Application of bispectrum analysis to detect faults in helical geared system

Gunasegaran, V.; Amarnath, M.; Chelladurai, H.; Krishna, I. R. Praveen

doi:10.1016/j.matpr.2021.04.177

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…The accuracy of signal characteristics is very important because it directly affects the result of the later identification of abnormal noise patterns and the progress of the study of the failure mechanism. Many methods have been applied to feature extraction of signals, such as wavelet transform (WT) [7][8][9][10], empirical mode decomposition (EMD) [11][12][13][14][15][16], local mean decomposition (LMD) [17][18][19][20][21], Ensemble Empirical Mode Decomposition (EEMD) [22], and Bispectrum analysis (BA) [23][24][25][26][27]. In 2016, J.Lin et al [28] presents a framework to analyze and simulate nonhomogeneous non-Gaussian corrosion fields on the external surface of buried in-service pipelines by using continuous and discrete wavelet transforms.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Noise Identification of Engines Based on Wavelet Transform and Bispectrum Analysis

Yang¹,

Zhang²,

Li³

2023

Preprint

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Abnormal noise is the most prominent problem for motorcycle, which affect consumers' purchasing desire and driving experience. The abnormal noise engines are always detected by manual auscultation(MA) after assembly. While MA is also affected by subjective and objective factors, its accuracy fluctuates greatly and has a large labor intensity. The most important is that it can not be applied in a corporation with mass production and high quality. To overcome those problems, this paper proposed an online engine abnormal noise detection method based on wavelet transform(WT) and bispectrum analysis(BA), which improves the accuracy and stability of engine abnormal noise identification and reduces the cost of the check. Firstly, the engine's acoustic signal is acquired by using a free-field microphone. Secondly, eliminated the background noise of signals by using the wavelet correlation coefficient(WCC) theory and extracted the signal features by applying power spectrum estimation and bispectrum analysis respectively. Thirdly, feature vectors are normalized before being used as support vector machine(SVM) samples. Fourthly, the appropriate kernel function and parameters are selected to train the vector machine using the training sample. Finally, testing samples are used to inspect the accuracy of vector machines. The result shows that the training accuracy and testing accuracy of the samples is high by using the method of WT-BA-SVM (wavelet transform - bispectrum analysis - support vector machines). WT-BA-SVM identified engine fault types effectively and provided the theoretical foundation for the establishment of an engine abnormal noise online detection system.

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Section: Introductionmentioning

confidence: 99%

Abnormal Noise Identification of Engines Based on Wavelet Transform and Bispectrum Analysis

Yang¹,

Zhang²,

Li³

2023

Preprint

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Experimental and Theoretical Analysis of Bispectrum Characteristics of Phase Coupled Signals

Wu,

Yuan

2024

J CIRCUIT SYST COMP

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Fourier transform points out that a certain function that meets certain conditions can be decomposed into a linear combination of trigonometric functions (sine or cosine functions). The functions of high-order cumulant include suppressing the Gaussian noise, eliminating independent signal components, and identifying the phase coupling phenomenon of the signal. To prove this hypothesis, this study constructs a cosine signal with the phase coupling phenomenon based on Fourier transform theory which substitutes it into the third-order cumulant expression and performs detailed reasoning. The constructed signal is extended to the complex signal domain and the same conclusion is obtained. The number of coupled signals is expanded from three to a higher value. The results of the study give definite mathematical and physical meaning to the bispectral peaks. The collected mechanical vibration signals are given to demonstrate this conclusion. The demonstrated characteristics of high-order cumulants have made them widely used in many fields.

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Application of bispectrum analysis to detect faults in helical geared system

Cited by 2 publications

References 19 publications

Abnormal Noise Identification of Engines Based on Wavelet Transform and Bispectrum Analysis

Abnormal Noise Identification of Engines Based on Wavelet Transform and Bispectrum Analysis

Experimental and Theoretical Analysis of Bispectrum Characteristics of Phase Coupled Signals

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