10 - Real-time fault diagnosis using deep fusion of features extracted by PeLSTM and CNN

Abstract

Analysis of 1-D vibration signals is the most common method used for safety analysis and health monitoring of rotary machines. How to effectively extract features involved in 1-D sequence data is crucial for the accuracy of real-time fault diagnosis. This chapter aims to develop a more effective means of extracting useful features potentially involved in 1-D vibration signals. First, an improved parallel long short-term memory called PeLSTM is designed by adding a peephole connection before each forget gate to prevent useless information transferring in the cell. It not only can solve the memory bottleneck problem of traditional long short-term memory for long sequence but also can make full use of all possible information helpful for feature extraction. Second, a fusion network with a new training mechanism is designed to fuse features extracted from PeLSTM and the convolutional neural network, respectively. The fusion network can incorporate a 2-D screenshot image into comprehensive feature extraction. It can provide a more accurate fault diagnosis result since the 2-D screenshot image is another form of expression for a 1-D vibration sequence involving additional trend and locality information. Finally, the real-time 2-D screenshot image is fed into the convolutional neural network to secure a real-time online diagnosis, which is the primary requirement of the engineers in health monitoring. Validity of the proposed method is verified by fault diagnosis for rolling bearing and gearbox.