Transient waveform matching based on ascending multi-wavelets for diagnostics and prognostics of bearing deterioration

Jiang, Kuosheng; Li, Zhixiong; Zhou, Yuanyuan; Sarkodie-Gyan, Thompson; Li, Weihua

doi:10.1016/j.isatra.2021.03.016

Cited by 16 publications

(3 citation statements)

References 42 publications

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“…The prerequisite for the feature extraction algorithm is to establish an effective signal model of bearing vibrations. Among them, the most commonly used is the deterministic signal model, which directly provides the waveform function caused by defects, such as various wavelet functions [1]. Based on this, a series of strategies have been proposed to construct the most matching fault feature waveform to achieve better performance [2,3].…”

Section: Introductionmentioning

confidence: 99%

Blind cyclostationary fault feature extraction in rolling bearings: a dual adaptive filtering approach

Sun,

Su,

Yang

et al. 2024

Meas. Sci. Technol.

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Extracting cyclostationary features from vibration signals is one of the most eﬀective approaches in bearing fault diagnosis. However, current methods require prior knowledge of cycle-frequencies or other statistical information, which constrains their applicability across various scenarios. In this paper, we introduce a novel dual adaptive ﬁltering method, incorporating cycle-frequency estimation to solve the existing problem. The method ﬁrstly employs an adaptive line enhancer (ALE) to isolate the ﬁrst-order cyclostationary signal, thereby the cycle-frequencies can be eﬀectively detected using an exhaustive estimation technique. Subsequently, an adaptive frequency-shift (FRESH) ﬁlter is further applied to extract the second-order cyclostationary features from the residual components. The proposed method successfully overcomes the challenge of separating cyclostationary signals without prior knowledge and can be tailored to real-time application scenarios. Besides, the approach distinguishes between the two cyclostationary signal types, effectively resolving any aliasing concerns inherent in their statistical characteristics. The eﬀectiveness of the method is veriﬁed through simulation, experiments, and engineering data analysis. It is demonstrated that the method signiﬁcantly enhances diagnostic accuracy and is more suitable for early fault diagnosis of rolling bearings by estimating spectral coherence on the extracted signals.

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Section: Introductionmentioning

confidence: 99%

Blind cyclostationary fault feature extraction in rolling bearings: a dual adaptive filtering approach

Sun,

Su,

Yang

et al. 2024

Meas. Sci. Technol.

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“…Torque is an important performance indicator of RV reducers, but the collected torque signals often contain a significant amount of noise due to environmental factors and exhibit non-linear and non-stationary characteristics. Wavelet thresholding and Empirical Mode Decomposition (EMD) [3] denoising methods have been extensively used by researchers for vibration signal denoising in rotating machinery, with good results [4][5]. However, wavelet thresholding methods suffer from the difficulty in selecting optimal thresholds, while EMD denoising methods have difficulty achieving good denoising performance in low signal-to-noise ratio conditions.…”

Section: Introductionmentioning

confidence: 99%

RV Reducer Torque Signal Denoising Research Based VMD

Wu,

Zheng,

Song

et al. 2023

AETR

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The RV reducer is a core component in industrial robots, but its performance is facing higher demands due to the expanding application scenarios. Accurately obtaining the torque signal is crucial for achieving precise control of the RV reducer, as torque is an important performance indicator. In this paper, a VMD-based noise reduction method for RV reducer torque signals is proposed, in which the VMD algorithm is used to decompose the original signal, and the IMFs are selected for signal reconstruction by fusing the correlation index and sample entropy. Experimental results show that the proposed method achieves higher SNR and lower MSE after noise reduction, demonstrating its effectiveness. This method provides a foundation for the precise control of RV reducers.

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“…Schmidt et al 17 proposed a system framework for obtaining consistent feature surfaces under time-varying operating conditions, which is suitable for fault diagnosis of gears and bearings under time-varying operating conditions. Jiang et al 18 propose a novel ascension multi-wavelet method for diagnosing the undergoing degradation state and predicting the remaining useful life of the bearings.…”

Section: Introductionmentioning

confidence: 99%

Application of instantaneous phase detection technology based on laser displacement sensor in fault diagnosis of spur gear of rotation vector reducer

Jiang

Wang

et al. 2022

Opt. Eng.

Self Cite

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Aiming at the problem of a spur gear fault of the rotation vector (RV) reducer in industrial robots, an instantaneous phase detection technology based on eccentric modulation and laser displacement sensor is proposed. Taking the RV-20E reducer as the object, through the analysis of the RV reducer gear fault mechanism, an RV reducer fault diagnosis test bed is developed to simulate the actual operating conditions of the RV reducer of industrial robot. The instantaneous phase information of the RV reducer is collected, and the adaptive permutation entropy is used to process the instantaneous phase data, which can judge the gear fault type quickly and effectively. The test results show that the fault diagnosis test bench based on this detection technology can effectively and quantitatively judge the fault type of the spur gear of the RV reducer. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Transient waveform matching based on ascending multi-wavelets for diagnostics and prognostics of bearing deterioration

Cited by 16 publications

References 42 publications

Blind cyclostationary fault feature extraction in rolling bearings: a dual adaptive filtering approach

Blind cyclostationary fault feature extraction in rolling bearings: a dual adaptive filtering approach

RV Reducer Torque Signal Denoising Research Based VMD

Application of instantaneous phase detection technology based on laser displacement sensor in fault diagnosis of spur gear of rotation vector reducer

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