Method and Safety Oriented Control Device for Determining and/or Selecting a Safe Condition

Abstract

A method and safety-oriented control device for determining and/or selecting a safe condition using a safety-oriented control device configured for safety-oriented control of an apparatus or installation via execution of a safety-oriented control program which, when executed, results in a safe reaction being triggered in the safety-oriented controller, wherein the method is implemented such that an ML model is configured and formed as a result, which is stored in a memory device, of the application of a machine learning method, such that data relevant to the determination of a safe condition are stored in connection with the triggering of the safe reaction, and such that a first safe condition is determined via the data relevant to the determination of the safe condition being applied to the ML model.

Claims

1. A method for determining a safe condition by utilizing a safety-oriented control device which is configured for safety-oriented control of an apparatus or installation via execution of a safety-oriented control program which, when executed, results in a safe reaction being triggered in the safety-oriented controller, the method comprising: storing an ML model in a memory device of an application of a machine learning method, the ML model being configured as and forming a result; storing data relevant to the determining the safe condition in connection with the triggering of the safe reaction; and determining a first safe condition via the data relevant to the determining of the safe condition being applied to the ML model.

2. The method as claimed in claim 1, wherein a plurality of safe conditions are stored in reference to the safety-oriented control of the apparatus or installation; and wherein the first safe condition is selected from the plurality of safe conditions via the data relevant to the determining of the safe condition being applied to the ML model.

3. The method as claimed in claim 2, wherein a succession of safe conditions is selected from the plurality of safe conditions via the data relevant to the determining of the safe condition being applied to the ML model; wherein the succession of safe conditions comprises the first safe condition and at least one further safe condition.

4. The method as claimed in claim 1, wherein the first safe condition is stipulated by at least one apparatus and/or installation parameter, and said at least one apparatus and/or installation parameter comprises at least one parameter value range; and wherein the application of the data relevant to the determining of the safe condition to the ML model further results in determination of a parameter value or a succession of parameter values from the parameter value range.

5. The method as claimed in claim 1, wherein the ML model is formed and configured as a result, which is stored in the memory device, of the application of the machine learning method to ML training data.

6. A safety-oriented control device for safety-oriented control of an apparatus or installation via the execution of a safety-oriented control program, comprising: a processor; and wherein the processor is configured to: store an ML model in a memory device of an application of a machine learning method, the ML model being configured as and forming a result; store data relevant to the determining the safe condition in connection with the triggering of the safe reaction; and determine a first safe condition via the data relevant to the determining of the safe condition being applied to the ML model.

7. The safety-oriented control device as claimed in claim 6, wherein one of: (i) the safety-oriented control device further comprises the memory device having the ML model and (ii) the safety-oriented control device is communicatively coupled to the memory device having the ML model.

8. The safety-oriented control device as claimed in claim 6, wherein the safety-oriented control device is formed and configured as a modular safety-oriented control device having a safety-oriented central module; and wherein the safety-oriented central module comprises the memory device having the ML model.

9. The safety-oriented control device as claimed in claim 7, wherein the safety-oriented control device is formed and configured as a modular safety-oriented control device having a safety-oriented central module; and wherein the safety-oriented central module comprises the memory device having the ML model.

10. The safety-oriented control device as claimed in claim 6, wherein the safety-oriented control device is formed and configured as a modular safety-oriented control device having a safety-oriented central module and a KI module; wherein the safety-oriented central module and the KI module are communicatively coupled via a backplane bus of the safety-oriented control device; and wherein the KI module comprises the memory device having the ML model.

11. The safety-oriented control device as claimed in claim 7, wherein the safety-oriented control device is formed and configured as a modular safety-oriented control device having a safety-oriented central module and a KI module; wherein the safety-oriented central module and the KI module are communicatively coupled via a backplane bus of the safety-oriented control device; and wherein the KI module comprises the memory device having the ML model.

12. The safety-oriented control device as claimed in claim 10, wherein the KI module is formed and configured as a safety-oriented KI module.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0125] The present invention is explained in more detail by way of illustration with reference to the accompanying figures, in which:

[0126] FIG. 1 shows an example of a safety-oriented controller that controls an applicable installation;

[0127] FIG. 2 shows a schematic depiction of an illustrative sequence for the selection of a safe condition using an ML model;

[0128] FIG. 3 is a flow chart of the method in accordance with the invention

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

[0129] FIG. 1 shows a safety-oriented modular control device 100, also referred to within the present disclosure as modular PLC 100. The modular PLC 100 comprises a safety-oriented central processing unit 110 having a memory device 112. A process image 114 of the central processing unit 110 is stored inside the memory device 112.

[0130] The central processing unit 110 is configured to execute a safety-oriented control program and formed and configured as a safety-oriented central processing unit 110 according to the standard IEC 61508. A backplane bus 140 connects the central processing unit 110 to an input/output module 120, which is likewise formed and configured as a safety-oriented input/output module 120. The process image 114 stores input and output values of the safety-oriented control program.

[0131] Moreover, the backplane bus 140 connects a KI module 130 to the central processing unit 110 and to the input/output module 120. The KI module 130 is likewise formed and configured as a safety-oriented KI module 130. The KI module 130 comprises a memory device 132 having a trained neural network 134 and is an example of a KI module in accordance with the present invention. The neural network 134 is an example of an ML model in accordance the present invention. The neural network 134 has been trained, for example, using a method and data as were disclosed in accordance with the exemplary embodiments.

