EP-4742102-A1 - METHOD FOR DETECTING ANOMALIES IN A COMPLEX SYSTEM BY MEANS OF AN AUTOENCODER, COMPUTER PROGRAM PRODUCT AND DEVICE

EP4742102A1EP 4742102 A1EP4742102 A1EP 4742102A1EP-4742102-A1

Abstract

The invention relates to anomaly detection in a system, for example an industrial plant or an energy generation plant such as a wind turbine. Essentially, an autoencoder model is trained. Therefore, the user does not need expert knowledge to configure anomaly detection; they only need to provide a dataset containing sufficient information about the normal behavior of the machine being monitored. This data is then used to train the model, and future measurement data can be monitored automatically. In the 2D plane of PCA-reduced vectors, mass production repeatedly follows the same paths. These paths, and any detectable, steadily increasing deviation of the path from the pattern, which might be due to wear, for example, can be quickly and reliably identified. The crucial difference to previously known methods lies in the mechanism by which the autoencoder decides which time series sequences are anomalies. Here, the focus is not on the autoencoder's reconstruction, but rather on the hidden states, i.e., the vector generated by the encoder.

Inventors

Al Hage Ali, Ali
Amschler, Benjamin
HERBST, PETER
Thamm, Aleksandra
Thamm, Florian

Assignees

Siemens Aktiengesellschaft

Dates

Publication Date: 20260513
Application Date: 20241106

Claims (17)

Computer-implemented method for anomaly detection in a complex system (A1, A2, A3) with a controller (S), wherein the controller receives data (S1, S2, S3) from the system, processes data and sends data back to the system, using an autoencoder (12), wherein: - the autoencoder (12) consists of an encoder (121), a decoder (123) and a hidden layer (122), - the autoencoder (12) is trained on the data (101) to be monitored of the complex system and - state vectors (105) are generated from the data calculated by the encoder (121) and hidden in the hidden layer (122) of the autoencoder, characterized by the fact that - the generated vectors are projected into 2-dimensional space, and - a first path (13) is generated based on the vectors projected into the 2-dimensional space, and new additional data (111) calculated by the encoder (121) and hidden in the Hidden Layer (122) of the autoencoder are generated (15) and projected into further high-dimensional vectors, and the result is compared with the first path (110), and a detected deviation is calculated in an anomaly score, and depending on the anomaly score, the control system (S) of the complex system (A1, A2, A3) triggers an alarm.
Computer-implemented method for anomaly detection in a complex system (A1, A2, A3) according to claim 1, characterized in that the complex system (A1, A2, A3) essentially performs cyclically recurring movements.
Computer-implemented method for anomaly detection in a complex system (A1, A2, A3) according to claim 1 or 2, characterized in that the complex system (A1, A2, A3) is in particular an industrial system (A1) for the manufacture of goods or an energy generation system (A3) or an amusement ride (A2).
Computer-implemented method for anomaly detection in a complex system (A1, A2, A3) according to one of the preceding claims, characterized in that the training data used for training the autoencoder (12) are normalized before training.
Computer-implemented method for anomaly detection in a complex system (A1, A2, A3) according to one of the preceding claims, characterized in that the training data used for training the autoencoder (12) are previously divided into time series sequences (102) of fixed length and the new data are divided into time series sequences (112) of the same length for a time series sequence.
Computer-implemented method for anomaly detection in a complex system (A1, A2, A3) according to one of the preceding claims, characterized in that the projection of the vectors onto 2D is carried out using Principal Component Analysis PCT (105).
Computer-implemented method for anomaly detection in a complex system (A1, A2, A3) according to one of the preceding claims, characterized in that the data are measured data from the complex system (A1, A2, A3), in particular temperature, motor or generator current, speed, active power, frequency, torque, DC link voltage.
Computer-implemented method for anomaly detection in a complex system (A1, A2, A3) according to one of the preceding claims, characterized in that, depending on the anomaly score, the controller (S) triggers a suitable action in the complex system (A1, A2, A3) to prevent damage to the system.
Computer program product for anomaly detection in a complex system (A1, A2, A3) suitable for carrying out the features of the method according to one of claims 1 to 8.
Device (11) for anomaly detection in a complex system (A1, A2, A3) with a controller (S), wherein the controller receives data (S1, S2, S3) from the system, processes data and sends data back to the system, by means of an autoencoder (12), where: - the autoencoder (12) consists of an encoder (121), a decoder (123) and a hidden layer, - the autoencoder (12) is trained on the data (101) of the complex system to be monitored and is suitable and configured to generate state vectors (105) from the data calculated by the encoder (121) and hidden in the hidden layer (122) of the autoencoder, and to project the generated vectors into the 2-dimensional space project, and generate a first path (13) based on the vectors projected into the 2-dimensional space, and the autoencoder (12) is suitable and configured to calculate new additional data (111) through the encoder (121) and to generate (15) high-dimensional vectors from the hidden states in the hidden layer (122) of the autoencoder and to compare them with the first path (110), and a comparator (C) is suitable and configured to identify a detected deviation and calculate an anomaly score, and, depending on the anomaly score, instruct the control (S) of the complex system (A1, A2, A3) to trigger an alarm.
Device for anomaly detection in a complex system (A1, A2, A3) according to claim 10, characterized in that the complex system (A1, A2, A3) essentially performs cyclically recurring movements.
Device for anomaly detection in a complex system (A1, A2, A3) according to claim 10 or 11, characterized in that the complex system is in particular an industrial plant for the manufacture of goods or an energy generation plant or an amusement ride.
Device for anomaly detection in a complex system (A1, A2, A3) according to one of the preceding claims 10 to 12, characterized in that the training data used for training the autoencoder are normalized data before training.
Device for anomaly detection in a complex system (A1, A2, A3) according to one of the preceding claims 10 to 13, characterized in that the training data used for training the autoencoder are previously divided into time series sequences of fixed length and the new data are divided into sequences of the same length and for a time series sequence.
Device for anomaly detection in a complex system (A1, A2, A3) according to one of the preceding claims 10 to 14, characterized in that the projection of the vectors onto 2D is carried out using Principal Component Analysis PCT (105).
Device for anomaly detection in a complex system (A1, A2, A3) according to one of the preceding claims 10 to 15, characterized in that the data are measured data from the complex system, in particular temperature, motor or generator current, speed, active power, frequency, torque, DC link voltage.
Device for anomaly detection in a complex system (A1, A2, A3) according to one of the preceding claims 10 to 16, characterized in that, depending on the anomaly score, a suitable action is triggered in the complex system by the control system in order to prevent damage to the system.

