MTAP-DK: Multivariate Time-Series Anomaly Prediction with Domain Knowledge

Predicting anomalies of mobile equipment plays an important role in performing preventive maintenance, alleviating major economic losses and personal safety issues. Previous studies basically adopted data-driven models for anomaly prediction or detection of industrial equipment, ignoring the importance of domain knowledge. The domain knowledge can more accurately and theoretically capture the complex relationship among features. However, building the deep learning models incorporating domain knowledge is very difficult due to the following challenges. First, the domain knowledge is often different from the actual state of the equipment, so it is difficult to obtain knowledge information that conforms to the real situation. Second, domain knowledge is difficult to directly and effectively be applied to deep learning models due to its diverse representations. In this paper, we propose a Multivariate Time-Series Anomaly Prediction with Domain Knowledge (MTAP-DK) to address these issues. Specifically, we firstly propose a knowledge extraction module, which can extract the domain equations that conform to the actual situation with the domain knowledge and historical data. Secondly, we design a domain guidance module to guide and constrain the graph neural network from the knowledge level, to improve its capabilities to express the relationship among features. Thirdly, we predict future data based on the graph incorporating knowledge information. Finally, the prediction is reconstructed by the multi-scale convolution reconstruction method, and the abnormal information is inferred according to the reconstruction error.