Incipient fault detection enhancement based on spatial-temporal multi-mode siamese feature contrast learning for industrial dynamic process

Incipient faults are characterized by low-amplitude, unclear fault features, which are susceptible to unknown disturbances, leading to unsatisfactory detection performance. In this paper, an incipient fault detection enhancement method based on siamese spatial-temporal multi-mode feature contrast learning method is proposed. Firstly, we design a novel siamese spatial-temporal multi-mode convolutional neural network model consisting of two weight-shared spatial-temporal multi-mode convolutional neural networks and one feature discrimination measure operator, which are then used to extract the spatial-temporal multi-mode features of two datasets and to measure the distance between them. Then, an incipient fault feature discrimination intensification training strategy is developed to enhance the incipient fault detection performance. Specifically, this strategy intends to maximize the feature distance between the normal data and the incipient fault data, as well as that between different incipient faults, while minimizing the feature distance between the normal data and between the same incipient faults. Moreover, due to the long-term slow change characteristic of the incipient fault, the multi-head self-attention Long Short-Term Memory is presented as a dynamic feature learning model to further lopsidedly learn the incipient fault temporal long-term dependency according to attention weights utilizing the scaled dot-product multi-head self-attention mechanism. Finally, the performance of the proposed method is demonstrated on two industrial cases.