Machine learning method for leakage detection in a pneumatic system

Abstract

Continuous condition monitoring of a pneumatic system, and in particular for early fault detection, is provided. The condition monitoring unit is formed with an interface to a memory in which a trained normal condition model is stored as a one-class model, which has been trained in a training phase with normal condition data and represents a normal condition of the pneumatic system. Furthermore, the condition monitoring unit comprises a data interface for continuously acquiring sensor data of the pneumatic system by means of a set of sensors, an extractor for extracting features from the acquired sensor data, a differentiator for determining deviations of the extracted features from learned features of the normal state model by means of a distance metric, a scoring unit for calculating an anomaly score from the determined deviations, and an output unit for outputting the calculated anomaly score.

Claims

1. A method for continuous condition monitoring of a pneumatic system, comprising the following method steps performed in an inference phase: providing a trained normal state model as a one-class model that has been trained in a training phase with normal state data representing a normal state of the pneumatic system; continuously acquiring sensor data from the pneumatic system using a set of sensors; extracting features from the acquired sensor data; determining deviations of extracted features from learned features of the normal state model using a distance metric; calculating an anomaly score from the determined deviations; and outputting the calculated anomaly score.

2. The method of claim 1, wherein the normal state model is a statistical model and/or machine learning model.

3. The method according to claim 1, in which the calculated and output anomaly score is used for anomaly detection, comprising at least leakage detection, and/or for runtime monitoring of the pneumatic system and wherein the normal state data comprise pressure signals and/or flow signals and/or microphone/body sound signals and/or valve switching times and/or signals from limit switches and/or continuous position signals and/or further valve-related time signals and/or other analog/digital measurement signals.

4. The method according to claim 1, performed directly in a fieldbus node and/or an edge device.

5. The method according to claim 1, wherein the anomaly score is forwarded to selected other network participants via a TCP/IP-based network protocol, in via one of an MQTT protocol or an OPC UA protocol.

6. The method according to claim 1, in which a productivity score is determined, when process cycles are automatically detected in order to evaluate how a cycle duration develops over a longer time horizon.

7. The method according to claim 1, wherein a representation or modeling of the normal state is performed via a bounding-box method or by means of a k-means method or via another suitable one-class-learning method.

8. The method according to claim 1, in which a normalization function, comprising a sigmoid function, is applied to the determined deviations and/or wherein an inflection point and/or a slope of the sigmoid function can be parameterized and/or wherein the sigmoid function is linearly rescaled in the training phase so that a graphical representation of the anomaly score is continuous.

9. The method according to claim 1, in which the method is controlled via meta-parameters, wherein the meta-parameters comprise a parameterization of the model, comprising at least a determination of the number of k-means centers and/or a number of bounding boxes and/or a calculation rule for the boundaries of the bounding boxes and/or a weighting of extracted features and/or further parameters for feature extraction.

10. The method according to claim 1, in which the normal state data in the training phase and productive data, comprising at least sensor data, in the inference phase are preprocessed using the same preprocessing methods.

11. The method according to claim 10, wherein the preprocessing methods comprise an execution of a pattern recognition algorithm on the sensor data and on the normal state data, in order to detect in the sensor data recurring patterns representing process cycles and wherein the detected process cycles are used as parameterization of a time window and/or wherein a result of the pattern recognition algorithm is used to calculate time windows in which the feature extraction is executed.

12. The method of claim 10, wherein one of the preprocessing methods comprises at least a pattern recognition algorithm, and wherein the pattern recognition algorithm comprises auto-correlation.

13. The method according to claim 1, comprising a dimensionality reduction method, and wherein the dimensionality reduction method is applied to the raw data and/or to the extracted features, in a data preprocessing step.

14. The method of claim 1, wherein the calculated anomaly score is subjected to a low-pass filter, the low-pass filter being parameterizable.

15. The method according to claim 1, in which sensitivity parameters are detected on an input field of a user interface, the sensitivity parameters characterizing under which conditions comprising at least how quickly differences between the extracted features and the learned features are processed as deviations.

16. The method according to claim 1, wherein the extracted features comprise statistical characteristics and comprise mean values, minima, maxima, differences, quantiles, quartiles, skewness and/or kurtosis of the sensor data and/or their derivatives, characteristics of the frequency analysis or other selected characteristics over time.

