Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference

Zhang, Long; Xiong, Guoming; Liu, Hesheng; Zhang, Huijun; Guo, Wu

doi:10.1016/j.eswa.2010.02.118

Cited by 308 publications

(155 citation statements)

References 37 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

276

204

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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

276

204

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“…Finally, the FuzzyEns were used as the input of the ANFIS, which achieved the accurate classification of bearing fault types. Zhang, et al [48], proposed an early fault diagnosis method based on multiscale entropy and ANFIS. Tran, et al [49], combined ANFIS with decision tree to achieve fault diagnosis.…”

Section: Fuzzy Logic and Neuro-fuzzy Systems (Nfss)mentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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“…There are several ways to calculate entropy, such as the approximate entropy, spectral entropy, multi-scale entropy, energy entropy. They are widely applied in feature extraction and fault diagnosis of machinery [35][36][37][38].…”

Section: Estimation Of Possible Outliers With Entropymentioning

confidence: 99%

Defect Detection for Wheel-Bearings with Time-Spectral Kurtosis and Entropy

Chen

Zeng

Chen

2014

Entropy

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Wheel-bearings easily acquire defects due to their high-speed operating conditions and constant metal-metal contact, so defect detection is of great importance for railroad safety. The conventional spectral kurtosis (SK) technique provides an optimal bandwidth for envelope demodulation. However, this technique may cause false detections when processing real vibration signals for wheel-bearings, because of sparse interference impulses. In this paper, a novel defect detection method with entropy, time-spectral kurtosis (TSK) and support vector machine (SVM) is proposed. In this method, the possible outliers in the short time Fourier transform (STFT) amplitude series are first estimated and preprocessed with information entropy. Then the method extends the SK technique to the time-domain, and extracts defective frequencies from reconstructed vibration signals by TSK filtering. Finally, the multi-class SVM was applied to classify bearing defects. The effectiveness of the proposed method is illustrated using real wheel-bearing vibration signals. Experimental results show that the proposed method provides a better performance in defect frequency detection and classification than the conventional SK-based envelope demodulation.

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Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference

Cited by 308 publications

References 37 publications

Time Series Analysis Using Composite Multiscale Entropy

Time Series Analysis Using Composite Multiscale Entropy

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Defect Detection for Wheel-Bearings with Time-Spectral Kurtosis and Entropy

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