[0132] Moreover, FIG. 1 depicts an installation 200 that comprises a transport device 210 and a robot 220. The modular PLC 100 is configured for the safety-oriented control of this installation 200. To this end, for example, the input/output module 120 is connected to the transport module 210 of the installation 200 via a first data line 124, or first field bus line 124. Moreover, a second data line 122, or second field bus line 122, connects the input/output module 122 to the robot 220 of the installation 200. The field bus lines 122, 124 are used to transmit control signals from the modular PLC 100 to the components 210, 220 of the installation 200 and applicable sensor or device data from the installation 200 back to the modular PLC 100.

[0133] The safety-oriented control of the installation 200 by the modular PLC 100 involves a cyclic execution of the safety-oriented control program that executes in the central processing unit 110 of the modular PLC 100 resulting in data of the process image 114 being read in at the beginning of a program cycle. These data are processed during the execution of the program cycle, and the results determined in the process are then stored in the process image 114, again as current control data. These current control data are then transmitted to the installation 200 via the backplane bus 140 and the input/output module 120 and also the field bus lines 124, 122. Applicable sensor data or other data of the installation 200 are transmitted back to the modular PLC 100 and the process image 114 in the central processing unit 110, again on the same path.

[0134] FIG. 2 shows an illustrative schematic sequence for the case in which the safety-oriented control of the installation 200 results in a safe reaction being triggered.

[0135] In this regard, the memory device 112 of the central processing unit 110 of the modular PLC 100 stores respective parameters for the installation 200 in reference to four safe conditions 310, 320, 330, 340. The parameters of the respective safe condition 310, 320, 330, 340 are used to explicitly define the applicable safe condition 310, 320, 330, 340 of the installation 200. The control program of the modular PLC 100 is configured such that handover of the applicable parameters of one of the safe conditions 310, 320, 330, 340 is immediately followed by triggering of the adoption of the applicable safe condition 310, 320, 330, 340 by the installation 200.

[0136] FIG. 2 schematically shows the central processing unit 110 with the memory device 112 and the process image 114 in the block on the far left. The triggering of the safe reaction now results in predefined data from the process image, as data 116 relevant to the determining of a safe condition, being transmitted via the backplane bus 140 from the central processing unit 110 to the KI module 130 and handed over there as input data to the trained neural network 134 that is stored there.

[0137] The trained neural network 134 is configured such that it has four (or more) outputs, where each of the outputs is assigned one of the safe conditions 310, 320, 330, 340. After the relevant data 116 are input into the neural network 134, one of the safe conditions 310, 320, 330, 340 is then output by the neural network and the information about this determined safe condition 310, 320, 330, 340, which corresponds to a first safe condition in accordance with the present invention, is transmitted back to the central processing unit 110 again via the backplane bus 140.

[0138] The parameters assigned to this selected safe condition 310, 320, 330, 340 are now read from the memory device 112 in the central processing unit 110 and routed to the safety-oriented control program such that there is immediate triggering of the adoption of the selected safe condition 310, 320, 330, 340 by the installation 200. Applicable control signals are then transmitted to the transport device 210 and the robot 220 of the installation 200 via the field bus lines 124, 122.

[0139] The present invention describes a method for selecting a safe condition for the purposes of safety-oriented control of an apparatus or installation, the safe condition being selected by using an ML model. This allows suitable safe conditions (in particular safe conditions that entail as little financial loss as possible) to be adopted for each specific situation in a simplified manner even for more complex machines, apparatuses or installations.

[0140] It is of no importance to the fact that a safety-oriented controller is involved that the results of an ML model are possibly not immediately logically comprehensible to a user. The only relevance to the fact that control is safety-oriented is that the triggering of a safe reaction results in a safe condition being adopted in any event. This is also always the case for the presently disclosed embodiments of the method kin accordance with the invention.

[0141] FIG. 3 is a flowchart of the method for determining a safe condition by utilizing a safety-oriented control device 100 that is configured for safety-oriented control of an apparatus or installation 200 via execution of a safety-oriented control program which, when executed, results in a safe reaction being triggered in the safety-oriented controller 100.

[0142] The method comprises storing an ML model 134 in a memory device 112, 132 of an application of a machine learning method, the ML model 134 being configured as and forming a result, as indicated in step 310.

[0143] Next, data 116 relevant to the determining the safe condition in connection with the triggering of the safe reaction is stored, as indicated in step 320.

[0144] Next, a first safe condition 310, 320, 330, 340 is determined via the data 116 relevant to the determining of the safe condition being applied to the ML model 134, as indicated in step 330.

[0145] Thus, while there have been shown, described and pointed out fundamental novel features of the invention as applied to a preferred embodiment thereof, it will be understood that various omissions and substitutions and changes in the form and details of the methods described and the devices illustrated, and in their operation, may be made by those skilled in the art without departing from the spirit of the invention. For example, it is expressly intended that all combinations of those elements and/or method steps which perform substantially the same function in substantially the same way to achieve the same results are within the scope of the invention. Moreover, it should be recognized that structures and/or elements and/or method steps shown and/or described in connection with any disclosed form or embodiment of the invention may be incorporated in any other disclosed or described or suggested form or embodiment as a general matter of design choice. It is the intention, therefore, to be limited only as indicated by the scope of the claims appended hereto.