopting a prioritisation of the fault cause and/or the process values from a message system (138); a prioritisation by a user.

Embodiment 4

(201) A method according to one of embodiments 1 to 3, characterised in that, for the purpose of automatically determining the fault cause for the fault situation and/or automatically determining the process values relevant to the fault situation, further fault causes and/or process values are proposed, wherein the proposal is made automatically on the basis of one or more of the following proposal criteria: a process relevance of the process values; a position of a process value or of a sensor determining the process value within the industrial-method plant (101); an amount by which a process value deviates from a defined process window and/or a normal condition; a prioritisation of historical process values in historical fault situations; physical dependences of the process values.

Embodiment 5

(202) A method according to one of embodiments 1 to 4, characterised in that historical fault situations are determined from a fault database (136) using one or more of the following similarity criteria: a fault classification of the historical fault situation; a historical fault situation in the same or a comparable plant part; process values of the historical fault situation that are identical or similar to process values of the recognised fault situation.

Embodiment 6

(203) A method according to one of embodiments 1 to 5, characterised in that historical process values that are identical or similar to process values of the recognised fault situation are determined from a process database (134).

Embodiment 7

(204) A method according to embodiment 6, characterised in that the determined historical process values are characterised as belonging to a historical fault situation.

Embodiment 8

(205) A method according to one of embodiments 1 to 7, characterised in that, for a recognised fault situation, a fault situation data set is stored in a fault database (136).

Embodiment 9

(206) A method according to embodiment 8, characterised in that a respective fault identification data set comprises one or more of the following fault situation data: a fault classification of the fault situation; process values that are linked to the fault situation, based on a prior linking from a message system; information on a point in time at which a respective fault situation occurred; information on a duration for which a respective fault situation occurred; information on the location in which a respective fault situation occurred; alarms; status messages.

Embodiment 10

(207) A method according to embodiment 8 or 9, characterised in that the fault situation data set of a respective fault situation comprises fault identification data for unambiguous identification of the recognised fault situation.

Embodiment 11

(208) A method according to one of embodiments 8 to 10, characterised in that documentation data and fault elimination data are stored in the fault situation data set of a respective fault situation.

Embodiment 12

(209) A method according to one of embodiments 8 to 11, characterised in that process values are stored during operation of the industrial-method plant (101), synchronised with a recognised fault situation.

Embodiment 13

(210) A method according to one of embodiments 8 to 12, characterised in that process values are provided with a time stamp by means of which the process values are configured to be unambiguously associated with a point in time.

Embodiment 14

(211) A fault analysis system (144) for fault analysis in an industrial-method plant (101), for example a painting plant (102), wherein the system takes a form and is constructed for the purpose of carrying out the method for fault analysis in an industrial-method plant (101), for example a painting plant (102), according to one of embodiments 1 to 13.

Embodiment 15

(212) An industrial control system (100) that comprises a fault analysis system (144) according to embodiment 14.

Embodiment 16

(213) A method for predicting process deviations in an industrial-method plant (101), for example a painting plant (102), wherein the method comprises the following: automatic generation of a prediction model; prediction of process deviations during operation of the industrial-method plant (101), using the prediction model.

Embodiment 17

(214) A method according to embodiment 16, characterised in that the method for predicting process deviations is carried out in an industrial supply air plant (128), a pre-treatment station (112), a station for cathodic dip coating (114) and/or a drying station (116, 120, 124).

Embodiment 18

(215) A method according to embodiment 16 or 17, characterised in that process deviations of production-critical process values in the industrial-method plant (101) are predicted by means of the prediction model, on the basis of changing process values during operation of the industrial-method plant (101).

Embodiment 19

(216) A method according to one of embodiments 16 to 18, characterised in that, for the purpose of automatically generating the prediction model, process values and/or status variables are stored during operation of the industrial-method plant (101) for a predetermined period.

Embodiment 20

(217) A method according to embodiment 19, characterised in that the predetermined period for which process values and/or status variables are stored during operation of the industrial-method plant (101) is predetermined in dependence on one or more of the following criteria: the industrial-method plant (101) is in an operation-ready state, in particular for a production operation, for at least approximately 60%, preferably for at least approximately 80%, of the predetermined period; the industrial-method plant (101) is in a production-ready state for at least approximately 60%, preferably for at least approximately 80%, of the predetermined period; during the predetermined period, the industrial-method plant (101) is operated in particular using all possible operating strategies; a predetermined number of process deviations and/or disruptions in the predetermined period.

Embodiment 21

(218) A method according to embodiment 19 or 20, characterised in that, for the purpose of generating the prediction model, a machine learning method is carried out, wherein the process values and/or status variables that are stored for the predetermined period are used for generating the prediction model.

Embodiment 22

(219) A method according to embodiment 21, characterised in that the machine learning method is carried out on the basis of features that are extracted from the process values and/or status variables stored for the predetermined period.

Embodiment 23

(220) A method according to embodiment 22, characterised in that one or more of the following is used for the purpose of extracting features: statistical key figures; coefficients from a principal component analysis; linear regression coefficients; dominant frequencies and/or amplitudes from the Fourier spectrum.

Embodiment 24

(221) A method according to one of embodiments 16 to 23, characterised in that a selected number of prediction data sets with process deviations (222) and a selected number of prediction data sets with no process deviations (220) are used for training the prediction model.

Embodiment 25

(222) A method according to embodiment 24, characterised in that selection of the number of prediction data sets with a process deviation is made on the basis of one or more of the following criteria: a minimum time interval between two prediction data sets with process deviations; an automatic selection on the basis of defined rules; a selection by a user.

