A Fault Feature Extraction Method for Rolling Bearings Based on Refined Composite Multi-Scale Amplitude-Aware Permutation Entropy

Song, Youshuo; Wang, Weiyu

doi:10.1109/access.2021.3078823

Cited by 7 publications

(6 citation statements)

References 56 publications

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“…Experimental data. In this case, the bearing vibration data come from the bearing data center of Case Western Reserve University (CWRU) (Lou and Loparo, 2004;Song and Wang, 2021), in which the bearing data acquired at a sampling frequency of 12 kHz are used for analysis. Here, three rotating speeds of 1797 r/min (load 0), 1772 r/min (load 1), and 1750 r/min (load 2) are selected to simulate the bearing operation under variable working conditions, and the vibration data of the three working conditions are marked as Dataset A, Dataset B, and Dataset C, respectively.…”

Section: Experimental Verificationmentioning

confidence: 99%

Rolling bearing fault diagnosis under variable working conditions using deep convolutional fuzzy system

Zhu

Zhou

Chen

et al. 2023

Transactions of the Institute of Measurement and Control

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This paper addresses the application of a deep convolutional fuzzy system (DCFS) for the fault diagnosis of rolling element bearings. The limitations of the conventional deep convolutional neural network (CNN) are the huge computational load of training the tones of parameters and the lack of interpretability for the corresponding parameters. In this paper, a DCFS-based bearing fault diagnosis method under variable working conditions is proposed. The DCFS on a high dimensional input space is a multilayer connection of many low dimensional fuzzy systems, which can overcome the computational and interpretability problems of the traditional CNN. Moreover, to improve the identification efficiency and diagnosis accuracy, the infinite feature selection (Inf-FS) algorithm is employed to select the most informative fault features. The proposed approach is experimentally demonstrated to be able to identify the different fault types and fault severities of rolling bearings under variable running states.

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Section: Experimental Verificationmentioning

confidence: 99%

Rolling bearing fault diagnosis under variable working conditions using deep convolutional fuzzy system

Zhu

Zhou

Chen

et al. 2023

Transactions of the Institute of Measurement and Control

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“…Table 4 shows the main parameters of the CA-YD-186 acceleration sensor. Two clearance states, namely a normal clearance state and a medium-worn state, were tested, using a normal bushing in 0.1 mm clearance and a worn bushing in 0.2 mm clearance [37]. The normal and worn bushings and the connecting rod are shown in Figure 14 The normal bearing clearance vibration signal is shown in Figure 15.…”

Section: Application For Reciprocating Compressor Fault Diagnosismentioning

confidence: 99%

A Double Interpolation and Mutation Interval Reconstruction LMD and Its Application in Fault Diagnosis of Reciprocating Compressor

Zhao

Liu³

et al. 2023

Applied Sciences

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The accuracy and stability of the envelope estimation function are enduring issues throughout the research process of LMD. This paper presents double interpolation and mutation interval reconstruction local mean decomposition (DIMIRLMD) to improve the stability of the demodulation process and the accuracy of PF components. DIMIRLMD first proposes a mutation interval reconstruction envelope algorithm using extreme symmetry points to suppress the demodulation mutation phenomenon, which disturbs the stability of the demodulation process, and then selects the optimal PF component from a double interpolation PF component library based on the index of orthogonality (IO) for a better hierarchical property. DIMIRLMD was employed to analyze the simulation signal and vibration signal of a reciprocating compressor in an oversized bearing clearance state, and the results illustrate its performances are more excellent than those of three other LMD methods. Furthermore, the envelope frequency spectrum obtained from the proposed LMD presents a clear double rotation fault frequency and lower noise disturbance.

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“…Then, we explored the relationship between the GE values and embeddin q. Although the larger the q value, the richer the information obtained after ph construction, a too-large q value is disadvantageous owing to the need of a very 30 q ), which is difficult to meet for a bearing vibration time series [22]. Figure 3 GE values of 1/f noise according to different embedding dimension q values; w with the increase in q value, GE also gradually increases and converges to a worth noting that when q ≥ 3, the fluctuation trend at different nodes no lon This indicates that the GE with embedding dimension q = 3 is sufficient to accu the dynamic changes.…”

Section: The Parameter Selection Of Hgementioning

confidence: 99%

“…Then, we explored the relationship between the GE values and embedding dimension q. Although the larger the q value, the richer the information obtained after phase space reconstruction, a too-large q value is disadvantageous owing to the need of a very large N (10 q -30 q ), which is difficult to meet for a bearing vibration time series [22]. Figure 3 illustrates the GE values of 1/f noise according to different embedding dimension q values; we found that with the increase in q value, GE also gradually increases and converges to about 0.3.…”

Section: The Parameter Selection Of Hgementioning

confidence: 99%

“…For instance, Wang et al [21] proposed a degradation feature extraction technique based on equalization symbol sequence entropy. Song et al [22] proposed a new nonlinear dynamic analysis method called refined composite multiscale amplitude-aware permutation entropy to extract features from bearing life data. Rai et al [23] used multiscale fuzzy entropy as a fault feature that formed probability distributions, after which the Jensen-Renyi divergence technique was applied, which discriminates the probability distribution of degraded multiscale entropy feature vectors against healthy multiscale entropy feature vectors to formulate the desired health indicator.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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A Fault Feature Extraction Method for Rolling Bearings Based on Refined Composite Multi-Scale Amplitude-Aware Permutation Entropy

Cited by 7 publications

References 56 publications

Rolling bearing fault diagnosis under variable working conditions using deep convolutional fuzzy system

Rolling bearing fault diagnosis under variable working conditions using deep convolutional fuzzy system

A Double Interpolation and Mutation Interval Reconstruction LMD and Its Application in Fault Diagnosis of Reciprocating Compressor

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

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