A mechanical fault detection strategy based on the doubly iterative empirical mode decomposition

Xia, Shiqi; Zhang, Junhui; Ye, Shaogan; Xu, Bing; Xiang, Jiawei; Tang, Hesheng

doi:10.1016/j.apacoust.2019.05.027

Cited by 27 publications

(12 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Figure 1, the specific cavitation damage will lead to internal leakage flows between the adjacent pistons [33]. The size of the cavitation damage is approximately a slender hole.…”

Section: Input Of the Specific Cavitation Damagementioning

confidence: 99%

Modelling and Fault Detection for Specific Cavitation Damage Based on the Discharge Pressure of Axial Piston Pumps

Xia

Xiang

2022

Mathematics

Self Cite

View full text Add to dashboard Cite

Cavitation will increase the leakage and discharge pressure fluctuation of axial piston pumps. In particular, specific cavitation damage may aggravate the pressure impact and performance degradation. The influence of the specific cavitation damage on the discharge pressure is unclear, and the need for fault detection of this damage is urgent. In this paper, we propose a discharge pressure-based model and fault detection methodology for the specific cavitation damage of axial piston pumps. The discharge pressure model with specific damage is constructed using a slender hole. The simulation model is solved through numerical integration. Experimental investigation of cavitation damage detection is carried out. Discharge pressure features in the time domain and frequency domain are compared. The results show that waveform distortions, spectrum energy relocation, generation of new frequencies and sidebands can be used as features for fault detection regarding the specific cavitation damage of axial piston pumps.

show abstract

Section: Input Of the Specific Cavitation Damagementioning

confidence: 99%

Modelling and Fault Detection for Specific Cavitation Damage Based on the Discharge Pressure of Axial Piston Pumps

Xia

Xiang

2022

Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…The S-transform based on wavelet transform and short-time Fourier transform is proposed to eliminate the selection of window function, improve the defect of fixed window width, and maintain direct contact with the phase spectrum and original signal of each frequency component in time–frequency representation [ 19 , 20 , 21 ]. Empirical mode decomposition (EMD) is an adaptive data processing method, which is mainly used for nonlinear, non-stationary time series processing [ 22 ]. The integrated ensemble empirical mode decomposition (EEMD) is proposed to solve the problem of modal aliasing in the EMD.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Rolling Bearings Using Optimized Variational Mode Decomposition and Resonance Demodulation

Zhang

Wang

2020

Entropy

View full text Add to dashboard Cite

It is difficult to extract the fault signal features of locomotive rolling bearings and the accuracy of fault diagnosis is low. In this paper, a novel fault diagnosis method based on the optimized variational mode decomposition (VMD) and resonance demodulation technology, namely GNVRFD, is proposed to realize the fault diagnosis of locomotive rolling bearings. In the proposed GNVRFD method, the genetic algorithm and nonlinear programming are combined to design a novel parameter optimization algorithm to adaptively optimize the two parameters of the VMD. Then the optimized VMD is employed to decompose the collected vibration signal into a series of intrinsic mode functions (IMFs), and the kurtosis value of each IMF is calculated, respectively. According to the principle of maximum value, two most sensitive IMF components are selected to reconstruct the vibration signal. The resonance demodulation technology is used to decompose the reconstructed vibration signal in order to obtain the envelope spectrum, and the fault frequency of locomotive rolling bearings is effectively obtained. Finally, the actual data of rolling bearings is selected to testify the effectiveness of the proposed GNVRFD method. The experiment results demonstrate that the proposed GNVRFD method can more accurately and effectively diagnose the fault of locomotive rolling bearings by comparing with other fault diagnosis methods.

show abstract

A mechanical fault detection strategy based on the doubly iterative empirical mode decomposition

Cited by 27 publications

References 48 publications

Modelling and Fault Detection for Specific Cavitation Damage Based on the Discharge Pressure of Axial Piston Pumps

Modelling and Fault Detection for Specific Cavitation Damage Based on the Discharge Pressure of Axial Piston Pumps

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

Fault Diagnosis for Rolling Bearings Using Optimized Variational Mode Decomposition and Resonance Demodulation

Contact Info

Product

Resources

About