METHOD AND DEVICE FOR ANALYZING A SEQUENTIAL PROCESS

Abstract

A device and method for analyzing a sequential process, the sequential process including at least one repeating subprocess, and the method comprising the following steps: Recording process data of the sequential process over a reference time period; Automatically determining phase limits, based on the recorded process data; Identifying at least one repeating subprocess, the duration of which is limited in time by two adjacent phase limits; Determining at least one reference variable for each identified repeating subprocess from the process data recorded in the time period; Recording process data of the sequential process over a time period following the reference time period, and repeating steps b. and c. for the purpose of detecting the recurrence of an identified subprocess; Comparing the recorded process data of the detected subprocess with the at least one reference variable of the corresponding identified subprocess to establish deviations from a normal operation.

Claims

1. A method to analyze a sequential process, the sequential process comprising at least one repeating subprocess, the method comprising: recording process data of the sequential process over a reference time period; automatically determining phase limits based on the recorded process data; identifying at least one repeating subprocess, a duration of which is limited in time by two adjacent phase limits; determining at least one reference variable for each identified repeating subprocess from the process data recording in the time period; recording process data of the sequential process over a time period following the reference time period; repeating the steps of automatically determining and identifying to detect the recurrence of an identified subprocess; and comparing the recorded process data of the detected subprocess with the at least one reference variable of the corresponding identified subprocess to establish deviations from a normal operation.

2. The method according to claim 1, wherein the sequential process is a cyclical sequential process, and the reference time period comprises at least one, preferably at least two, periodic times of the cyclical sequential process, and wherein the method further comprises automatically determining the periodic time.

3. The method according to claim 1, wherein the method further comprises automatically determining the number of repeating subprocesses during a periodic time or an execution time of the sequential process.

4. The method according to claim 3, wherein the automated determination of the number of repeating subprocesses comprises at least the calculation of a difference between a reference distribution and a normalized gain value and/or the evaluation of at least one cost function.

5. The method according to claim 1, wherein a control program of the sequential process and/or exact process phases of the sequential process are unknown at the start of the analysis of the sequential process for a device, which is configured to analyze the sequential process.

6. The method according to claim 1, wherein the process data are sensor data or aggregate signals of sensor signals or exclusively total power consumption data of the sequential process and/or vibration data of an industrial plant.

7. The method according to claim 1, wherein different search methods and cost functions are used to automatically determine phase limits of a sequential process and to identify at least one repeating subprocess of the sequential process.

8. The method according to claim 1, wherein the step of automatically determining phase limits is carried out with the aid of change point detection methods.

9. The method according to claim 1, wherein the at least one reference variable of a subprocess includes: mean value, standard deviation, and/or variance.

10. The method according to claim 1, wherein the identification of at least one repeating subprocess comprises the identification of similar curve profiles of the process data, similar curve profiles preferably having a certain sequence of positive and/or negative increases within predetermined tolerance ranges.

11. The method according to claim 1, wherein the method further comprises determining at least one comparison variable for the detected subprocess, and the comparison comprising a comparison of the at least one comparison variable of the detected subprocess with the at least one reference variable of a corresponding subprocess, and the comparison variable of a subprocess being able to include at least: mean value, standard deviation, and/or variance.

12. The method according to claim 1, wherein the comparison involves a comparison of the value of the at least one comparison variable at the present point in time with a value of the corresponding reference variable at an earlier point in time, and/or a comparison of the value of the at least one comparison variable of the detected subprocess with the value of this comparison variable of a further corresponding subprocess during the same period of the sequential process.

13. The method according to claim 1, wherein the normal operation is determined by the reference variable and a predetermined tolerance range of the reference variable for each identified subprocess.

14. The method according to claim 1, further comprising: rating the process stability of the sequential process and/or at least one subprocess, based on an ascertainment of a deviation from normal operation.

15. The method according to claim 1, further comprising: displaying the results of the comparison on a user interface and/or forwarding these results to a further controller.

16. The method according to claim 1, further comprising: identifying the type of deviation from normal operation.

17. A device to analyze a sequential process, the device comprising: at least one sensor arrangement for recording process data of the sequential process, wherein the device is configured to carry out the method according to claim 1.

