Motor shaft misalignment detection using multiscale entropy with wavelet denoising

Lin, Jun-Lin; Liu, Julie Yu‐Chih; Li, Chih-Wen; Tsai, Li-Feng; Chung, Hsin-Yi

doi:10.1016/j.eswa.2010.04.009

Cited by 53 publications

(22 citation statements)

References 20 publications

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“…The MSE has been successfully applied to different research fields in the past decades. These applications include the analyses of the human gait dynamics [2], heart rate variability [3,4], electroencephalogram [5], postural control [6], vibration of rotary machine [7,8], rainfall time series [9], time series of river flow [10], electroseismic time series [11], time series of traffic flow [12], social dynamics [13], chatter in the milling process [14], and vibrations of a vehicle [15], etc.. These works demonstrate the effectiveness of the MSE algorithm for the analysis of the complex time series.…”

Section: Introductionmentioning

confidence: 99%

Time Series Analysis Using Composite Multiscale Entropy

Lin

et al. 2013

Entropy

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Multiscale entropy (MSE) was recently developed to evaluate the complexity of time series over different time scales. Although the MSE algorithm has been successfully applied in a number of different fields, it encounters a problem in that the statistical reliability of the sample entropy (SampEn) of a coarse-grained series is reduced as a time scale factor is increased. Therefore, in this paper, the concept of a composite multiscale entropy (CMSE) is introduced to overcome this difficulty. Simulation results on both white noise and 1/f noise show that the CMSE provides higher entropy reliablity than the MSE approach for large time scale factors. On real data analysis, both the MSE and CMSE are applied to extract features from fault bearing vibration signals. Experimental results demonstrate that the proposed CMSE-based feature extractor provides higher separability than the MSE-based feature extractor.

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Section: Introductionmentioning

confidence: 99%

Time Series Analysis Using Composite Multiscale Entropy

Lin

et al. 2013

Entropy

273

204

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“…In general, the entropy increases with the degree of disorder and is the maximum for a completely random system [23]. Since the entropy analysis has been utilized for feature extraction of pathological and biological systems and presented the effectiveness of disease diagnosis, the concept of entropy quantification can be also employed for machine fault detection [25][26][27][28].…”

Section: Entropy and Multi-scale Analysismentioning

confidence: 99%

“…Therefore, they proposed the idea of multi-scale entropy (MSE) for separating the human heartbeat signals of healthy and pathological groups. In light of the effectiveness of MSE in physiological and biological signal analysis, different MSE-based algorithms and improved methods were proposed to reduce the influence of noise and employed in vibration analysis of rotating machinery [25][26][27][28]. Furthermore, the concept of multi-scale permutation entropy (MPE) was applied to diagnose bearing faults and demonstrated enhanced performance compared with the single scale permutation entropy method [29].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Analysis Based Ball Bearing Defect Diagnostics Using Mahalanobis Distance and Support Vector Machine

et al. 2013

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Abstract:The objective of this research is to investigate the feasibility of utilizing the multi-scale analysis and support vector machine (SVM) classification scheme to diagnose the bearing faults in rotating machinery. For complicated signals, the characteristics of dynamic systems may not be apparently observed in a scale, particularly for the fault-related features of rotating machinery. In this research, the multi-scale analysis is employed to extract the possible fault-related features in different scales, such as the multi-scale entropy (MSE), multi-scale permutation entropy (MPE), multi-scale root-mean-square (MSRMS) and multi-band spectrum entropy (MBSE). Some of the features are then selected as the inputs of the support vector machine (SVM) classifier through the Fisher score (FS) as well as the Mahalanobis distance (MD) evaluations. The vibration signals of bearing test data at Case Western Reserve University (CWRU) are utilized as the illustrated examples. The analysis results demonstrate that an accurate bearing defect diagnosis can be achieved by using the extracted machine features in different scales. It can be also noted that the diagnostic results of bearing faults can be further enhanced through the feature selection procedures of FS and MD evaluations.

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“…''db4", ''db8" and ''db20" from the Daubechies family were used as mother wavelets with no clear advantage of one of them in the ANN performances. (Lin et al, 2010) utilized vibration measurements to distinguish effectively between aligned and misaligned motors. The proposed method calculates the difference between the MSE of the original vibration signal and that of the signal after the signal is de-noised by wavelet transform.…”

Section: Motorsmentioning

confidence: 99%