Fault Separation and Detection for Compound Bearing-Gear fault Condition Based on Decomposition of Marginal Hilbert Spectrum

Abstract

For the rotating machinery system, it is a challenge to explore fault detection and diagnosis for multiple-faults condition, which simultaneously contains faulty bearing components and faulty gear components. In the study, a fault feature separation and extraction approach is proposed for the bearing-gear fault condition through combining empirical mode decomposition (EMD), Hilbert transform (HT), principal component analysis (PCA), independent component analysis (ICA) techniques. Firstly, EMD is implemented to decompose the single sensor signal to obtain multiple sub-band signals termed as the intrinsic mode functions (IMFs). Secondly, the most relevant IMFs to bearing and gear fault features are selected to construct multiple-channels model with the help of the simulated bearing and gear fault signals. Thirdly, HT is utilized to compute marginal Hilbert spectrum (MHS) for each IMF in multiple-channels model, to construct an MHS matrix. Finally, some statistically independent components are obtained by decomposing the MHS matrix with PCA and ICA, and multiple fault features are identified from these components. The experimental application of the proposed method is put into a bearing-belt-gearbox union machinery system to evaluate its validity. The experimental analysis results indicate that the proposed method is effective to separate and extract a bearing fault and a gear fault for two types of compound bearing-gear fault conditions. INDEX TERMS Empirical mode decomposition (EMD), Hilbert transform (HT), independent component analysis (ICA), marginal Hilbert spectrum (MHS), multiple faults detection. XUELI ZHU received the B.Sc. degree in construction industry enterprise automation from Construction Industry Enterprise Automation, in 1982, and the Ph.D. degree in heating, gasing, ventilation, and air conditioning engineering from the

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“…Signal decomposition-based methods are similar to pattern recognition that relied on feature engineering, in which the different components are expected to be separated. Scholars and researchers have proposed lots of successful methods for compound fault diagnosis, such as Wavelet Transform (WT) [20][21][22][23][24][25][26][27][28], Variational Mode Decomposition (VMD) [29][30][31][32][33][34], Local Mean Decomposition (LMD) [35], Singular Spectrum Decomposition (SSD) [36,37], Symplectic Geometry Mode Decomposition (SGMD) [38,39], and other methods [40][41][42][43][44][45][46][47][48]. first, the compound fault signals are separated into different empirical models by empirical WT; second, a duffing oscillator which incorporates all single fault frequency is used to establish the fault isolator; finally, all the single faults can be recognized one by one by observing the chaotic motion from the Poincar mapping of the fault isolator outputs [20].…”

“…For instance, Tang et al proposed a compound fault detection method with virtual multichannel signals in the angel domain and applied it to monitoring the rolling bearings under varying working conditions [43]. More details can be found in [40][41][42][43][44][45][46][47][48], which are not enumerated here. and Cyclostationary Blind Deconvolution (CYCBD) [63], can enhance weak periodic features and suppress signal noise by constructing a comb filter, thus, have been proven to be an effective tool for separating compound fault with weak components.…”

Coal-Gangue Interface Detection Based on Ensemble Empirical Mode Decomposition Energy Entropy

“…Its key idea is to decompose the complex signal into several IMFs, which contain different time scales reflecting the local physical characteristics of signals. However, the mode mixing of EMD will make IMF lose its physical meaning [29]- [31]. Huang considers that mode mixing is an intermittent phenomenon and is related to the selection of extreme points during decomposition.…”

Fault separation and detection algorithm based on Mason Young Tracy decomposition and Gaussian mixture models

“…These data are stored in the database, but they have not been applied so much that the traditional industry often has the phenomenon of “rich data and lack of information” (Diren et al , 2019). With the operation of large power grids, these data gradually become big data, there is a wide correlation between big data, and it is worthwhile to excavate the intrinsic characteristics of data through data mining, so as to provide evidence for fault detection and separation (Li et al , 2019). The fault classification of large-scale power grid system based on data-driven method is to excavate the fault features hidden in the big data generated during the operation of the power grid.…”