Transient signal analysis based on Levenberg–Marquardt method for fault feature extraction of rotating machines

Wang, Shibin; Cai, Guoyin; Zhu, Zhongkui; Huang, Weiguo; Zhang, Xingwu

doi:10.1016/j.ymssp.2014.09.010

Cited by 60 publications

(24 citation statements)

References 58 publications

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“…He and Ding [134] proposed a local time-frequency template matching method for bearing transient feature extraction. Wang et al [135,136] used the sparse representation method with wavelet dictionary for extracting the transient feature in a faulty gearbox, in which wavelet was selected by correlation filtering. A comparison study demonstrated that the proposed sparse representation method outperformed the EMD in transient feature extraction [137].…”

Section: Sparse Decomposition Analysismentioning

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: Sparse Decomposition Analysismentioning

confidence: 99%

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

et al. 2017

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“…Ding et al combined state and least squares parameter estimation algorithms for dynamic systems . Levenberg‐Marquardt method was put forward to analyze transient signal for fault feature extraction of rotating machines . Authors considered distributed estimation over sensor networks based on distributed conjugate gradient strategies .…”

Section: Introductionmentioning

confidence: 99%

Efficient and accurate spectral method for the time‐fractional dual‐phase‐lag heat transfer model and its parameter estimation

Zheng

Jiang

Zhang

2019

Math Methods in App Sciences

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In the current paper, a heat transfer model is suggested based on a time‐fractional dual‐phase‐lag (DPL) model. We discuss the model in two parts, the direct problem and the inverse problem. Firstly, for solving it, the finite difference/Legendre spectral method is constructed. In the temporal direction, we employ the weighted and shifted Grünwald approximation, which can achieve second order convergence. In the spatial direction, the Legendre spectral method is used, it can obtain spectral accuracy. The stability and convergence are theoretically analyzed. For the inverse problem, the Bayesian method is used to construct an algorithm to estimate the four parameters for the model, namely, the time‐fractional order α, the time‐fractional order β, the delay time τT, and the relaxation time τq. Next, numerical experiments are provided to demonstrate the effectiveness of our scheme, with the values of τq and τT for processed meat employed. We also make a comparison with another method. The data obtained for the direct problem are used in the parameter estimation. The paper provides an accurate and efficient numerical method for the time‐fractional DPL model.

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“…Its running state directly affects the machinery performance. It was reported that bearing faults accommodate 45%-55% of motor failures [1,2]. Thus, the diagnosis of bearing faults plays a key role in the reliable operation of motors.…”

Section: Introductionmentioning

confidence: 99%

A Fault Feature Extraction Method for Motor Bearing and Transmission Analysis

2017

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Abstract:Roller bearings are the most widely used and easily damaged mechanical parts in rotating machinery. Their running state directly affects rotating machinery performance. Empirical mode decomposition (EMD) easily occurs illusive component and mode mixing problem. From the view of feature extraction, a new feature extraction method based on integrating ensemble empirical mode decomposition (EEMD), the correlation coefficient method, and Hilbert transform is proposed to extract fault features and identify fault states for motor bearings in this paper. In the proposed feature extraction method, the EEMD is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs) with different frequency components. Then the correlation coefficient method is used to select the IMF components with the largest correlation coefficient, which are carried out with the Hilbert transform. The obtained corresponding envelope spectra are analyzed to extract the fault feature frequency and identify the fault state by comparing with the theoretical value. Finally, the fault signal transmission performance of vibration signals of the bearing inner ring and outer ring at the drive end and fan end are deeply studied. The experimental results show that the proposed feature extraction method can effectively eliminate the influence of the mode mixing and extract the fault feature frequency, and the energy of the vibration signal in the bearing outer ring at the fan end is lost during the transmission of the vibration signal. It is an effective method to extract the fault feature of the bearing from the noise with interference.

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Transient signal analysis based on Levenberg–Marquardt method for fault feature extraction of rotating machines

Cited by 60 publications

References 58 publications

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

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

Efficient and accurate spectral method for the time‐fractional dual‐phase‐lag heat transfer model and its parameter estimation

A Fault Feature Extraction Method for Motor Bearing and Transmission Analysis

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