18. The device according to claim 17, wherein the sensor arrangement comprises a current sensor, a power consumption sensor and/or a vibration sensor.

19. A computer program, comprising program instructions, which are carried out by at least one processor and prompt the processor to control a device according to the method according to claim 1.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present invention will become more fully understood from the detailed description given hereinbelow and the accompanying drawings which are given by way of illustration only, and thus, are not limitive of the present invention, and wherein:

[0055] FIG. 1 shows a schematic representation of a device for analyzing a sequential process;

[0056] FIG. 2 shows a schematic sequence of a method for analyzing a sequential process;

[0057] FIG. 3 shows an exemplary representation of process data of a sequential process;

[0058] FIG. 4 shows an exemplary representation of process data of a further sequential process;

[0059] FIGS. 5a to 5C show an exemplary representation of process data of a further sequential process;

[0060] FIG. 6 shows an example of a normalized gain function; and

[0061] FIGS. 7A to 7D show an exemplary representation of deviations from the normal operation.

DETAILED DESCRIPTION

[0062] FIG. 1 shows a schematic representation of a device 50 for analyzing a cyclical or noncyclical sequential process Y. An example of a cyclical sequential process Y is a repeating task, which is carried out by a robot. Sequential process Y may comprise, for example, the following three subprocesses y.sub.t,k . . . t,k+1: grasp component y.sub.t,o . . . t,1, change position y.sub.t,1 . . . t,2, release component y.sub.t,2 . . . t,o+T. A further example of a sequential process Y is an injection molding process, including the following five subprocesses y.sub.t,k . . . t,k+1: close mold y.sub.t,o . . . t,1, inject y.sub.t,1 . . . t,2, hold pressure y.sub.t,2 . . . t,3, plasticize y.sub.t,3 . . . t,4, open mold y.sub.t,4 . . . t,o+T. Individual subprocesses y.sub.t,k . . . t,k+1 are separated from each other in each case by phase limits t.sub.0 . . . t.sub.k. An example of a noncyclical sequential process T comprises subprocesses y.sub.t,k . . . t,k+1 of machine on, machine off, standby. A further example of a noncyclical sequential process Y comprises subprocesses y.sub.t,k . . . t,k+1 of room occupied, room unoccupied, room occupied by multiple visitors.

[0063] Device 50 may record process data 20, 20′, 20″ for the purpose of analyzing sequential process Y. In particular, device 50 may comprise a sensor arrangement 52 for recording process data 20, 20′, 20″ of the sequential process. Process data 20, 20′, 20″ may be an overall input variable (aggregate signal), for example the total power consumption. Process data 20, 20′, 20″ may also be another aggregate signal, such as vibration data of an industrial plant, temperature data, noise emission data or the like. Correspondingly, sensor arrangement 52 may comprise at least one current sensor, a power consumption sensor, a vibration sensor, a temperature sensor, a noise emission sensor and/or other process data sensors.

[0064] Individual output variables 22, 24, 26, 28 of sequential process Y (e.g., component-specific power consumption, component-specific vibration data, component-specific temperature data, component-specific noise emission data, location data of individual components, or the like) may be inaccessible to the user of sequential process Y and/or to device 50 and thus not be available for analyzing sequential process Y. To nevertheless be able to analyze sequential process Y, process data 20, 20′, 20″ may be recorded and analyzed according to method 100 for analyzing a sequential process.

[0065] FIG. 2 shows a schematic representation of a method 100 for analyzing a sequential process; The method comprises the steps of (a.) recording 110 process data, optionally automatically determining 115 the periodic time of the sequential process; (b.) determining 120 phase limits, optionally automatically determining 125 the number of repeating subprocesses; (c.) identifying 130 a repeating subprocess; (d.) determining 140 a reference variable; (e.) recording 150 process data; and (f.) comparing 160 the recorded process data for the purpose of establishing deviations from a normal operation. Sequential process Y may thus be analyzed by observing process data 20, 20′, 20′—even without knowledge of output variables 22, 24, 26, 28.

