An adaptive multiple time domain synchronous averaging method and its application in vibration signal feature enhancement

Yin, Na; Meng, Zong; Guan, Yaping; Fan, Fengjie

doi:10.1088/1361-6501/ac3d08

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Techniques related to synchronous analysis have demonstrated their effectiveness and practicality in rotation machinery damage detection and condition monitoring [6][7][8], especially in cases with variable speed operations. For example, synchronous averaging can effectively separate the information synchronized with the shaft speed [9][10][11][12], such as the shaft speed response, the gear meshing response, and their high-order harmonics. For bearing fault analysis, the application of the virtual shaft concept [13] has been introduced with success.…”

Section: Introductionmentioning

confidence: 99%

Improved Synchronous Sampling and Its Application in High-Speed Railway Bearing Damage Detection

Wang,

Huang,

Zhang

et al. 2024

Machines

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Synchronous analysis is one of the most effective and practical techniques in rotating machinery diagnostics, especially in cases with variable speed operations. A modern analog-to-digital convertor (ADC) usually digitizes an analog signal to an equal time interval data series. Synchronous resampling converts the data series from an equal time interval data series to an equal shaft rotation angle interval data series. This conversion is usually achieved in the digital domain with the aid of shaft speed information, through either direct measurement or identification from a measured vibration signal, which is a time-consuming process. In order to improve the computational efficiency as well as the data processing accuracy, in this paper, a fast synchronous time-point calculation method based on an inverse function interpolation procedure is proposed. By identifying the inverse function of the instantaneous phase with respect to time, the calculation process of synchronous time points is optimized, which results in improved calculation efficiency and accuracy. These advantages are demonstrated by numerical simulations as well as experimental verifications. The numerical simulation results show that the proposed method can improve calculation speed by about five times. The synchronous analysis based on the proposed method was applied to a bearing fault detection in a high-speed rail carriage, which demonstrated the advantages of the proposed algorithm in improving the signal-to-noise ratio (SNR) for bearing damage feature extraction.

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Section: Introductionmentioning

confidence: 99%

Improved Synchronous Sampling and Its Application in High-Speed Railway Bearing Damage Detection

Wang,

Huang,

Zhang

et al. 2024

Machines

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“…Bearing failures significantly reduce the operational reliability of machinery, resulting in economic losses and casualties [6]. During the rotation of the faulty bearing, a series of cyclic repetitive impulses are generated between the faulty part and bearing components [7]. The fault impulses are modulated by the inherent frequency of the rolling bearing, recursively decomposing the signal into a group of amplitude modulation-frequency modulation (AM-FM) components, which are named as intrinsic mode functions.…”

Section: Introductionmentioning

confidence: 99%

An adaptive spectrum segmentation-based optimized VMD method and its application in rolling bearing fault diagnosis

Meng¹,

Wang²,

Liu³

et al. 2022

Meas. Sci. Technol.

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Variational Mode Decomposition (VMD) is a signal decomposition algorithm with excellent denoising ability. However, the drawback that VMD is unable to determine the input parameters adaptively seriously affects the decomposition results. For this issue, an optimized VMD method based on modified scale-space representation (MSSR-VMD) is proposed. Firstly, MSSR is proposed to segment the fault signal spectrum, acquiring modes' number and the initial center frequency for each mode adaptively. Moreover, a pre-decomposition step is added to the original VMD, which selects a target mode from divided frequency bands. Finally, the penalty factor of the target mode is adjusted during the iterative update of the VMD to achieve accurate extraction for the fault features. MSSR-VMD and other adaptive decomposition algorithms are employed to handle the simulated and experimental signals separately. By comparing the analysis results, the method has certain superiority in rolling bearing fault feature extraction.

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Fault Diagnosis of Marine Diesel Engine Based on Multi-scale Time Domain Decomposition and Convolutional Neural Network

Li,

Cui

2024

Polish Maritime Research

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Marine diesel engines work in an environment with multiple excitation sources. Effective feature extraction and fault diagnosis of diesel engine vibration signals have become a hot research topic. Time-domain synchronous averaging (TSA) can effectively handle vibration signals. However, the key phase signal required for TSA is difficult to obtain. During signal processing, it can result in the loss of information on fault features. In addition, frequency multiplication signal waveforms are mixed. To address this problem, a multi-scale time-domain averaging decomposition (MTAD) method is proposed and combined with signal-to-image conversion and a convolutional neural network (CNN), to perform fault diagnosis on a marine diesel engine. Firstly, the vibration signals are decomposed by MTAD. The MTAD method does not require the acquisition of the key phase signal and can effectively overcome signal aliasing. Secondly, the decomposed signal components are converted into 2-D images by signal-to-image conversion. Finally, the 2-D images are input into the CNN for adaptive feature extraction and fault diagnosis. Through experiments, it is verified that the proposed method has certain noise immunity and superiority in marine diesel engine fault diagnosis.

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An adaptive multiple time domain synchronous averaging method and its application in vibration signal feature enhancement

Cited by 3 publications

References 30 publications

Improved Synchronous Sampling and Its Application in High-Speed Railway Bearing Damage Detection

Improved Synchronous Sampling and Its Application in High-Speed Railway Bearing Damage Detection

An adaptive spectrum segmentation-based optimized VMD method and its application in rolling bearing fault diagnosis

Fault Diagnosis of Marine Diesel Engine Based on Multi-scale Time Domain Decomposition and Convolutional Neural Network

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