Industrial Control System and Method for Operating the Industrial Control System

Abstract

A method for operating an industrial control system that has an automation controller with a sequential program, an actuator which actuates a switching component of the power electronics, and an input module, wherein activation and deactivation operations of the switching component cause electromagnetic interference that corrupts a measured value recorded via the input module, where a temporal occurrence of the activation and deactivation operations and/or an operating state is predicted for the switching component, and where the prediction is used to perform a correction of the measured value at a prediction time instant or during a prediction time range with respect to the corruption caused by the electromagnetic interference.

Claims

1. A method for operating an industrial control system including an automation controller having a sequential program, an actuator configured to actuate a switching component of the power electronics, and an input module, activation and deactivation operations of the switching component causing electromagnetic interference which corrupts a measured value recorded via the input module, the method comprising: predicting at least one of (i) a temporal occurrence of the activation and deactivation operations and (ii) an operating state for the switching component; and performing a correction of the measured value based on the prediction at a prediction time instant or during a prediction time range with respect to the corruption caused by the electromagnetic interference.

2. The method as claimed in claim 1, wherein a temporal occurrence of program code instructions in the sequential program is utilized to predict at least one of (i) the activation and deactivation operations and (ii) the operating state.

3. The method as claimed in claim 2, wherein in addition a recording module is operated such that, during operation of the industrial control system, a learning process is implemented for a neural network in the recording module, a monitored learning being implemented here; wherein a predetermined output to be learned of the prediction time instants through a temporal occurrence of the program code instructions in the sequential program with actually occurring electromagnetic interference is monitored via sensor data of sensors which record electromagnetic interference on at least one of switching components and drives to be activated; and wherein from the sensor data the actual time instants of the interference are determined and made available to the neural network as additional input variables.

4. The method as claimed in claim 1, wherein the input module is operated with a digital filter which stabilizes the recorded measured value against interfering influences, the prediction being utilized here to parameterize the filter and the interfering influences being minimized with the parameterized filter.

5. The method as claimed in claim 2, wherein the input module is operated with a digital filter which stabilizes the recorded measured value against interfering influences, the prediction being utilized here to parameterize the filter and the interfering influences being minimized with the parameterized filter.

6. The method as claimed in claim 3, wherein the input module is operated with a digital filter which stabilizes the recorded measured value against interfering influences, the prediction being utilized here to parameterize the filter and the interfering influences being minimized with the parameterized filter.

7. An industrial control system comprising an automation controller having a sequential program; an actuator configured to actuate a switching component of the power electronics; an input module, activation and deactivation operations of the switching component causing electromagnetic interference which corrupts a measured value recorded via the input module; a predictor configured to predict a temporal occurrence of at least one of (i) the activation and deactivation operations and (ii) an operating state for the switching component; a corrector configured to correct the measured value at a prediction time instant or during a prediction time range with respect to the corruption by the electromagnetic interference.

8. The industrial control system as claimed in claim 7, wherein the predictor is further configured to evaluate a temporal occurrence of program code instructions in the sequential program to predict at least one of (i) the activation and deactivation operations and (ii) the operating state (BZ) of the switching component of the power electronics.

9. The industrial control system as claimed in claim 7, further comprising: a recording module including a neural network, the recording module being configured to perform a learning process for the neural network during operation of the industrial control system, the recording module here being further configured to implement a monitored learning, in which a predetermined output of the prediction time instants through the temporal occurrence of the program code instructions in the sequential program with the actually occurring electromagnetic interference to be learned is monitored via sensor data of sensors which are arranged on at least one of (i) the switching components and (ii) drives to be activated; wherein the recording module being further configured to determine the actual time instants of the interference from the sensor data and provide said determined the actual time instants to the neural network (NN) as additional input variables.

10. The industrial control system as claimed in claim 8, further comprising: a recording module including a neural network, the recording module being configured to perform a learning process for the neural network during operation of the industrial control system, the recording module here being further configured to implement a monitored learning, in which a predetermined output of the prediction time instants through the temporal occurrence of the program code instructions in the sequential program with the actually occurring electromagnetic interference to be learned is monitored via sensor data of sensors which are arranged on at least one of (i) the switching components and (ii) drives to be activated; wherein the recording module being further configured to determine the actual time instants of the interference from the sensor data and provide said determined the actual time instants to the neural network as additional input variables.

11. The industrial control system as claimed in claim 7, wherein the input module is provided with a digital filter which stabilizes the recorded measured value against interfering influences, the input module being configured to parameterize the filter via the prediction and to minimize the interfering influences with the parameterized filter.

12. The industrial control system as claimed in claim 8, wherein the input module is provided with a digital filter which stabilizes the recorded measured value against interfering influences, the input module being configured to parameterize the filter via the prediction and to minimize the interfering influences with the parameterized filter.

13. The industrial control system as claimed in claim 9, wherein the input module is provided with a digital filter which stabilizes the recorded measured value against interfering influences, the input module being configured to parameterize the filter via the prediction and to minimize the interfering influences with the parameterized filter.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings show an exemplary embodiment of the invention, in which:

[0025] FIG. 1 shows a general view of an industrial control system in accordance with the invention;

[0026] FIG. 2 shows a temporal diagram of activation operations and deactivation operations with a prediction time instant in accordance with the invention;

[0027] FIG. 3 shows a graphical plot of a measured value characteristic of a measured value with a compensation in accordance with the invention; and

[0028] FIG. 4 is a flowchart of the method in accordance with the invention.

DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

[0029] FIG. 1 shows an industrial control system 1 comprising an automation controller CPU with a sequential program OB1. The sequential program OB1 contains program code instructions AWL; one of the program code instructions AWL will, at a specific time instant, ultimately activate the switching component SR via an actuator 2 for actuating power electronics. Alternatively, a program code instruction AWL will actuate a motor directly via an output module 3. Accordingly, the automation controller CPU is configured with the actuator 2 for actuating the switching component SR of the power electronics and a measured value MW can be recorded via an input module EA. Activation and deactivation operations EV, AV (see FIG. 2) of the switching component SR give rise to (cause) electromagnetic interference EMI, which is recorded via the input module EA and can corrupt the measured value.

[0030] The industrial control system 1 therefore comprises a predictor 10, which is configured to predict a temporal occurrence of the activation and deactivation operations EV, AV and/or a time duration of an operating state BZ for the switching component SR. A corrector KM is also available, which is configured to correct the measured value MW at a prediction time instant VZ or during a prediction time range VZB with respect to the corruption by the electromagnetic interference EMI. If the switching component SR, for example, an inverter, is activated, then this inverter in turn activates a first motor M1. A first sensor 51 and a second sensor S2 is arranged on the first motor M1. Both the actuation of the switching component SR or of the inverter and also in turn the actuation of the first motor M1 give rise to (cause) electromagnetic interference EMI. The automation controller CPU is connected communicatively via a bus 9 to the switching component SR and the input module EA. In this special case, the input module EA is coupled to an interface module IM via a backplane bus to an additional output module 3. If the input module EA records a measured value MW and activates the switching component SR at a certain time instant and electromagnetic interference EMI is in turn transferred to the measured value MW, then a corrupted measured value MW is transferred over the bus 9 to the predictor 10. A corrector KM is embedded in the predictor 10 and corrects the measured value MW based on the now known prediction time instant VZ. The predictor 10 can send a corrected measured value MW′ to the automation controller CPU.

[0031] The predictor 10 is further configured to evaluate a temporal occurrence of the program code instructions AWL in the sequential program OB1 such that the activation and deactivation operations EV, AV and/or the operating state BZ of the switching component SR are included in the prediction.

[0032] Here, a recording module AI is used as an aid. The recording module AI has a neural network NN, which is configured to carry out a learning process for the neural network NN during operation of the industrial control system 1, the recording module AI here being further configured to implement a monitored learning, in which a predetermined output to be learned of the prediction time instants through the temporal occurrence of the program code instructions AWL in the sequential program OB1 with the actually occurring electromagnetic interference is monitored via the sensor data of the first sensor 51 and the second sensor S2, which are arranged in the vicinity of the switching component SR or directly on a first motor. The recording module AI is configured to determine the actual time instants of the interference from the sensor data SD and to make them available to the neural network NN as additional input variables.

[0033] The input module EA has a digital filter F, which is configured to stabilize the recorded measured value against interfering influences, the input module EA here being configured to parameterize the filter F accordingly via the prediction. The interfering influences can likewise be minimized with the parameterized filter F. A long filter time increases the stability of a measured value MW against interfering influences, for example. However, a higher filter time also results in a slower data rate of the measured values MW and more sluggish response times RZ.

[0034] An optimally set filter time of the filter F on the input module EA is thus advantageous.

[0035] FIG. 2 shows the temporal course of an activation operation EV and a deactivation operation AV. The activation operation EV starts at a prediction time instant VZ. The switching component SR is now in an operating state BZ for a prediction time range VZB. When the time of the prediction time range VZB ends, the deactivation operation AV coincides temporally with the end of the prediction time range VZB.

[0036] FIG. 3 shows a measured value characteristic MWK of the measured values MW. This starts for example with an activation operation EV; a motor M1 is started and electromagnetic interference EMI occurs. The prediction time instant VZ is now known. As a result, it is possible to add a compensation KOMP temporally to the measured value MW or the measured value MWK around the occurrence of the electromagnetic interference EMI, and thus obtain a corrected measured value MW or a corrected measured value characteristic MWK.

[0037] FIG. 4 is a flowchart of the method for operating an industrial control system 1 including an automation controller CPU having a sequential program 0131, an actuator 2 configured to actuate a switching component SR of the power electronics, and an input module EA, where activation and deactivation operations EV, AV of the switching component SR causes electromagnetic interference EMI that corrupts the measured value MW recorded via the input module EA.

[0038] The method comprises predicting a temporal occurrence of the activation and deactivation operations EV, AV and/or an operating state BZ for the switching component SR, as indicated in step 410.

[0039] Next, a correction of the measured value MW is performed based on the prediction at a prediction time instant or during a prediction time range VZB with respect to the corruption caused by the electromagnetic interference EMI as indicated in step 420.

[0040] 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.