Multiblock Adaptive Convolution Kernel Neural Network for Fault Diagnosis in a Large-Scale Industrial Process

Abstract

The feature extraction method plays a vital role in the fault diagnosis of large-scale processes. In this study, a novel feature extraction method named multiblock adaptive convolution kernel neural network (MBCKN) for fault diagnosis in large-scale processes is proposed. First, the large-scale process is decomposed into several key blocks. Then, several different convolutional kernels are designed in the same layer, and the attention mechanism is used to adaptively select the appropriate convolutional kernel for feature extraction based on the input in each block. Finally, the adaptive fusion method is designed to fuse the features of these blocks for fault diagnosis of the whole process. The proposed method can adaptively select suitable convolutional kernels according to different fault types, and the fusion method considers the interblock relationship under different faults, which brings improvement in diagnostic accuracy for faults with relatively similar features. Through the simulation experiment of the Tennessee Eastman (TE) chemical process, the results show that the proposed method has better performance than the Long Short-Term Memory Neural Network, the single-layer Convolution Neural Network (CNN), and the multi-layer CNN method.

Double Branch Rebalanced Network for Class Imbalance Learning in Process Fault Diagnosis

“…Among them, the methods based on machine learning and deep learning have been extensively studied. Especially, deep learning based methods, in which a convolutional neural network (CNN) is widely used due to its powerful feature extraction ability, have attracted attention. , Xu et al proposed a bearing fault diagnosis method based on a deep convolutional neural network (DCNN) and random forest (RF). They first convert time domain vibration signals into image, then use DCNN to extract deep features from the image, and finally use RF to realize bearing fault diagnosis.…”