A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings

Liu, Huanhuan; Han, Minghong

doi:10.1016/j.mechmachtheory.2014.01.011

Cited by 170 publications

(105 citation statements)

References 15 publications

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“…Liu and Han [7] proposed a novel fault feature extraction method based on the local mean decomposition (LMD) and the multi-scale entropy. The researchers conducted a study on faulty rolling bearing.…”

Section: Fig 1: the Decomposition Results Generated By (A) Emd (B) Lcmentioning

confidence: 99%

Vibration Condition Monitoring: Latest Trend and Review

Hui

Hee

Leong

et al. 2015

AMM

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Abstract. Vibration analysis has proven to be the most effective method for machine condition monitoring to date. Various effective signal analysis methods to analyze and extract fault signature that embedded in the raw vibration signals have been introduced in the past few decades such as fast Fourier transform (FFT), short time Fourier transform (STFT), wavelets analysis, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), etc. however, these is still a need for human to interpret vibration signature of faults and it is regarded as one of the major challenge in vibration condition monitoring. Thus, most recent researches in vibration condition monitoring revolved around using Artificial Intelligence (AI) techniques to automate machinery faults detection and diagnosis. The most recent literatures in this area show that researches are mainly focus on using machine learning techniques for data fusion, features fusion, and also decisions fusion in order to achieve a higher accuracy of decision making in vibration condition monitoring. This paper provides a review on the most recent development in vibration signal analysis methods as well as the AI techniques used for automated decision making in vibration condition monitoring in the past two years.

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Section: Fig 1: the Decomposition Results Generated By (A) Emd (B) Lcmentioning

confidence: 99%

Vibration Condition Monitoring: Latest Trend and Review

Hui

Hee

Leong

et al. 2015

AMM

View full text Add to dashboard Cite

show abstract

“…This is because of its strong ability to deal with nonstationary signals and superior time-frequency analysis performance. However, LMD itself also has a large amount of iterative computation and problems associated with end effects [10,11]. Recently, Cheng et al proposed a new selfadaptive signal processing method, local characteristic-scale decomposition (LCD), which can decompose a nonstationary signal into several intrinsic scale components (ISCs) [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

Liang

Dong-min

Daidi

et al. 2018

Shock and Vibration

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A rolling bearing fault diagnosis method based on ensemble local characteristic-scale decomposition (ELCD) and extreme learning machine (ELM) is proposed. Vibration signals were decomposed using ELCD, and numerous intrinsic scale components (ISCs) were obtained. Next, time-domain index, energy, and relative entropy of intrinsic scale components were calculated. According to the distance-based evaluation approach, sensitivity features can be extracted. Finally, sensitivity features were input to extreme learning machine to identify rolling bearing fault types. Experimental results show that the proposed method achieved better performance than support vector machine (SVM) and backpropagation (BP) neural network methods.

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“…Furthermore, many contributions [8][9][10][11][12] have considered information entropy as a fault feature for fault diagnosis because the occurrence of faults may change the signal distribution in the time and frequency domains. However, a feature is merely sensitive to a corresponding failure.…”

Section: Introductionmentioning

confidence: 99%

Motion Sequence Decomposition-Based Hybrid Entropy Feature and Its Application to Fault Diagnosis of a High-Speed Automatic Mechanism

Wang¹,

Hong-xia²,

Du³

2017

Entropy

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High-speed automatic weapons play an important role in the field of national defense. However, current research on reliability analysis of automaton principally relies on simulations due to the fact that experimental data are difficult to collect in real life. Different from rotating machinery, a high-speed automaton needs to accomplish complex motion consisting of a series of impacts. In addition to strong noise, the impacts generated by different components of the automaton will interfere with each other. There is no effective approach to cope with this in the fault diagnosis of automatic mechanisms. This paper proposes a motion sequence decomposition approach combining modern signal processing techniques to develop an effective approach to fault detection in high-speed automatons. We first investigate the entire working procedure of the automatic mechanism and calculate the corresponding action times of travel involved. The vibration signal collected from the shooting experiment is then divided into a number of impacts corresponding to action orders. Only the segment generated by a faulty component is isolated from the original impacts according to the action time of the component. Wavelet packet decomposition (WPD) is first applied on the resulting signals for investigation of energy distribution, and the components with higher energy are selected for feature extraction. Three information entropy features are utilized to distinguish various states of the automaton using empirical mode decomposition (EMD). A gray-wolf optimization (GWO) algorithm is introduced as an alternative to improve the performance of the support vector machine (SVM) classifier. We carry out shooting experiments to collect vibration data for demonstration of the proposed work. Experimental results show that the proposed work in this paper is effective for fault diagnosis of a high-speed automaton and can be applied in real applications. Moreover, the GWO is able to provide a competitive diagnosis result compared with the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm.

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A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings

Cited by 170 publications

References 15 publications

Vibration Condition Monitoring: Latest Trend and Review

Vibration Condition Monitoring: Latest Trend and Review

A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

Motion Sequence Decomposition-Based Hybrid Entropy Feature and Its Application to Fault Diagnosis of a High-Speed Automatic Mechanism

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