Name
Title | ||
|---|---|---|
Real-Time Fault Diagnosis Using Deep Fusion Of Features Extracted By Parallel Long Short-Term Memory With Peephole And Convolutional Neural Network |
Abstract | ||
|---|---|---|
Analysis of one-dimensional vibration signals is the most common method used for safety analysis and health monitoring of rotary machines. How to effectively extract features involved in one-dimensional sequence data is crucial for the accuracy of real-time fault diagnosis. This article aims to develop more effective means of extracting useful features potentially involved in one-dimensional vibration signals. First, an improved parallel long short-term memory called parallel long short-term memory with peephole is designed by adding a peephole connection before each forget gate to prevent useless information transferring in the cell. It can not only 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 new training mechanism is designed to fuse features extracted from parallel long short-term memory with peephole and convolutional neural network, respectively. The fusion network can incorporate two-dimensional screenshot image into comprehensive feature extraction. It can provide more accurate fault diagnosis result since two-dimensional screenshot image is another form of expression for one-dimensional vibration sequence involving additional trend and locality information. Finally, real-time two-dimensional screenshot image is fed into 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1177/0959651820948291 | PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING |
Keywords | DocType | Volume |
Fault diagnosis, deep learning, deep feature fusion, parallel long short-term memory with peephole, convolutional neural network | Journal | 235 |
Issue | ISSN | Citations |
10 | 0959-6518 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Funa Zhou | 1 | 0 | 0.68 |
| Zhiqiang Zhang | 2 | 0 | 0.34 |
| Danmin Chen | 3 | 7 | 2.16 |