17. The method of claim 1, wherein the method, after acquiring the sensor data, executes a preprocessing algorithm on the acquired sensor data that transforms the data into a different format and/or filters out outlier data.

18. A condition monitoring unit for continuous condition monitoring of a pneumatic system, for early fault detection, the condition monitoring unit being designed to carry out a method of claim 1, having: an interface to a memory in which a trained normal state model is stored as a one-class model that has been trained in a training phase with normal state data and represents a normal state of the pneumatic system; a data interface for continuously acquiring sensor data of the pneumatic system by means of a set of sensors; an extractor for extracting features from the acquired sensor data; a differentiator for determining deviations of the extracted features from learned features of the normal state model using a distance metric; a scoring unit for calculating an anomaly score from the determined deviations; and an output unit for outputting the calculated anomaly score.

19. A computer program comprising instructions which, when the computer program is executed by a computer, cause the computer program to execute the method according to claim 1.

Description

BRIEF

[0089] FIG. 1 shows a schematic representation of part of a pneumatic system, in particular a valve terminal with a large number of actuators;

[0090] FIG. 2 is an example of a flow chart for a continuous condition monitoring process;

[0091] FIG. 3 shows an example of a schematic representation of a signal flow diagram of an exemplary pneumatic system with continuous condition monitoring;

[0092] FIG. 4a is a schematic example of a distance determination using the bounding box method;

[0093] FIG. 4b is a schematic example of a distance determination using the k-means method;

[0094] FIG. 5 is a schematic representation of a normalization function according to the present invention.

DETAILED DESCRIPTION OF THE FIGS

[0095] In the following, the invention is described in more detail by means of embodiment examples in connection with the figures.

[0096] The scope of protection of the present invention is given by the claims and is not limited by the features explained in the description or shown in the figures.

[0097] The present invention relates to a method and a device for monitoring the condition of pneumatic systems, in particular for detecting anomalies such as leaks.

[0098] FIG. 1 shows an overview illustration of a pneumatic system 100 with a condition monitoring unit 114. The pneumatic system 100 includes a valve island 102. It is conceivable that a pneumatic system 100 may include other components or multiple valve islands 102. Other components of the pneumatic system 100 may include a controller 104, a terminal 106, and a communication interface 108.

[0099] The valve terminal 102 comprises valves v1, v2, v3. In addition, a plurality of actuators a1, . . . , a6 are located on the valve terminal. The actuators a1, . . . , a6 are connected to the valves v1, . . . , v3 and are controlled by them. For example, the actuator a1 can be a tensioner (clamper). The valve v1 connected to it can cause the tensioner (actuator a1) to open and close. Further, a digital input/output hub 112 is located on the valve terminal 102 and is connected to the actuator a1 via the signal line s1. A signal line s2 connects the valve v1 to the stroke 112. Corresponding signal lines lead from the actuators a2, . . . , a6 and the valves v2, . . . v3 to the stroke for digital inputs and outputs 112. For the sake of clarity, a more detailed illustration is omitted here. It should be noted that the signal lines s1, s2 are shown here as wires. However, these can also be replaced in each case by wireless communication interfaces.

[0100] The stroke (hub) 112 of the valve terminal 102 is further connected to a fieldbus node 110. The fieldbus node 110 represents the data center of the valve island 102 and includes the condition monitoring unit 114. The condition monitoring unit 114 (hereinafter also referred to as “monitoring unit 114” for short) may be made available in a persistent memory 116 (e.g., flash memory) of the fieldbus node 110. The condition monitoring unit 114 includes, for example, models and their parameters, training and inference algorithms, training data, state data, meta-parameters, and configuration parameters (not shown). In addition, the fieldbus node has non-persistent memory (e.g., RAM). Here, for example, historical state data and the associated anomaly scores can be stored.

[0101] The monitoring unit 114 receives sensor data via the stroke 112 in the training and operating state of the pneumatic system 100 (i.e., during inference). Exemplary sensor m1, e.g., a limit switch, on the actuator a1 and a sensor for detecting a timestamp m2 on the valve v1 are shown. The sensor m1 measures, for example, the time at which the actuator a1 has reached a predetermined position. The sensor m2 measures, for example, the time at which the valve v1 was opened or closed. From this sensor data, the monitoring unit calculates an anomaly score using the one-class model described above. This can be communicated to other components and/or valve terminals of the pneumatic system via the communication interfaces 108 and/or displayed on the terminal 106.

