Affiliation:
1. School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
2. Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
3. Beijing Institute of Aerospace Control Device, Beijing 100094, China
Abstract
Rotating machinery has a complicated structure and interaction of multiple components, which usually results in coupling faults with complex dynamic characteristics. Fault diagnosis methods based on vibration signals have been widely used, however, these methods are intricate when identifying coupling faults, especially in the situation where coupling faults share similar patterns. As a noncontact and nonintrusive temperature-measuring technique, methods by infrared images can recognize multiple faults through temperature variations; however, it is not effective if the faults are temperature-insensitive. In this paper, an improved machinery fault diagnosis technique based on information fusion of infrared images and vibration signals is studied, to have better utilization of multisource sensors and to solve the problems when one single type of data is separately used. Firstly, data enhancement for infrared images and data visualization for vibration are performed on the dataset by using the principle of graphics and Short-Term Fourier Transform, which increases the diversity of the dataset and enhances the generalization ability of the model. Then, a multichannel convolution neural network-based method is constructed to achieve data-level information fusion and improve the fault diagnosis accuracy. The effectiveness of the presented method is validated by the experimental studies on a rotor test stand, the results illustrate that the coupling faults can be effectively identified by the information fusion method, and the fault diagnosis accuracy is higher in comparison with the method by a signal from single-source sensors.
Funder
General Project of Scientific Research Program of Beijing Education Commission
Subject
Mechanical Engineering,Mechanics of Materials,Geotechnical Engineering and Engineering Geology,Condensed Matter Physics,Civil and Structural Engineering
Cited by
12 articles.
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