A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM

Lv, Mingyang; Zhang, Chunguang; Guo, Aibin; Liu, Fang

doi:10.1109/access.2020.3048492

Cited by 12 publications

(4 citation statements)

References 49 publications

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“…where x 1 j = y j /y j = 1, x q j = y j+q−1 /y j . Given the similarity tolerance e, e ∈ (0,1], the grey relational coefficient that indicates the closeness between X q (i) and X q (j) is defined as (5) where…”

Section: Grey Entropymentioning

confidence: 99%

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A New Approach to the Degradation Stage Prediction of Rolling Bearings Using Hierarchical Grey Entropy and a Grey Bootstrap Markov Chain

Cheng,

Ma,

Gao

2023

Sensors

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Degradation stage prediction, which is crucial to monitoring the health condition of rolling bearings, can improve safety and reduce maintenance costs. In this paper, a novel degradation stage prediction method based on hierarchical grey entropy (HGE) and a grey bootstrap Markov chain (GBMC) is presented. Firstly, HGE is proposed as a new entropy that measures complexity, considers the degradation information embedded in both lower- and higher-frequency components and extracts the degradation features of rolling bearings. Then, the HGE values containing degradation information are fed to the prediction model, based on the GBMC, to obtain degradation stage prediction results more accurately. Meanwhile, three parameter indicators, namely the dynamic estimated interval, the reliability of the prediction result and dynamic uncertainty, are employed to evaluate the prediction results from different perspectives. The estimated interval reflects the upper and lower boundaries of the prediction results, the reliability reflects the credibility of the prediction results and the uncertainty reflects the dynamic fluctuation range of the prediction results. Finally, three rolling bearing run-to-failure experiments were conducted consecutively to validate the effectiveness of the proposed method, whose results indicate that HGE is superior to other entropies and the GBMC surpasses other existing rolling bearing degradation prediction methods; the prediction reliabilities are 90.91%, 90% and 83.87%, respectively.

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Section: Grey Entropymentioning

confidence: 99%

“…Such unexpected damage can make the mechanical system break down and result in enormous economic losses. Therefore, to maintain the safe and reliable operation of mechanical systems and keep a low downtime, it is of practical significance to predict the performance degradation stages of rolling bearings [4,5].…”

Section: Introductionmentioning

confidence: 99%

A New Approach to the Degradation Stage Prediction of Rolling Bearings Using Hierarchical Grey Entropy and a Grey Bootstrap Markov Chain

Cheng,

Ma,

Gao

2023

Sensors

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“…It describes the parameters of correlation between two signals. Suppose that the i-th signal in the source signal s is s i , and the estimated signal obtained after the algorithm is run is s i ′ , then the calculation formula of the correlation coefficient between s i and s i ′ is as follows [48]:…”

Section: Correlation Coefficientmentioning

confidence: 99%

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

Miao

Zhao

Jia

et al. 2021

International Journal of Rotating Machinery

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The vibration signal of rotating machinery compound faults acquired in actual fields has the characteristics of complex noise sources, the strong background noise, and the nonlinearity, causing the traditional blind source separation algorithm not be suitable for the blind separation of rotating machinery coupling fault. According to these problems, an extraction method of multisource fault signals based on wavelet packet analysis (WPA) and fast independent component analysis (FastICA) was proposed. Firstly, according to the characteristic of the vibration signal of rotating machinery, an effective denoising method of wavelet packet based on average threshold is presented and described to reduce the vibration signal noise. In the method, the thresholds of every node of the best wavelet packet basis are acquired and averaged, and then the average value is used as a global threshold to quantize the decomposition coefficient of every node. Secondly, the mixed signals were separated by using the improved FastICA algorithm. Finally, the results of simulations and real rotating machinery vibration signals analysis show that the method can extract the rotating machinery fault characteristics, verifying the effectiveness of the proposed algorithm.

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“…Similarly, a rich literature exists on the topic of feature extraction approaches on vibration signals for prognostic monitoring applications. These include statistical time-domain features [25][26][27] and timefrequency feature extraction methods such as Short-time Fourier Transform (STFT) [28][29][30], signal Envelope Analysis (EA) [31][32][33], Wavelet Transform (WT) [34,35], Feature Mode Decomposition (FMD) [36], and Empirical Mode Decomposition (EMD) methods [37][38][39][40]. In recent years, a variety of Machine Learning (ML) algorithms have been proposed to perform the task of bearing RUL predictions.…”

Section: Introductionmentioning

confidence: 99%

Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings

2023

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Improving the reliability and performance of electric and rotating machines is crucial to many industrial applications. This will lead to improved robustness, efficiency, and eco-sustainability, as well as mitigate significant health and safety concerns regarding sudden catastrophic failure modes. Bearing degradation is the most significant cause of machine failure and has been reported to cause up to 75% of low-voltage machine failures. This paper introduces a low complexity machine learning (ML) approach to estimate the remaining useful life (RUL) of rolling element bearings using real vibration signals. This work explores different ML recipes using novel feature engineering coupled with various k-Nearest Neighbour (k-NN), and Support Vector Machines (SVM) kernel and weighting functions in order to optimise this RUL approach. Original non-linear wear state models and feature sets are investigated, the latter are derived from Short-time Fourier Transform (STFT) and Hilbert Marginal Spectrum (HMS). These feature sets incorporate one-third octave band filtering for low complexity multivariate feature subspace compression. Our proposed ML algorithm stage has employed two robust supervised ML approaches: weighted k-NN and SVM. Real vibration data were drawn from the Pronostia platform to test and validate this prognostic monitoring approach. The results clearly demonstrate the effectiveness of this approach, with classification accuracy results of up to 82.8% achieved. This work contributes to the field by introducing a robust and computationally inexpensive method for accurate monitoring of machine health using low-cost vibration-based sensing.

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A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM

Cited by 12 publications

References 49 publications

A New Approach to the Degradation Stage Prediction of Rolling Bearings Using Hierarchical Grey Entropy and a Grey Bootstrap Markov Chain

A New Approach to the Degradation Stage Prediction of Rolling Bearings Using Hierarchical Grey Entropy and a Grey Bootstrap Markov Chain

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings

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