[0102] The communication interfaces 108 may be, for example, communication interfaces of a distributed system, such as OPC Unified Architecture (OPC UA). This interface may be used to communicate with other fieldbus nodes and/or an IT data pool. In addition or alternatively, the communication interface can be designed as a machine-to-machine communication interface and serve, for example, to transmit messages via a Message Queuing Telemetry Transport (MQTT) protocol. This is shown in FIG. 1 with the reference sign MQTT Broker.

[0103] The fieldbus node 110 is further connected to a controller, such as a PLC 104, and a terminal 106. The terminal 106 may include a user interface for input by an operator. In addition or alternatively, the terminal may serve to display 106 the anomaly score provided by the monitoring unit 114.

[0104] In a preferred embodiment of the invention, the condition monitoring unit 114 comprises three interfaces, a first interface to the sensors m, a second interface to a memory 116 in which the trained one-class model is stored, and a third interface, which may be a human-machine interface 320 or a terminal 106 and is for inputting and outputting data. In a simple embodiment, the condition monitoring unit 114 may include the extractor 304, the differentiator 310, and the scoring unit 318. Of course, the memory 116 may also be formed as an internal memory so that the trained one-class model may be stored internally and locally in the fieldbus node 110.

[0105] FIG. 2 is an example of a flowchart for a continuous condition monitoring method 200 with steps 202-212 performed in an inference phase. The method 200 may run in the monitoring unit 114 of the pneumatic system 100, or the monitoring unit 114 may initiate the corresponding steps.

[0106] In a first step 202, a trained normal state model is provided. The normal state model was trained as a one-class model and with state data of the normal state of the pneumatic system 100. In the normal state, the pneumatic system 100 runs without errors. The normal state data represents this case. If the actuator a1 is a tensioner, the normal state can be used to specify exactly how long it takes to complete a production cycle.

[0107] In a step 204, sensor data of the pneumatic system 100 is continuously acquired by means of a set of sensors. The set of sensors includes at least the sensors m1 and m2 described above. In addition, several of these or other types of sensors (for example, flow sensors, pressure sensors, microphones, structure-borne sound pickups) may collect sensor data.

[0108] Furthermore, in step 206, features are extracted from the continuously acquired sensor data. By extracting, physically interpretable quantities, the features, are derived from the pure measured data points. For example, measured time stamps are assigned the characteristic of a duration associated with a particular process.

[0109] This is followed by step 208, which determines the deviations of the extracted features from learned features of the normal state by means of a distance metric. The distance metric can be, for example, a Euclidean norm, a sum norm, or a maximum norm.

[0110] From these determined deviations, an anomaly score is calculated in step 210. This calculation is described in more detail in connection with FIG. 3.

[0111] The anomaly score is output in step 212. The output may be, for example, via the terminal 106 of the pneumatic system. The anomaly score may also be communicated exclusively or additionally to other participants via the communication interface 108. In addition or alternatively, the anomaly score can be communicated, for example, as a control variable to the control 104. This can adjust its manipulated variable if required. Furthermore, the anomaly score and the associated state data can be stored in the non-volatile memory 116 of the fieldbus node 110.

[0112] FIG. 3 shows an example of a schematic representation of a signal flow diagram 300 including associated signal processing components of an exemplary continuous condition monitoring pneumatic system. In particular, box 310 represents a differentiator directed to determine deviations on which the calculation of the anomaly score is based. The input 302 consists of the continuously recorded (acquired) sensor signals. These include, for example, valve switching times and/or signals from the limit switches of an actuator, for example a cylinder. From these sensor signals, as shown in box 304, features are extracted by means of an extractor 304, i.e. quantities are derived that provide information about the functioning of the pneumatic system. In the present case, this can include the actuator features “reaction time extension”, “travel time extension”, “reaction time retraction”, and “travel time retraction” and/or possible latencies of the actuator.

[0113] The extracted features can be normalized to simplify their representation in an n-dimensional space. This is particularly advantageous if the features derived from the sensor data contain different physical quantities and/or magnitudes (for example, pressure and time) that are to be further processed together.

