Joint amplitude and frequency demodulation analysis based on local mean decomposition for fault diagnosis of planetary gearboxes

Feng, Zhipeng; Zuo, Ming J.; Qu, Jian; Tian, Tao; Liu, Zhiliang

doi:10.1016/j.ymssp.2013.05.016

Cited by 106 publications

(54 citation statements)

References 32 publications

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“…[202], the authors applied the LMD method to the gear and roller bearing fault diagnosis and proved that LMD has better performance compared with EMD [202]. Feng et al [203] proposed a joint amplitude and frequency demodulation method based on LMD for fault diagnosis of planetary gearboxes, whereas Liu and Han [204] used LMD to decompose the non-linear and non-stationary fault bearing signals into a series of product functions for feature extraction. Variational mode decomposition (VMD) is a newly developed technique for adaptive signal decomposition, and can non-recursively decompose a multi-component signal into a number of quasi-orthogonal intrinsic mode functions.…”

Section: Emd Lmd and Vmdmentioning

confidence: 99%

Basic research on machinery fault diagnostics: Past, present, and future trends

et al. 2017

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Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

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Section: Emd Lmd and Vmdmentioning

confidence: 99%

Basic research on machinery fault diagnostics: Past, present, and future trends

et al. 2017

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“…The IMFs represent the natural oscillatory mode embedded in the signal and work as the basis functions, which are determined by the signal itself. The detailed theory of HHT, including EMD, can be seen in documents [19][20][21][22][23] and it will not be introduced any more in this paper.…”

Section: Emd Theorymentioning

confidence: 99%

“…Empirical mode decomposition (EMD) [19][20][21][22][23], as a new time-frequency analysis technique, has been developed and widely applied in fault diagnosis of rotating machinery recently. EMD can decompose the complicate signal into a set of complete, simple and almost orthogonal components named intrinsic mode function (IMF).…”

Section: Introductionmentioning

confidence: 99%

Application of empirical mode decomposition and Euclidean distance technique for feature selection and fault diagnosis of planetary gearbox

Li¹,

Zhao²,

Liu³

et al. 2016

J. vibroeng.

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Abstract.Planetary gearbox plays an important role in large and complex mechanical equipment due to the advantage that it can provide larger transmission ratio in a compact space than fixed shaft gearbox. However, its fault diagnosis is a dilemma due to the special structure and harsh working conditions. This paper applies Empirical Mode Decomposition (EMD) and Euclidean Distance Technique (EDT) for planetary gearbox feature selection and fault diagnosis. EMD is a self-adaptive signal processing method that can be applied to non-linear and non-stationary signal and it can also get the aim of de-noising. EDT can give out the quantitative fault diagnosis result. And its theoretical knowledge is easy to understand. An intrinsic mode function (IMF) selection method based on energy ratio is proposed to select IMFs which include sensitive fault information. A two-stage feature selection and weighting method based on EDT is applied to get a new combinative feature and 36 feature parameters are extracted before this process. Then, the feature vector matrix of each raw signal can be computed out by extracting the new combinative feature from every IMF. Finally, the diagnosis result can be obtained through calculating the Euclidean Distance value between two feature vector matrixes. Namely, the health state of the tested signal is as same as the trained signal which the Euclidean Distance between them is the minimum. The performance of the proposed method is validated by experimental data and industrial data.

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“…Then, the earliest extracted OPF that contains the main fault information is selected for further analysis. Lastly, the fault feature could be extracted effectively by performing the spectrum analysis of the instantaneous amplitudes of the selected OPF [30].…”

Section: Application To Rotating Machinery Fault Diagnosismentioning

confidence: 99%

A new rotating machinery fault diagnosis method based on improved local mean decomposition

Zhao

et al. 2015

Digital Signal Processing

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Joint amplitude and frequency demodulation analysis based on local mean decomposition for fault diagnosis of planetary gearboxes

Cited by 106 publications

References 32 publications

Basic research on machinery fault diagnostics: Past, present, and future trends

Basic research on machinery fault diagnostics: Past, present, and future trends

Application of empirical mode decomposition and Euclidean distance technique for feature selection and fault diagnosis of planetary gearbox

A new rotating machinery fault diagnosis method based on improved local mean decomposition

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