Abstract
Errors and failures occur inevitably in modern Cyber-Physical Systems (CPS) due to their structural variability and internal heterogeneity. This can cause economic losses or even hazardous accidents. Currently, deep learning-based anomaly detection methods, e.g., Transformer or LSTM-based detectors, have shown tremendous results in terms of anomaly detection and prevention. However, focusing solely on improving detection performance without classification and interpretation of the detected anomalies is not enough for many industrial scenarios. Instead of only reporting an anomaly, the detection results should be understandable and transparent for the users. The interpretability can provide some guidance and help to identify suitable countermeasures for different types of anomalies.
In this paper, we introduce a Multivariate Long Short Term Memory Fully Convolutional Network (MLSTM-FCN) for anomaly classification based on multivariate time-series data generated from industrial robotic manipulators. Specifically, we investigate several scenarios: no collision, collision with another manipulator, and manually injected sensor faults. We collect time-series data from the simulations of robotics arms using CoppeliaSim software. We feed these data into the MLSTM-FCN model and train it to be a multivariate time-series classifier. The paper presents the simulative case study results that show that the MLSTM-FCN model can efficiently classify different types of anomalies.
