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Using Expert Knowledge for Masking Irrelevant Data Streams in Siamese Networks for the Detection and Prediction of Faults

Ongoing initiatives such as Made in China 2025 or Industry 4.0 are transforming manufacturing environments into complex cyber-physical production systems (CPPS) consisting of multiple interacting subsystems with a huge number of sensors and actuators. To monitor such an environment, it is necessary to centrally process the high-dimensional time series generated during the operation of the whole CPPS. Characteristically for this scenario is that a particular data stream is usually only causally related to a very small number of other data streams, and only a relatively small subset of the total data streams is relevant for the detection of a particular failure mode. For this purpose, we propose a siamese neural network that employs 2D convolutions for extracting temporal features data stream-wise, followed by graph convolutions for extracting spatial features. Especially, we focus on the integration of expert knowledge for masking irrelevant data streams. We evaluate our approach against state-of-the-art similarity measures for time series such as dynamic time warping, NeuralWarp, and a learned similarity metric based on ROCKET representations. Our approach delivers at least equivalent, if not better, results when compared to the best approach that does not integrate expert knowledge while requiring only 1/20 of learnable parameters, which indicates its practicality for integrating expert knowledge.