Embodiment 26

(223) A method according to embodiment 24 or 25, characterised in that prediction data sets with process deviations are characterised as such if a process deviation occurs within a predetermined time interval.

Embodiment 27

(224) A method according to embodiment 26, characterised in that the process values and/or status variables that are stored for the predetermined period are grouped into prediction data sets by pre-processing.

Embodiment 28

(225) A method according to embodiment 27, characterised in that the pre-processing comprises the following: regularisation of the process values stored for the predetermined period; grouping the process values and/or status variables into prediction data sets by classifying the process values and/or status variables into time windows with a time offset.

Embodiment 29

(226) A prediction system (146) for predicting process deviations in an industrial-method plant, wherein the prediction system takes a form and is constructed for the purpose of carrying out the method for predicting process deviations in an industrial-method plant (101), for example a painting plant (102), according to one of embodiments 16 to 29.

Embodiment 30

(227) An industrial control system (100) that comprises a prediction system (146) according to embodiment 29.

Embodiment 31

(228) A method for anomaly and/or fault recognition in an industrial-method plant (101), for example a painting plant (102), wherein the method comprises the following: automatic generation of an anomaly and/or fault model (233) of the industrial-method plant (101) that comprises information on the occurrence probability of process values; automatic input of process values of the industrial-method plant (101) during operation thereof; automatic recognition of an anomaly and/or fault situation by determining an occurrence probability by means of the anomaly and/or fault model (233) on the basis of the process values of the industrial-method plant (101) that have been input and by checking the occurrence probability for a limit value.

Embodiment 32

(229) A method according to embodiment 31, characterised in that the anomaly and/or fault model (233) comprises structural data containing information on a process structure in the industrial-method plant (101), and/or in that the anomaly and/or fault model (233) comprises parameterisation data containing information on relationships between process values of the industrial-method plant (101).

Embodiment 33

(230) A method according to embodiment 31 or 32, characterised in that, for the purpose of generating the anomaly and/or fault model (233), one or more of the following steps is carried out: structure identification (246) for determining a process structure of the industrial-method plant (101); determination of causalities (254) in the determined process structure of the industrial-method plant (101); structure parameterisation (256) of the relationships in the determined process structure of the industrial-method plant (101).

Embodiment 34

(231) A method according to embodiment 33, characterised in that, in the context of structure identification (246) for determining a process structure of the industrial-method plant (101), a structure graph that in particular maps relationships in the industrial-method plant (101) is determined.

Embodiment 35

(232) A method according to embodiment 34, characterised in that determination of the structure graph is performed using one or more of the following: a machine learning method; expert knowledge (248); known circuit diagrams and/or procedure diagrams (250); designations in a numbering system of the industrial-method plant (101).

Embodiment 36

(233) A method according to embodiment 33 to 35, characterised in that the industrial-method plant (101) is activated by test signals for the purpose of structure identification, in particular for determining the structure graph.

Embodiment 37

(234) A method according to one of embodiments 33 to 36, characterised in that the determining of causalities (254) in the determined process structure of the industrial-method plant (101) is performed using one or more of the following: system input signals (240) and system output signals (242) that are generated on activation of the industrial-method plant (101) by test signals; expert knowledge (248); known circuit diagrams and/or procedure diagrams (252); designations in a numbering system of the industrial-method plant (101).

Embodiment 38

(235) A method according to one of embodiments 33 to 37, characterised in that, for the purpose of structure parameterisation (246) of the relationships in the determined process structure of the industrial-method plant (101), one or more of the following is used: methods for determining probability density functions, in particular Gaussian mixture models; known physical relationships between process values; physical characteristic diagrams of functional elements of the industrial-method plant (101), for example characteristic diagrams of valves (232).

Embodiment 39

(236) A method according to embodiment 38, characterised in that data from regular operation of the industrial-method plant (101) and/or data obtained by activation of the industrial-method plant (101) by test signals are used for the purpose of structure parameterisation (246) using methods for determining probability density functions, in particular using Gaussian mixture models.

Embodiment 40

(237) A method according to embodiment 39, characterised in that the data that are used for structure parameterisation (246) using methods for determining probability density functions, in particular using Gaussian mixture models, are pre-processed before the structure parameterisation (246).

Embodiment 41

(238) A method according to one of embodiments 31 to 40, characterised in that during generation of the anomaly and/or fault model (233) a limit value for the occurrence probability of a process value is established, wherein an anomaly is recognised if this falls below the limit value.

Embodiment 42

(239) A method according to one of embodiments 31 to 41, characterised in that a fault cause of a recognised anomaly and/or a recognised fault situation is identified by means of the method for anomaly and/or fault recognition.

Embodiment 43

(240) A method according to one of embodiments 31 to 42, characterised in that the industrial-method plant (101) comprises or is formed by one or more of the following treatment stations (104) of a painting plant: pre-treatment station (112); station for cathodic dip coating (114); drying stations (116, 120, 124); industrial supply air plant (128); painting robot.

Embodiment 44

(241) An anomaly and/or fault recognition system (148) for recognising an anomaly and/or fault, which takes a form and is constructed to carry out the method for anomaly and/or fault recognition in an industrial-method plant (101), for example a painting plant (102), according to one of embodiments 31 to 43.

Embodiment 45

(242) An industrial control system (100) that comprises an anomaly and/or fault recognition system (148) according to embodiment 44.

Embodiment 46

(243) An industrial control system that comprises a fault analysis system according to embodiment 14, a prediction system for predicting process deviations in an industrial-method plant according to embodiment 29 and/or an anomaly and/or fault recognition system according to embodiment 44.