Impact fault detection of gearbox based on variational mode decomposition and coupled underdamped stochastic resonance

Li, Jimeng; Wang, Hui; Zhang, Jinfeng; Yao, Xifeng; Zhang, Yungang

doi:10.1016/j.isatra.2019.04.031

Cited by 27 publications

(15 citation statements)

References 43 publications

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“…An et al [41] compared the performance of the VMD, the EMD, and the WT, and proved that the VMD outperforms other methods. Considering the above all, many novel signal processing methods around the VMD have been proposed [42][43][44][45]. Based on the advantages of the VMD in denoising, this paper uses the VMD to eliminate the complex background noise in the experimental signal.…”

Section: •3•mentioning

confidence: 99%

Dynamic characteristics of a gear system with double-teeth spalling fault and its fault feature analysis

Shi

Wen

Pan

et al. 2020

Preprint

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Gear fault diagnosis has been a vital technology to enhance the reliability and reduce the maintenance cost of gear systems. Tooth spalling is one of the most destructive surface failure models of the gear faults. Revealing the dynamic characteristics of a gear system with spalling fault and extracting the fault feature are the premise and basis for effective fault diagnosis. Previous studies have mainly concentrated on the spalling damage on a single gear tooth, but the spalling distributed over double teeth which usually occurs in practical engineering problems is rarely reported. To remedy this deficiency, this paper constructs a new dynamical model of a gear system with double-teeth spalling fault and validates this model with various experimental tests. The dynamic characteristics of gear systems are obtained by considering the excitations induced by the number of spalling teeth, the relative position of two faulty teeth, and the rotational speed. The method based on the Variational Mode Decomposition (VMD) and the Fast Kurtogram (FK) is proposed to extract the features of the double-teeth spalling fault. First, the raw signal is decomposed into a set of Intrinsic Mode Functions (IMFs) by applying the VMD, and the IMFs with strong correlation are summed as a reconstructed signal. The reconstructed signal is then filtered by an optimal band-pass filter based on the FK. Combined with envelope spectrum analysis, the feature extraction ability of the proposed method is compared with that of the original FK method and the method based on the Empirical Mode Decomposition and the FK, respectively. The results indicate that the proposed dynamic model and fault feature extraction method can provide a theoretical basis for spalling defect diagnosis of gear systems.

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Section: •3•mentioning

confidence: 99%

Dynamic characteristics of a gear system with double-teeth spalling fault and its fault feature analysis

Shi

Wen

Pan

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…For these reasons, scholars have proposed many optimization ideas for adaptively selecting K and α [ 21 , 22 ]. The adaptive VMD method leads to a varying number of IMFs, so component screening is usually performed after decomposition [ 23 , 24 ]. The process of screening IMFs requires expert knowledge and is time-consuming and labor-intensive.…”

Section: Introductionmentioning

confidence: 99%

Single-Sensor Engine Multi-Type Fault Detection

Tang

Cheng

et al. 2023

Sensors

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Engine fault detection is conducive to improving equipment reliability and reducing maintenance costs. In practical scenarios, high-quality data is difficult to obtain. Usually, only single-sensor data is available. This paper proposes a fault detection method combining Variational Mode Decomposition (VMD) and Random Forest (RF). At first, the spectral energy distribution is obtained by decomposing and statistic the engine data of multiple working conditions. Based on the spectral energy distribution, the overall optimal mode number was identified, and the quadratic penalty term was optimized using SNR. The improved VMD (IVMD) improves mode aliasing and iterative efficiency and unifies feature dimensions. Decomposition of real signals demonstrates the effectiveness. The paper designs a feature vector composed of seven types of attributes, including unit bandwidth energy, center frequency, maximum singular value and so on. The feature vector is then fed to RF for classification. Features are selected in order of importance to classification to improve the training efficiency. By comparing with various algorithms, the proposed method has higher accuracy and faster training efficiency in single-speed, multi-speed and cross-speed single-sensor data diagnosis. The results show that the method has application prospects with little training data and low hardware requirements.

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“…The TQWT is an effective denoising algorithm, but it is affected by Q factor selection [9][10]. The SR has a good performance in obtaining sensitive fault features, but its system parameters will affect the denoising effect [11][12]. The VMD can eliminate the interference parts from random noise and harmonic components to obtain fault features, but it faces the problem of decomposition modes and balance parameters [13][14].…”

Section: Introductionmentioning

confidence: 99%

Transient impulses enhancement based on adaptive multi-scale improved differential filter and its application in rotating machines fault diagnosis

Guo

Shi

et al. 2022

ISA Transactions

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Impact fault detection of gearbox based on variational mode decomposition and coupled underdamped stochastic resonance

Cited by 27 publications

References 43 publications

Dynamic characteristics of a gear system with double-teeth spalling fault and its fault feature analysis

Dynamic characteristics of a gear system with double-teeth spalling fault and its fault feature analysis

Single-Sensor Engine Multi-Type Fault Detection

Transient impulses enhancement based on adaptive multi-scale improved differential filter and its application in rotating machines fault diagnosis

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