Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: A review

Li, Zhixiong; Yu, Jiang; Hu, Chao; Peng, Zhongxiao

doi:10.1016/j.measurement.2016.04.036

Cited by 122 publications

(65 citation statements)

References 86 publications

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“…The early detection of faults prevents financial loss and downtimes. In the literature researchers developed methods of fault detection based on acoustic [1][2][3][4][5][6][7][8][9][10][11][12][13], thermal [14][15][16][17][18][19][20] and vibration signals [21][22][23][24][25][26][27][28][29]. An acoustic signal is difficult to process because microphone records many sounds from environment.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

Głowacz¹,

Głowacz²,

Głowacz³

et al. 2017

Archives of Metallurgy and Materials

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A degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction, high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in conveyor belt. The diagnostics of such motors is essential. An early detection of faults prevents financial loss and downtimes. The authors proposed a technique of fault diagnosis based on recognition of currents. The authors analyzed 4 states of three phase induction motor: healthy three phase induction motor, three phase induction motor with 1 faulty rotor bar, three phase induction motor with 2 faulty rotor bars, three phase induction motor with faulty ring of squirrel-cage. An analysis was carried out for original method of feature extraction called MSAF-RATIO15 (Method of Selection of Amplitudes of Frequencies -Ratio 15% of maximum of amplitude). A classification of feature vectors was performed by Bayes classifier, Linear Discriminant Analysis (LDA) and Nearest Neighbour classifier. The proposed technique of fault diagnosis can be used for protection of three phase induction motors and other rotating electrical machines. In the near future the authors will analyze other motors and faults. There is also idea to use thermal, acoustic, electrical, vibration signal together.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

Głowacz¹,

Głowacz²,

Głowacz³

et al. 2017

Archives of Metallurgy and Materials

View full text Add to dashboard Cite

show abstract

“…The most common signal processing methods include time-domain analysis and time-frequency analysis [5] such as amplitude spectrum analysis, order analysis [6], cepstrum analysis [7,8], envelope spectrum analysis [6], Hilbert transform demodulation analysis [9], wavelet analysis, autocorrelation analysis [10], and empirical mode decomposition (EMD) [11]. Among the available vibration analysis methods, EMD is an effective signal analysis method which is suited for dealing with nonlinear and nonstationary signals [12].…”

Section: Introductionmentioning

confidence: 99%

Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

Chen

Zhou

Fang

et al. 2017

International Journal of Rotating Machinery

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A gear transmission system is a complex nonstationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and Deep Briefs Network (DBN) is proposed to treat the vibration signals measured from gearbox. The original data is decomposed into a set of intrinsic mode functions (IMFs) using EEMD, and then main IMFs were chosen for reconstructed signal to suppress abnormal interference from noise. The reconstructed signals were regarded as input of DBN to identify gearbox working states and fault types. To verify the effectiveness of the EEMD-DBN in detecting the faults, a series of gear fault simulate experiments at different states were carried out. Results showed that the proposed method which coupled EEMD and DBN can improve the accuracy of gear fault identification and it is capable of applying to fault diagnosis in practical application.

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“…Practically, the vibration signal is generally nonstationary and nonperiodic when the fault occurs, so the traditional FFT cannot meet the needs of the time-varying and nonstationary signals detection and time-frequency analysis [2].…”

Section: Introductionmentioning

confidence: 99%

Study on Frequency Characteristics of Rotor Systems for Fault Detection Using Variational Mode Decomposition

Chen

Zhou

Fang

et al. 2017

International Journal of Rotating Machinery

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Due to the complicated structure, vibration signal of rotating machinery is multicomponent with nonstationary and nonlinear features, so it is difficult to diagnose faults effectively. Therefore, effective extraction of vibration signal characteristics is the key to diagnose the faults of rotating machinery. Mode mixing and illusive components existed in some conventional methods, such as EMD and EEMD, which leads to misdiagnosis in extracting signals. Given these reasons, a new fault diagnosis method, namely, variation mode decomposition (VMD), was proposed in this paper. VMD is a newly developed technique for adaptive signal decomposition, which can decompose a multicomponent signal into a series of quasi-orthogonal intrinsic mode functions (IMFs) simultaneously, corresponding to the components of signal clearly. To further research on VMD method, the advantages and characteristics of VMD are investigated via numerical simulations. VMD is then applied to detect oil whirl and oil whip for rotor systems fault diagnosis via practical vibration signal. The experimental results demonstrate the effectiveness of VMD method.

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Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: A review

Cited by 122 publications

References 86 publications

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

Study on Frequency Characteristics of Rotor Systems for Fault Detection Using Variational Mode Decomposition

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