[0066] FIG. 3 shows an exemplary representation of process data 20 of a sequential process Y, which were recorded during a time period T.sub.ref (reference time period) and/or a time period T.sub.mes (measurement time period). FIG. 3 also shows output variables 22 and 24, which represent, for example, the power consumption of individual components—such as individual actuators—of an industrial plant over the course of time. According to the method, process data 20 are recorded, which represent, for example, the time characteristic of the total power consumption of the industrial plant. Output variables 22 and 24, which each represent, for example, the time characteristic of a component-specific power consumption of a component of the industrial plant, are not recorded and are thus not available for the analysis of sequential process Y. Based on process data 20 recorded during reference time period T.sub.ref, phase limits are automatically determined and repeating subprocesses identified. At least one reference variable is furthermore determined for each identified repeating subprocess. Correspondingly, further process data may be recorded during a measurement time period T.sub.mes, which follows reference time period T.sub.ref. These process data as well would represent the time characteristic of the total power consumption of the industrial plant in the above example. The recurrence of an identified subprocess is detected according to the method. The process data recorded during measurement time period T.sub.mes may then be compared with a previously determined reference variable of the corresponding identified subprocess for the purpose of establishing deviations from a normal operation.

[0067] In particular, the recorded process data may be aggregate signals, for example total power consumption data of the sequential process. The use of aggregate signals makes it possible to analyze sequential processes without explicitly having access to output variables 22, 24, which represent, for example, the time characteristic of a component-specific power consumption of a component of the industrial plant.

[0068] FIG. 4 shows an exemplary representation of process data 20, 20′ of a further sequential process Y. This exemplary representation is intended to illustrate the automatic determination of the phase limits as an example, based on the change point detection method. For this purpose, recorded process data 20, 20′ (signal) are divided into phases (i.e., subprocesses y.sub.t.sub.k.sub.. . . t.sub.k+1). The phase limits are described by t.sub.0 . . . t.sub.3. To determine the phase limits, the sum of all costs c(y.sub.t.sub.0.sub.. . . t.sub.2) of the signal profile

[00010]$V\left(t;y\right)=\underset{k=0}{\overset{3}{.Math.}}\left(c\left(y_{t_k\mathrm{.Math.}{t}_{k+1}}\right)\right)$

must be minimized by varying the phase limits t.sub.k. The cost functions measure, for example, the deviation of the signal with respect to its mean value (in this case, y.sub.0, ref, y.sub.1,ref, y.sub.2, ref) between two adjacent phase limits. Cost functions for further features or the combination thereof may also be used. The phase limits are derived by minimizing the function V(t; y). A signal is shown in FIG. 4, which represents process data 20 which were recorded during a reference time period T.sub.ref. Likewise, the illustrated signal may represent process data 20′ which were recorded during a measurement time period T.sub.mes. Illustrated signal 20, 20′ assumes three values in the illustrated example in FIG. 4. If the cost functions measure the deviations with respect to the mean value between two adjacent phase limits, a minimum of the function V(t; y) is assumed if the limits t.sub.k are selected in such a way that they are situated exactly at the points in time, at which the signal changes its value. This is the case between t.sub.0 and t.sub.1 for y.sub.t,0 . . . t,1,ref, between t.sub.1 and t.sub.2 for y.sub.t,1 . . . t,2,ref, and between t.sub.2 and T (or. t.sub.3) for y.sub.t,2 . . . t,0+T,ref. The automatically determined phase limits are thus t.sub.0, t.sub.1, and t.sub.2.

[0069] FIGS. 5a through 5C each show an exemplary representation of process data 20 of a further sequential process Y. Process data 20 may represent, for example, the profile of the total power consumption of an industrial plant over time during sequential process Y. The raw signal of the recorded process data is shown on the left in each case. The process data after the analysis according to method steps (b.) and (c.) are shown on the right, i.e., after automatic determination 120 of phase limits t.sub.0, . . . , t.sub.k and identification 130 of at least one repeating subprocess y.sub.t,k . . . t,k+1. The particular (raw) signals are noisy signals, FIG. 5A showing a signal having abrupt changes of the mean value, FIG. 5B showing a jagged signal, and FIG. 5C showing a mixed signal. These signals are suitable as input data (process data) for analyzing the sequential process.

[0070] FIG. 6 shows an example of the calculation of a difference between a reference distribution and a normalized gain value gain.sub.K.sup.norm, as described above, this calculation underlying the automated determination of the number of repeating subprocesses.

