Empirical mode decomposition of acoustic signals for diagnosis of faults in gears and rolling element bearings

Amarnath, M.; Krishna, I. R. Praveen

doi:10.1049/iet-smt.2011.0082

Cited by 82 publications

(52 citation statements)

References 18 publications

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“…It should be noted again that (12) implies that the monocomponents of ( ) satisfy the conditions of (8). Multiplying the complex signal by the mapping function with the phase V ( ), 1 < < − 1, we obtain…”

Section: Signal Decomposition Methodsmentioning

confidence: 99%

“…The EMD can decompose a multicomponent signal into intrinsic mode functions whose amplitude and IF can be demodulated by Hilbert transform. Due to its adaptivity, the EMD has received increasing attentions in the field of signal processing and been applied in a broad domain such as vibration signal analysis, acoustic signal analysis, and geophysical studies [11][12][13]. Similar to EMD, the local mean decomposition (LMD) proposed by Smith decomposes signals into a set of functions, each of which is the product of an amplitude and a pure frequency modulation signal.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Generalized Demodulation and Hilbert Transform Based Signal Decomposition Method

Ren

Wang

et al. 2017

Shock and Vibration

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This paper proposes a new signal decomposition method that aims to decompose a multicomponent signal into monocomponent signal. The main procedure is to extract the components with frequencies higher than a given bisecting frequency by three steps: (1) the generalized demodulation is used to project the components with lower frequencies onto negative frequency domain, (2) the Hilbert transform is performed to eliminate the negative frequency components, and (3) the inverse generalized demodulation is used to obtain the signal which contains components with higher frequencies only. By running the procedure recursively, all monocomponent signals can be extracted efficiently. A comprehensive derivation of the decomposition method is provided. The validity of the proposed method has been demonstrated by extensive numerical analysis. The proposed method is also applied to decompose the dynamic strain signal of a cable-stayed bridge and the echolocation signal of a bat.

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Section: Signal Decomposition Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Generalized Demodulation and Hilbert Transform Based Signal Decomposition Method

Ren

Wang

et al. 2017

Shock and Vibration

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“…Gear faults are mainly constituted of gear surface damage, including gear surface pitting, abrasion, scoring, plastics deformation etc., and wheel tooth break-down. It is widely known that gear fault has direct negative effects on impeding secure reliable operation, reducing production efficiency and causing casualties etc [3]. Therefore, in recent decades, it draws great attention on researches on faults diagnosis on gearbox for its significance.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Gear Based on the Variable Fuzzy Similarity Nearness

Wu¹,

Yao²,

Li³

2015

TOMEJ

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Abstract:In order to improve the validity and accuracy of fault diagnosis on mechanical equipment, this study applies fuzzy nearness approach by adjusting the traditional formulations. Besides, it points out the disadvantages in fault diagnosis of traditional nearness approach based on experimental data of gear transmission. A fuzzy nearness functionbased model is constructed to execute the fault diagnosis with the assistance of fuzzy nearness principles and fuzzy mathematical membership degree. Following that, a experimental data-based numerical example is implemented, which demonstrates that the adjusted fuzzy nearness approach performs better than the traditional one in diagnosing gear fault effectively and meeting the practical requirements of gear transmission fault diagnosis.

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“…The method reaches reliable tooth root crack detection, but it does not face the effect of the speed variation over the proposed fault indicator. In [14], the authors dedicated their effort in the estimation of kurtosis and crest factor of the resulting EMD time-domain acoustic signals to determine the conditions of bearings and gears. It was found that the methodology is very effective, but the interpretation of the severity damage is not obvious.…”

Section: Introductionmentioning

confidence: 99%