Study on planetary gear fault diagnosis based on variational mode decomposition and deep neural networks

Li, Yong; Cheng, Gang; Liu, Chang; Chen, Xihui

doi:10.1016/j.measurement.2018.08.002

Cited by 104 publications

(58 citation statements)

References 26 publications

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“…When gears break down, the faulty component often causes an impact, which generates impact energy. Types of gear fault include missing tooth, broken tooth, chipping tip, root crack, and spalling conditions [36][37][38]. However, in the case of a broken tooth, the gearbox needs to change to a new gear.…”

Section: Verification and Discussion Of The Proposed Methodsmentioning

confidence: 99%

Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

Wang

Lee

2019

Applied Sciences

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Fault characteristic extraction is attracting a great deal of attention from researchers for the fault diagnosis of rotating machinery. Generally, when a gearbox is damaged, accurate identification of the side-band features can be used to detect the condition of the machinery equipment to reduce financial losses. However, the side-band feature of damaged gears that are constantly disturbed by strong jamming is embedded in the background noise. In this paper, a hybrid signal-processing method is proposed based on a spectral subtraction (SS) denoising algorithm combined with an empirical wavelet transform (EWT) to extract the side-band feature of gear faults. Firstly, SS is used to estimate the real-time noise information, which is used to enhance the fault signal of the helical gearbox from a vibration signal with strong noise disturbance. The empirical wavelet transform can extract amplitude-modulated/frequency-modulated (AM-FM) components of a signal using different filter bands that are designed in accordance with the signal properties. The fault signal is obtained by building a flexible gear for a helical gearbox with ADAMS software. The experiment shows the feasibility and availability of the multi-body dynamics model. The spectral subtraction-based adaptive empirical wavelet transform (SS-AEWT) method was applied to estimate the gear side-band feature for different tooth breakages and the strong background noise. The verification results show that the proposed method gives a clearer indication of gear fault characteristics with different tooth breakages and the different signal-noise ratio (SNR) than the conventional EMD and LMD methods. Finally, the fault characteristic frequency of a damaged gear suggests that the proposed SS-AEWT method can accurately and reliably diagnose faults of a gearbox.

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Section: Verification and Discussion Of The Proposed Methodsmentioning

confidence: 99%

Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

Wang

Lee

2019

Applied Sciences

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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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“…The Teager-Kaiser energy operator can separate the instantaneous amplitude and instantaneous frequency of a single-component AM-FM signal, that is [66,67]:…”

Section: Of 22mentioning

confidence: 99%

“…The test bench can simulate typical faults such as broken teeth, tooth surface wear, pitting and missing teeth of key parts such as planetary gears, sun gears, ring gears, etc., and adopt a three-way acceleration sensor to collect x, y, and z in the state of broken gears. The acceleration signal is in three directions, the sensor arrangement is shown in Figure 3, the sampling frequency is 12,800 Hz, and the sampling time is 10 s [67][68][69]. The broken tooth fault is set at the second stage solar gear of the planetary gearbox, and the input frequency of the servo motor is 40 Hz.…”

Section: Introduction To the Experimentsmentioning

confidence: 99%

A New Method of Two-stage Planetary Gearbox Fault Detection Based on Multi-Sensor Information Fusion

Zhang

Cheng

et al. 2019

Applied Sciences

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Due to their high transmission ratio, high load carrying capacity and small size, planetary gears are widely used in the transmission systems of wind turbines. The planetary gearbox is the core of the transmission system of a wind turbine, but because of its special structure and complex internal and external excitation, the vibration signal spectrum shows strong nonlinearity, asymmetry and time variation, which brings great trouble to planetary gear fault diagnosis. The traditional time-frequency analysis technology is insufficient in the condition monitoring and fault diagnosis of wind turbines. For this reason, we propose a new method of planetary gearbox fault diagnosis based on Compressive sensing, Two-dimensional variational mode decomposition (2D-VMD) and full-vector spectrum technology. Firstly, the nonlinear reconstruction and noise reduction of the signal is carried out by using compressed sensing, and then the signal with multiple degrees of freedom is adaptively decomposed into multiple sets of characteristic scale components by using 2D-VMD. Then, Rényi entropy is used as the optimization index of 2D-VMD analysis performance to extract the effective target intrinsic mode function (IMF) component, reconstruct the dynamics signal in the planetary gearbox, and improve the signal-to-noise ratio. Then, using the full-vector spectrum technique, the homologous information collected by numerous sensors is data layer fused in the spatial domain and the time domain to increase the comprehensiveness and certainty of the fault information. Finally, the Teager–Kaiser energy operator is used to demodulate the potential low-frequency dynamics frequency characteristics from the high-frequency domain and detect the fault characteristic frequency. Furthermore, the correctness and validity of the method are verified by the fault test signal of the planetary gearbox.

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Study on planetary gear fault diagnosis based on variational mode decomposition and deep neural networks

Cited by 104 publications

References 26 publications

Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

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

A New Method of Two-stage Planetary Gearbox Fault Detection Based on Multi-Sensor Information Fusion

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