Explainable Failure Prediction for Multi-channel Sensor Data
Date:
Prediction of equipment failures is an important issue in various industries. Also, identifying the causes of the failures can be very helpful in determining how to deal with it. In this study, we propose an attention mechanism based neural network model that yields high performance to predict the failures with interpretability. The model provides attention distributions of sensor-level and segment-level denoting which sensor and segment contribute to the prediction. We evaluate the performance of the proposed method through the real multi-channel sensor data collected from the vehicle engine.