Bearing Fault Diagnosis Method Based on Improved Singular Value Decomposition Package

Zhu, Huibin; He, Zhangming; Xiao, Yaqi; Wang, Jiongqi; Zhou, Hongyan

doi:10.3390/s23073759

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…Specificity = TN/(TN + FP) (27) where sensitivity measures the proportion of actual positives correctly identified, and specificity measures the ratio of real negatives correctly identified. Good models usually have high sensitivity and high specificity.…”

Section: Comparison With Other Relevant Methodsmentioning

confidence: 99%

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Intelligent Health Assessment of Aviation Bearing Based on Deep Transfer Graph Convolutional Networks under Large Speed Fluctuations

Zhao

Zhu

Yao

et al. 2023

Sensors

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As a critical support and fixed component of aero engines, electro-hydrostatic actuators, and other equipment, the operation of aviation bearings is often subject to high speed, high-temperature rise, large load, and other continuous complex fluctuation conditions, which makes their health assessment tasks more difficult. To solve this problem, an intelligent health assessment method based on a new Deep Transfer Graph Convolutional Network (DTGCN) is proposed for aviation bearings under large speed fluctuation conditions. First, a new DTGCN algorithm is designed, which mainly uses the domain adaptation mechanism to enhance the performance of Graph Convolutional Network (GCN) and the generalization performance of transfer properties. Specifically, order spectrum analysis is employed to resample the vibration signals of aviation bearings and transform them into order spectral signals. Then, the trained 1dGCN is used as the feature extractor, and the designed Dynamic Multiple Kernel Maximum Mean Discrepancy (DMKMMD) is calculated to match the difference in edge distribution. Finally, the aligned features are fed into the softmax classifier for intelligent health assessment. The effectiveness of the proposed diagnostic algorithm and method are validated by using aviation bearing fault data set under large speed fluctuation conditions.

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Section: Comparison With Other Relevant Methodsmentioning

confidence: 99%

“…Specifically, the domain adaptation problem and task for cross-bearing fault feature extraction can be described as follows [27,28]: First, assuming that a labeled source domain…”

Section: Problem Descriptionmentioning

confidence: 99%

Intelligent Health Assessment of Aviation Bearing Based on Deep Transfer Graph Convolutional Networks under Large Speed Fluctuations

Zhao

Zhu

Yao

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…VMD can effectively separate the various components of a signal, select the modal reconstruction signal after modal decomposition, eliminate the modes containing background noise in the signal, and then reconstruct the modes of each order, to achieve its purpose of raising a signal from background noise. As a result, this strategy is often used for signal denoising [6,7]. Bai et al [8] have designed an intelligent fault diagnosis strategy that combines optimized VMD and migration learning for diesel engine fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method of Roadheader Bearing Based on VMD and Domain Adaptive Transfer Learning

Zhang

2023

Sensors

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The roadheader is a core piece of equipment for underground mining. The roadheader bearing, as its key component, often works under complex working conditions and bears large radial and axial forces. Its health is critical to efficient and safe underground operation. The early failure of a roadheader bearing has weak impact characteristics and is often submerged in complex and strong background noise. Therefore, a fault diagnosis strategy that combines variational mode decomposition and a domain adaptive convolutional neural network is proposed in this paper. To start with, VMD is utilized to decompose the collected vibration signals to obtain the sub-component IMF. Then, the kurtosis index of IMF is calculated, with the maximum index value chosen as the input of the neural network. A deep transfer learning strategy is introduced to solve the problem of the different distributions of vibration data for roadheader bearings under variable working conditions. This method was implemented in the actual bearing fault diagnosis of a roadheader. The experimental results indicate that the method is superior in terms of diagnostic accuracy and has practical engineering application value.

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A Novel Method for Identifying Resonance Frequency Band in Weak Bearing Fault Diagnosis of Electric Driving System

Yue,

Zhu,

Cheng

et al. 2024

Shock and Vibration

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Because of the uncertainty of the structure and environment of the electric driving system (EDS), the fault signature of the rotating mechanism is complicated. A novel method based on Hilbert transform with modified fast kurtogram (HTMFK), which is used for identifying the bearing faults in the EDS, is proposed. The modified principle and algorithm flow of the proposed method are derived. A high pass filter based on the frequency band identified by HTMFK is constructed and applied to fault diagnosis. Simulation signals demonstrate the ability of demodulating signals and identifying the fault resonance band. The bearing fault bench experiment of EDS is carried out in a semianechoic chamber. The corresponding fault tests are conducted according to different operating conditions. The applicability of HTMFK is verified by comparing the square envelope spectrums. Compared with other methods, the proposed method identifies the fault resonance frequency band more effectively and expands the application range of bearing fault diagnosis in EDS.

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Bearing Fault Diagnosis Method Based on Improved Singular Value Decomposition Package

Cited by 4 publications

References 29 publications

Intelligent Health Assessment of Aviation Bearing Based on Deep Transfer Graph Convolutional Networks under Large Speed Fluctuations

Intelligent Health Assessment of Aviation Bearing Based on Deep Transfer Graph Convolutional Networks under Large Speed Fluctuations

Fault Diagnosis Method of Roadheader Bearing Based on VMD and Domain Adaptive Transfer Learning

A Novel Method for Identifying Resonance Frequency Band in Weak Bearing Fault Diagnosis of Electric Driving System

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