Undecimated Multiwavelet and Hilbert-Huang Time-frequency Analysis and Its Application in the Incipient Fault Diagnosis of Planetary Gearboxes

Sun, Hailiang

doi:10.3901/jme.2013.03.056

Cited by 6 publications

(3 citation statements)

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“…As the transmission component widely used in various machines, the gearbox plays a critical role in mechanical systems, and thus, gearbox fault diagnosis based on vibration signals attracts considerable research interests [1]. In addition to deterministic approaches such as statistical features [2,3] and time-frequency analysis [4,5], many analysis tools have been used, including decision tree [6], frequency demodulation [7,8], and stochastic resonance [9,10]. However, vibration analysis still has inherent limitations in the diagnosis of complex gear system, in which the vibration signal is easily contaminated by a large amount of interference, and the strength of the signal depends on the position of the vibration transducer.…”

Section: Introductionmentioning

confidence: 99%

Application of Instantaneous Rotational Speed to Detect Gearbox Faults Based on Double Encoders

Liu

Kong

et al. 2019

Chin. J. Mech. Eng.

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Considerable studies have been carried out on fault diagnosis of gears, with most of them concentrated on conventional vibration analysis. However, besides the complexity of gear dynamics, the diagnosis results in terms of vibration signal are easily misjudged owing to the interference of sensor position or other components. In this paper, an alternative gearbox fault detection method based on the instantaneous rotational speed is proposed because of its advantages over vibration analysis. Depending on the timer/counter-based method for the pulse signal of the optical encoder, the varying rotational speed can be obtained effectively. Owing to the coupling and meshing of gears in transmission, the excitations are the same for the instantaneous rotational speed of the input and output shafts. Thus, the differential signal of instantaneous rotational speeds can be adopted to eliminate the effect of the interference excitations and extract the associated feature of the localized fault effectively. With the experiments on multistage gearbox test system, the differential signal of instantaneous speeds is compared with other signals. It is proved that localized faults in the gearbox generate small angular speed fluctuations, which are measurable with an optical encoder. Using the differential signal of instantaneous speeds, the fault characteristics are extracted in the spectrum where the deterministic frequency component and its harmonics corresponding to crack fault characteristics are displayed clearly.

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Section: Introductionmentioning

confidence: 99%

Application of Instantaneous Rotational Speed to Detect Gearbox Faults Based on Double Encoders

Liu

Kong

et al. 2019

Chin. J. Mech. Eng.

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“…Especially for incipient fault, the fault features are too weak to be extracted [5]. Time-frequency analysis techniques, such as Wigner-Ville distribution [6], wavelet transform [7], and EMD [8], are effective in suppressing signal noise. In Wigner-Ville distribution, cross-term interferences among multiple component signals impair fault feature because of quadratic form of Wigner-Ville distribution.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Reducer via Improved LMD and SVM‐RFE‐MRMR

Xiao-guang

Song

et al. 2018

Shock and Vibration

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The vibration signals are usually characterized by nonstationary, nonlinearity, and high frequency shocks, and the redundant features degrade the performance of fault diagnosis methods. To deal with the problem, a novel fault diagnosis approach for rotating machinery is presented by combining improved local mean decomposition (LMD) with support vector machine–recursive feature elimination with minimum redundancy maximum relevance (SVM-RFE-MRMR). Firstly, an improved LMD method is developed to decompose vibration signals into a subset of amplitude modulation/frequency modulation (AM-FM) product functions (PFs). Then, time and frequency domain features are extracted from the selected PFs, and the complicated faults can be thus identified efficiently. Due to degradation of fault diagnosis methods resulting from redundant features, a novel feature selection method combining SVM-RFE with MRMR is proposed to select salient features, improving the performance of fault diagnosis approach. Experimental results on reducer platform demonstrate that the proposed method is capable of revealing the relations between the features and faults and providing insights into fault mechanism.

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“…In [12,13], multiwavelet lifting scheme was improved for compound faults separation and extraction. In [14], the undecimated multiwavelet was proposed for fault diagnosis of planetary gearboxes.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Mechanical Fault Diagnosis Based on Multiwavelet Adaptive Threshold Denoising and MPSO

Sun

Wang

et al. 2014

Mathematical Problems in Engineering

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The condition diagnosis of rotating machinery depends largely on the feature analysis of vibration signals measured for the condition diagnosis. However, the signals measured from rotating machinery usually are nonstationary and nonlinear and contain noise. The useful fault features are hidden in the heavy background noise. In this paper, a novel fault diagnosis method for rotating machinery based on multiwavelet adaptive threshold denoising and mutation particle swarm optimization (MPSO) is proposed. Geronimo, Hardin, and Massopust (GHM) multiwavelet is employed for extracting weak fault features under background noise, and the method of adaptively selecting appropriate threshold for multiwavelet with energy ratio of multiwavelet coefficient is presented. The six nondimensional symptom parameters (SPs) in the frequency domain are defined to reflect the features of the vibration signals measured in each state. Detection index (DI) using statistical theory has been also defined to evaluate the sensitiveness of SP for condition diagnosis. MPSO algorithm with adaptive inertia weight adjustment and particle mutation is proposed for condition identification. MPSO algorithm effectively solves local optimum and premature convergence problems of conventional particle swarm optimization (PSO) algorithm. It can provide a more accurate estimate on fault diagnosis. Practical examples of fault diagnosis for rolling element bearings are given to verify the effectiveness of the proposed method.

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Undecimated Multiwavelet and Hilbert-Huang Time-frequency Analysis and Its Application in the Incipient Fault Diagnosis of Planetary Gearboxes

Cited by 6 publications

References 0 publications

Application of Instantaneous Rotational Speed to Detect Gearbox Faults Based on Double Encoders

Application of Instantaneous Rotational Speed to Detect Gearbox Faults Based on Double Encoders

Fault Diagnosis for Reducer via Improved LMD and SVM‐RFE‐MRMR

Intelligent Mechanical Fault Diagnosis Based on Multiwavelet Adaptive Threshold Denoising and MPSO

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