Coupling Fault Feature Extraction Method Based on Bivariate Empirical Mode Decomposition and Full Spectrum for Rotating Machinery

Jia, Rong; Ma, Fuqi; Wang, Hua; Luo, Xingqi; Ma, Xin

doi:10.1155/2018/4598706

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…The full spectrum [9][10][11] is one of such methods and the plot of which can not only present the interrelation of vibration data from orthogonal sensors but also provide information about the direction of precession. This information is some of the characteristics of the fault of rotating machinery, thereby, the full spectrum shows great potential in the machinery fault diagnosis [12][13][14]. Based on similar principles, Qu et al [15] reasearched the rationale of holospectrum.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional instantaneous orbit map for rotor-bearing system based on a novel multivariate complex variational mode decomposition algorithm

Cui,

Huang,

et al. 2021

Preprint

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Full spectrum and holospectrum are homogenous information fusion technology developed for the fault diagnosis of rotating machinery and are often used in the analysis of the orbit of rotating machinery. However, both of the techniques are based on Fourier transform, so they can only handle stationary signals, which limits their development. By drawing inspiration from the approach of multivariate variational mode decomposition (MVMD) and the complex-valued signal decomposition, we propose a method called multivariate complex variational mode decomposition (MCVMD) for processing non-stationary complex-valued signals of multi-dimensional bearing surfaces in this work. In particular, the proposed method takes the advantages of the joint information between the complex-valued signals of multi-dimensional bearing surfaces, and owing to this property, we provide its three-dimensional instantaneous orbit map (3D-IOM) to present the overall perspective of the rotor-bearing system and also offer a high-resolution time-full spectrum (Time-FS) to display the forward and backward frequency components of all the bearing surfaces within a timefrequency plane. The effectiveness of the proposed method through both the simulated experiment and the real-life complex-valued signals are shown in this paper.

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Section: Introductionmentioning

confidence: 99%

Three-dimensional instantaneous orbit map for rotor-bearing system based on a novel multivariate complex variational mode decomposition algorithm

Cui,

Huang,

et al. 2021

Preprint

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“…The mapping relationship between fault feature and fault mode is established by constructing intelligent diagnosis model. The early machine learning methods include artificial neural network [6], support vector machine, empirical mode decomposition [7], decision tree and so on. After more than 10 years, it has developed into the current deep learning technology, such as stacked supervised auto-encoder [8][9], deep convolutional network [10], deep belief network [11], deep residual network [12], genetic algorithm [13] and so on.…”

Section: Introductionmentioning

confidence: 99%

Analysis of fault coupling vibration and transmission characteristics of multistage gear transmission system

H¹,

Wang²,

Wang³

et al. 2022

J. vibroeng.

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In multi-stage gear transmission system, the accident is often not caused by a single fault, but by coupling fault. The decoupling and identification of coupling faults has always been a hot topic. Through the analysis of frequency characteristics and transmission characteristics, this paper explored the correlation of coupling faults in multistage gear transmission system, and analyzed the coupling mechanism of faults. According to the fault test-bed, a 3D model was built to analyze the contact force and vibration characteristics of gears at all stages. STFT and waterfall diagram were used to analyze the coupling correlation characteristics of experimental signals under two single faults and coupling faults: fixed-axis gear crack and planetary gear wear. The contact force transfer function of all stages of gears to the box was built by using the system identification theory. The source of the fault characteristic frequency component was found through the transfer function, and the transmission characteristic of the fault was revealed. It can be seen from the results that the fault coupling is not the superposition of frequency characteristics. In the coupled signal, the frequency characteristic with the largest amplitude increase is not the fault gear itself, but the intermediate gear connected with them and affected by them. This phenomenon is often misled and misjudged in the analysis.

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“…However, they are not suited for the signal of complex construction. A self-adaptive method for non-stationary signals and nonlinear is called Empirical mode decomposition (EMD) [19], and it has been successfully implemented for; (a) fault diagnosis [20], (b) wind energy [21], (c) flight flutter [22], (d) image processing [23], (e) health monitoring [24], (f) electroencephalogram (EEG) analysis [25], and (g) electrocardiogram (ECG) signals [26]. Moreover, it still flops to disintegrate a signal within the existence of a high trend.…”

Section: Introductionmentioning

confidence: 99%

Health Assessment and Fault Detection System for an Industrial Robot Using the Rotary Encoder Signal

Algburi

Gao

2019

Energies

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In an industrial robot, rotary encoders have been extensively used for dynamic control and positioning. This study shows that the encoder signal, after appropriate processing, can also be efficiently utilized for the health observation of energy performance of industrial robots system. Singular spectrum analysis (SSA) and Hilbert transform (HT) is proposed in this work, for detecting weak position oscillations to estimate the instantaneous amplitudes (IA) and the instantaneous frequencies (IF) of an industrial robot based on the encoder signal. Compared with empirical mode decomposition (EMD) and HT, the singular spectrum analysis and Hilbert transform (SSAHT) outperforms empirical mode decomposition Hilbert transform (EMDHT) in terms of ability and precision to determine source noise, and it can accurately catch the weak oscillations without signal deformation in both position and speed introduced via mechanical flaws. Combined with SSA, the IA and IF of both oscillations and residual are extracted by HT. They are obtained from the robot arm movement. These features play an important role in improving the performance detecting weak oscillations and the residual, essential information to evaluate the health conditions and fault detection to serve the energy performance for the industrial robot. The efficiency of the proposed system has been verified both numerical simulation and experimental data. The outcomes prove that the proposed SSAHT can detect flaw indications and additionally, it can also identify faulty components. Thus, the study presents a promising tool for the health monitoring of an industrial robot instead of the vibration-based monitoring scheme.

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Coupling Fault Feature Extraction Method Based on Bivariate Empirical Mode Decomposition and Full Spectrum for Rotating Machinery

Cited by 8 publications

References 34 publications

Three-dimensional instantaneous orbit map for rotor-bearing system based on a novel multivariate complex variational mode decomposition algorithm

Three-dimensional instantaneous orbit map for rotor-bearing system based on a novel multivariate complex variational mode decomposition algorithm

Analysis of fault coupling vibration and transmission characteristics of multistage gear transmission system

Health Assessment and Fault Detection System for an Industrial Robot Using the Rotary Encoder Signal

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