Description

Industrial controllers (programmable logic controllers, PLCs) are used to monitor and control complex systems consisting of many interacting units. Such a system could be, for example, an industrial production plant for manufacturing goods or materials. However, other applications are also known; for instance, the complex system could be a power generation plant, specifically a wind turbine, or the motion control system for an amusement ride. Many amusement rides (carousels, swings, Ferris wheels, roller coasters, or drop towers) are controlled by a controller and also perform cyclical movements, in which a similar sequence of movements is repeated, with the end of one movement transitioning directly into the beginning of a new cycle. Monitoring the smooth operation of these rides is essential, as a malfunction could potentially endanger the lives of the passengers. The units and the plant's work process, and potentially the production output, are monitored by sensors, generating countless data points throughout operation. Furthermore, PLC programs are used to collect live sensor data for sample production, maximizing process performance. This provides the plant operator with deeper insights into the manufacturing process, leading to increased production efficiency and reduced downtime. The following assumes that the control system in the plant (among other things) controls a movement sequence which (ideally) is executed identically and repeatedly. During operation, changes occur over time, for example due to wear and tear or external influences, which may not initially be apparent to the plant operator. These changes can be caused by mechanical issues, but also by faulty electronic components or microelectronics. External influences, such as the environmental conditions to which the plant is exposed during operation, cannot be ruled out either. If changes in the system/machine are not detected early, this can lead to more extensive damage, resulting in increased downtime and additional, higher maintenance costs. From the operator's perspective, this must be avoided at all costs, as consequential damage necessitates unnecessarily large repairs, leads to prolonged operational downtime, and ultimately results in unnecessary financial losses. Therefore, it is in the operator's interest to identify and assess all changes as quickly as possible in order to derive appropriate measures. The cause of the anomaly must be identified, as well as the affected parts of the system and the resulting consequences. Repairs, maintenance, or replacement of affected parts must be planned: Is a critical part of the system affected, or can a response be scheduled for a later time, such as during a planned shutdown period? Therefore, anomaly detection methods are used that automatically evaluate a large number of different measurement data and thus recognize correlations and changes that are not visible even to experts. The automatic detection of anomalies based on time series data is a complex problem that is usually only solved for very specific applications. The goal of these detection methods is a high anomaly detection rate with a low false prediction rate. Previous state-of-the-art approaches present promising results, but their reliability remains questionable. The developed methods are often tested and evaluated with trivial, unrealistic datasets. R. Wu and E. J. Keogh. "Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress", 2020 Predicted anomalies of such models should therefore always be interpreted with caution. Furthermore, there are different types of anomalies (point, contextual, and collective anomalies) that require approaches of varying complexity. When detecting contextual anomalies, all data must always be considered within their context. In the case of time-series data, this context can extend over an arbitrarily long time period, thus making the application of neural networks difficult, as processing large time periods with the common recurrent neural networks (RNNs) requires a high computational effort. Today, the condition of plants and machinery is monitored through regular manual inspection and continuous maintenance of plant components. Changes or problems are usually only detected when machines and systems have failed. Furthermore, there are numerous algorithms in the field of artificial intelligence, most of which are developed for specific domains and therefore cannot simply be transferred to other applications. These include: statistical methods, machine learning, clustering, and deep learning (autoencoders, variational autoencoders, generative adversarial networks). An autoencoder is an artificial neural network used to learn efficient encodings. The goal of an autoencoder is to learn a compressed representation (encoding) for a set of data and thereby extract essential features. This allows it to be used for dimensionality reduction. The autoencoder