Information Fusion of Infrared Images and Vibration Signals for Coupling Fault Diagnosis of Rotating Machinery

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.

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

“…Wang_2021 [233] Rauber_2021 [219] Fan_2021 [234] Qian_2020 [235] Zhao_2020a [236] Chen_2020 [237] Li_2020 [238] Xin_2020 [239] Li_2019 [240] Hoang_2019 [241] Qian_2018 [242] Qian_2018 [242] Bai_2021b [243] Sharma_2021 * [25]…”

Appraising machine learning classifiers for discriminating rotor condition in 50W–12V operational wind turbine for maximizing wind energy production through feature extraction and selection process

“…The frequency of vibration had been received from the ADC. These vibration signals were used to derive functions by extracting features (Bai et al, 2021). The accelerometer is connected to one end of the cable and to the other end to the DAQ system analog input and output (AIO) terminal.…”

Research on mechanical fault diagnosis based on MADS evidence fusion theory

“…Multi-source sensor information fusion for mechanical fault diagnosis is still in its infancy due to its complexity and feature extraction and fusion issues [24,25]. Due to the rapid development of deep learning-related research results, designing a complete fault diagnosis system based on multi-source information fusion using deep learning to realize the algorithm structure, including data preprocessing, classifiers, and evidence fusion, has become important [26,27]. Ghosh et al [28] discovered an evidence theory and multiinformation fusion to effectively-identified composite faults and provided maintenance instructions for composite fault diagnosis by fussing several maximal statistical distances and identifying fault classes technique.…”