Industrial data classification using stochastic configuration networks with self-attention learning features

Industrial data contain a lot of noisy information, which cannot be well suppressed in deep learning models. The current industrial data classification models are problematic in terms of feature incompleteness and inadequate self-adaptability, insufficient capacity for approximation of classifier and weak robustness. To this end, this paper proposes an intelligent classification method based on self-attention learning features and stochastic configuration networks (SCNs). This method imitates human cognitive mode to regulate feedback so as to achieve ensemble learning. In particular, firstly, at the feature extraction stage, a fused deep neural network model based on self-attention is constructed. It adopts a self-attention long short-term memory (LSTM) network and self-attention residual network with adaptive hierarchies and extracts the fault global temporal features and local spatial features of the industrial time-series dataset after noise suppression, respectively. Secondly, at the classifier design stage, the fused complete feature vectors are sent to SCNs with universal approximation capability to establish general classification criteria. Then, based on generalized error and entropy theory, the performance indexes for real-time evaluation of credibility of uncertainty classified results are established, and the adaptive adjustment mechanism of self-attention fusion networks for the network hierarchy is built to realize the self-optimization of multi-hierarchy complete features and their classification criteria. Finally, fuzzy integral is used to integrate the classified results of self-attention fusion network models with different hierarchies to improve the robustness of the classification model. Compared with other classification models, the proposed model performs better using rolling bearing fault dataset.