[0071] FIGS. 7A through 7D each show an exemplary representation of a time characteristic of the process stability S for subprocesses y.sub.t,0 . . . t,1, y.sub.t,1 . . . t,2, y.sub.t,2 . . . t,0+T. The process stability may be rated, for example, on a scale of 0 to 1. Value 1 corresponds in this case to a setpoint process stability, which was initially determined, for example, during the recording of the process data within reference time period T.sub.ref. If a deviation from the normal operation is established, for example by comparing the comparison variable with the corresponding reference variable, the process stability may be rated with a value less than 1 for the corresponding subprocess to be rated.

[0072] The time characteristics of process stability S shown in FIGS. 7A through 7D for subprocesses y.sub.t,0 . . . t,1, y.sub.t,1 . . . t,2, y.sub.t,2 . . . t,0+T are recorded over a long time period t>>T. Each point of a time characteristic represents the process stability of a corresponding subprocess y.sub.t,0 . . . t,1, y.sub.t,1 . . . t,2, y.sub.t,2 . . . t,0+T, as rated after the execution (and detection) of the particular subprocess.

[0073] A lower threshold value S.sub.min of process stability S is also plotted in FIGS. 7A through 7D. If process stability S is rated as greater than S.sub.min after the execution (and detection) of the particular subprocess, no deviation or a tolerable deviation from the normal operation is present. If process stability S of the sequential process and/or the subprocess drops below predefined lower threshold value S.sub.min, for example the sequential process and/or the subprocess may be stopped, a warning issued and/or a warning interval adapted.

[0074] In FIG. 7A, process stability S is above lower threshold value S.sub.min for all subprocesses y.sub.t,0 . . . t,1, y.sub.t,1 . . . t,2, y.sub.t,2 . . . t,0+T. Consequently, none of subprocesses y.sub.t,0 . . . t,1, y.sub.t,1 . . . t,2, y.sub.t,2 . . . t,0+T deviates from the normal operation, and a tolerable deviation from the normal operation is present in each case. The (sub)process quality and (sub)process stability may be rated as good.

[0075] In FIG. 7B, process stability S deviates from the normal operation for subprocess y.sub.t,1 . . . t,2, i.e., process stability S is at least partially below threshold value S.sub.min. In particular, process stability S decreases for subprocess y.sub.t,1 . . . t,2 as observation time t progresses. The type of deviation (in this case: drift) may be classified and output to the user. The occurrence of a deviation of the “drift” type may, for example, point to the wear of a component, which is in operation during subprocess y.sub.t,1 . . . t,2.

[0076] Process stability S also deviates from the normal operation for subprocess y.sub.t,2 . . . t,0+T in FIG. 7C. In this case, a slow drifting of the process stability S does not occur (as in FIG. 7B), but instead a sudden change occurs. The type of deviation (in this case: shift) may be classified and output to the user. The occurrence of a deviation of the “shift” type may, for example, point to a sudden damage to a component, which is in operation during subprocess y.sub.t,2 . . . t,0+T.

[0077] An example of a fourth case is shown in FIG. 7D. In this case, an “anomaly” occurs in the rating of process stability S for subprocess y.sub.t,2 . . . t,0+T. The type of deviation (in this case: anomaly) may be classified and output to the user. The occurrence of a deviation of the “anomaly” type may, for example, point to an sequential process or subprocess which was not optimally set. For example, participating components collide or “get stuck”. Likewise, a deviation of the “anomaly” type may point to an imminent failure of a component.

[0078] A detected deviation and/or the type of detected deviation is/are typically output to the user of the sequential process. The latter may then interpret the process data, the comparison variable and/or the process stability, in particular the time characteristic of the process stability to draw conclusions as to the deviation from the normal operation, the type of deviation from the normal operation and/or the cause of the deviation from the normal operation for the entire sequential process and/or individual subprocesses.

[0079] The assessment of the (sub)process quality and stability may be simplified by the present invention. This may take place separately for each subprocess and/or for the entire sequential process. In particular, no raw sensor data need to be interpreted for assessing the (sub)process quality.

[0080] The invention being thus described, it will be obvious that the same may be varied in many ways. Such variations are not to be regarded as a departure from the spirit and scope of the invention, and all such modifications as would be obvious to one skilled in the art are to be included within the scope of the following claims.