Non-stationary vibration feature extraction method based on sparse decomposition and order tracking for gearbox fault diagnosis

Li, Yongzhuo; Ding, Kang; He, Guolin; Jiao, Xintao

doi:10.1016/j.measurement.2018.04.063

Cited by 95 publications

(40 citation statements)

References 19 publications

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“…Order spectrum analysis (OSA) is a useful tool for reducing the impact of speed fluctuation on rotating machinery, and is widely used in the fault diagnosis of PGs [24][25][26][27][28]. The basic element of OSA is the angle-domain synchronous average (ADSA).…”

Section: The Angle-domain Synchronous Average and Order Spectrummentioning

confidence: 99%

Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis

Shen

Shi

Zhen

et al. 2020

Sensors

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The vibration of a planetary gearbox (PG) is complex and mutually modulated, which makes the weak features of incipient fault difficult to detect. To target this problem, a novel method, based on an adaptive order bispectrum slice (AOBS) and the fault characteristics energy ratio (FCER), is proposed. The order bispectrum (OB) method has shown its effectiveness in the feature extraction of bearings and fixed-shaft gearboxes. However, the effectiveness of the PG still needs to be explored. The FCER is developed to sum up the fault information, which is scattered by mutual modulation. In this method, the raw vibration signal is firstly converted to that in the angle domain. Secondly, the characteristic slice of AOBS is extracted. Different from the conventional OB method, the AOBS is extracted by searching for a characteristic carrier frequency adaptively in the sensitive range of signal coupling. Finally, the FCER is summed up and calculated from the fault features that were dispersed in the characteristic slice. Experimental data was processed, using both the AOBS-FCER method, and the method that combines order spectrum analysis with sideband energy ratio (OSA-SER), respectively. Results indicated that the new method is effective in incipient fault feature extraction, compared with the methods of OB and OSA-SER.

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Section: The Angle-domain Synchronous Average and Order Spectrummentioning

confidence: 99%

Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis

Shen

Shi

Zhen

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The vibration signal is highly related to the modal parameter of the mechanical structure that directly contains more information about the system health status of the operating mechanism than electrical signal [20,21]. Besides, based on variable feature extraction and analysis method [22,23], vibration signal has been successfully be utilized in fault diagnosis of rotating machinery like gearbox [24] and combustion engines [25]. These valuable studies provide some guidance for fault diagnosis of switching mechanism like high voltage circuit breakers.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers

et al. 2020

Chin. J. Mech. Eng.

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According to statistic data, machinery faults contribute to largest proportion of High-voltage circuit breaker failures, and traditional maintenance methods exist some disadvantages for that issue. Therefore, based on the wavelet packet decomposition approach and support vector machines, a new diagnosis model is proposed for such fault diagnoses in this study. The vibration eigenvalue extraction is analyzed through wavelet packet decomposition, and a four-layer support vector machine is constituted as a fault classifier. The Gaussian radial basis function is employed as the kernel function for the classifier. The penalty parameter c and kernel parameter δ of the support vector machine are vital for the diagnostic accuracy, and these parameters must be carefully predetermined. Thus, a particle swarm optimizationsupport vector machine model is developed in which the optimal parameters c and δ for the support vector machine in each layer are determined by the particle swarm algorithm. The validity of this fault diagnosis model is determined with a real dataset from the operation experiment. Moreover, comparative investigations of fault diagnosis experiments with a normal support vector machine and a particle swarm optimization back-propagation neural network are also implemented. The results indicate that the proposed fault diagnosis model yields better accuracy and efficiency than these other models.

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“…The analysis methods based on a vibration signal have been diffusely used in the fault diagnosis of mechanical equipment [1,2], because the vibration signal usually contains the main information of the operating state about the equipment [3,4]. However, the observed signals of mechanical equipment are often non-stationary [5,6], and accompanied with multiple fault characteristics at the same time in real engineering [7,8]. Moreover, the coupling of fault features also increases the difficulty of compound faults diagnosis [9,10].…”

Section: Introductionmentioning

confidence: 99%

A Novel Signal Separation Method Based on Improved Sparse Non-Negative Matrix Factorization

Wang

et al. 2019

Entropy

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In order to separate and extract compound fault features of a vibration signal from a single channel, a novel signal separation method is proposed based on improved sparse non-negative matrix factorization (SNMF). In view of the traditional SNMF failure to perform well in the underdetermined blind source separation, a constraint reference vector is introduced in the SNMF algorithm, which can be generated by the pulse method. The square wave sequences are constructed as the constraint reference vector. The output separated signal is constrained by the vector, and the vector will update according to the feedback of the separated signal. The redundancy of the mixture signal will be reduced during the constantly updating of the vector. The time–frequency distribution is firstly applied to capture the local fault features of the vibration signal. Then the high dimension feature matrix of time–frequency distribution is factorized to select local fault features with the improved SNMF method. Meanwhile, the compound fault features can be separated and extracted automatically by using the sparse property of the improved SNMF method. Finally, envelope analysis is used to identify the feature of the output separated signal and realize compound faults diagnosis. The simulation and test results show that the proposed method can effectively solve the separation of compound faults for rotating machinery, which can reduce the dimension and improve the efficiency of algorithm. It is also confirmed that the feature extraction and separation capability of proposed method is superior to the traditional SNMF algorithm.

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Non-stationary vibration feature extraction method based on sparse decomposition and order tracking for gearbox fault diagnosis

Cited by 95 publications

References 19 publications

Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis

Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis

Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers

A Novel Signal Separation Method Based on Improved Sparse Non-Negative Matrix Factorization

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