Research on Aeroengine Rub-Impact Fault Analysis Based on Wavelet Scalogram Statistical Feature

Wu, Yuhou; Zhang, Da Zhi; Li, Xin Liang; Xue, Jing

doi:10.4028/www.scientific.net/amm.48-49.942

Cited by 1 publication

(1 citation statement)

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“…However, it is difficult to explicitly build a relationship between the CWTS and fault conditions. Although statistical feature [ 25 ] and one-dimensional vector were developed to recognize the difference, they are not sensitive to small changes in CWTS. For example, the wavelet grey moment (WGM) of the CWTS in Figure 2 and Figure 3 are 20.24 and 20.51, respectively.…”

Section: Proposed Methodsmentioning

confidence: 99%

A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network

Guo

Yang

Gao

et al. 2018

Sensors

193

125

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Fault diagnosis is critical to ensure the safety and reliable operation of rotating machinery. Most methods used in fault diagnosis of rotating machinery extract a few feature values from vibration signals for fault diagnosis, which is a dimensionality reduction from the original signal and may omit some important fault messages in the original signal. Thus, a novel diagnosis method is proposed involving the use of a convolutional neural network (CNN) to directly classify the continuous wavelet transform scalogram (CWTS), which is a time-frequency domain transform of the original signal and can contain most of the information of the vibration signals. In this method, CWTS is formed by discomposing vibration signals of rotating machinery in different scales using wavelet transform. Then the CNN is trained to diagnose faults, with CWTS as the input. A series of experiments is conducted on the rotor experiment platform using this method. The results indicate that the proposed method can diagnose the faults accurately. To verify the universality of this method, the trained CNN was also used to perform fault diagnosis for another piece of rotor equipment, and a good result was achieved.

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Section: Proposed Methodsmentioning

confidence: 99%