[0114] In box 308, a distance metric may be determined as an optional step in a configuration phase, e.g., via data collection on a human-machine interface applied or to be applied by the differentiator 310.

[0115] In Box 310, which represents Differentiator 310, the deviation of the extracted features from the learned features is determined. The learned features refer to the features derived during training by normal state data. The deviation can be determined either by a bounding-box method 311 or by a k-means method 312. It is also conceivable that both methods could be used for more robust results given sufficient computational capacity.

[0116] In the bounding-box method 311 it is determined whether the extracted features lie within the space bounded by the bounding-box or whether a boundary value violation is to be assumed. In the latter case, the distance between the extracted features and the bounding box is determined, otherwise the distance is “zero” (see explanation for FIG. 4a). In the k-means method, the distance of the features to the nearest cluster center is determined (see explanations for FIG. 4b).

[0117] The determined distance is mapped to an arbitrary interval (for example, from “zero” to “one”) of the anomaly score by a normalization function 314. The result is smoothed by the low-pass filter 316 and the corresponding anomaly score is provided as output by a scoring unit 318. The anomaly score is finally output by an output unit 320.

[0118] FIG. 4a and FIG. 4b illustrate in an exemplary manner the determination of the distance of the extracted features from the normal state of the pneumatic system. FIG. 4a shows the determination of the deviation using the bounding-box method. The bounding-box (rectangle shown) represents the space to which the learned features of the normal state belong. If an extracted feature lies inside the bounding-box, then the distance is zero. If an extracted feature lies outside the bounding-box, its distance from the bounding box is determined. This is shown by the dashed line. Various distance metrics, weighted distance metrics or a combination of distance metrics can be used for this purpose. Distance metrics are for example the Euclidean norm, the maximum norm or the sum norm.

[0119] FIG. 4b shows the determination of the deviation using the k-means method. The “cluster centers” represent the centers of the k-means clusters that group the learned feature data of the normal state. The grouping of the learned feature data of the normal state is illustrated by the cluster outline in FIG. 4b. The distance of the extracted feature (“test data”) to the nearest center of the learned features is determined within the k-means method (dashed line).

[0120] Based on the distances determined by the bounding-box or k-means method, the one-class model determines an anomaly score that is output to the operator via the output unit 320.

[0121] For FIG. 4a and FIG. 4b, it should be noted that the two-dimensional representation chosen is for illustration purposes only and that the features may be higher-dimensional (n-dimensional) objects.

[0122] FIG. 5 is a schematic representation of a normalization function according to the present invention. The normalization function is a sigmoid function. In the present case, the sigmoid function was rescaled so that a distance of zero can be assigned an anomaly score of zero. The graphs C1-C3 show the influence of the parameterization of the sigmoid function on the anomaly score and how it represents the measured deviations (“distance” on the x-axis). The deviations are mapped to the anomaly score 0 . . . 1, which facilitates their interpretability and thus an appropriate reaction to possible anomalies.

[0123] In particular, the point of inflection and slope of the sigmoid function are parameterized in the present case. Starting from the sigmoid curve C1, increasing the inflection point means a shift along the positive direction of the x-axis. This makes the model less sensitive, because higher deviations or distances are now represented by a lower anomaly score.

[0124] Furthermore, an increase in the slope of the sigmoid curve C1, as exemplarily shown for curve C3, causes smaller differences in the deviations or distances to lead to larger differences in the anomaly score. Depending on the error tolerance of the pneumatic system, the normalization curve can be selected and parameterized.

[0125] Finally, it should be noted that the description of the invention and the embodiments are in principle not to be understood restrictively with respect to any particular physical realization of the invention. All features explained and shown in connection with individual embodiments of the invention may be provided in different combinations in the subject matter according to the invention in order to simultaneously realize their advantageous effects.

[0126] The scope of protection of the present invention is given by the claims and is not limited by the features explained in the description or shown in the figures.

[0127] It is particularly obvious to a person skilled in the art that the invention can be applied not only to the sensor data mentioned, but also to other metrologically recorded variables that at least partly influence an operating state of the pneumatic system. Furthermore, the components of the condition monitoring unit can be realized distributed on several physical products.