Gearbox compound fault diagnosis based on a combined MSGMD–MOMEDA method

Zhang, Jianqun; Zhang, Qing; Qin, Xianrong; Sun, Yaqi; Zhang, Jun

doi:10.1088/1361-6501/ac456b

Cited by 18 publications

(16 citation statements)

References 33 publications

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“…3 Gear faults as well as rolling bearing faults are not found in time, which would cause an abnormal shutdown or even damage the equipment. 1,5 Gearbox fault diagnosis is of great significance for avoiding accidents and ensuring that it works safely. 2,5 Mechanical fault diagnosis methods based on the deep learning model have developed rapidly in the last few years.…”

Section: Introductionmentioning

confidence: 99%

“…1,5 Gearbox fault diagnosis is of great significance for avoiding accidents and ensuring that it works safely. 2,5 Mechanical fault diagnosis methods based on the deep learning model have developed rapidly in the last few years. 6 Because of the strong feature learning ability, the deep learning model can extract features adaptively for mechanical fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…1,5 Gearbox fault diagnosis is of great significance for avoiding accidents and ensuring that it works safely. 2,5…”

Section: Introductionmentioning

confidence: 99%

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Gearbox fault diagnosis based on frequency-domain Gramian angular difference field and deep convolutional neural network

Zhang

Feng

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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Gearbox fault diagnosis is of great significance to avoid accidents and reduce operation and maintenance costs for machinery such as the quayside container crane. In recent years, mechanical fault diagnosis methods based on deep learning have developed rapidly. How to perform diagnosis simply based on deep learning in the case of limited labeled samples is still a challenging problem. To address this issue, this paper proposes a new fault diagnosis method combining the frequency-domain Gramian angular difference field (FDGADF) and deep convolutional neural network (DCNN), namely, FDGADF-DCNN. To explore the availability of the FDGADF-DCNN method, two experiment datasets are studied. When the proportion of labeled samples is 70%, the recognition accuracy of both datasets exceeds 98%. When only 20% of the labeled samples are available (the average number of training samples in each category is only dozens), FDGADF-DCNN can achieve a recognition accuracy of no less than 95% in two datasets, in which a benchmark dataset is 100% and the dataset collected from a scaled-down test bench is 95%. Compared with other 11 methods, the FDGADF-DCNN method achieves the highest recognition accuracy. These results indicate that the FDGADF-DCNN method is expected to be used for gearbox fault diagnosis under limited labeled samples.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gearbox fault diagnosis based on frequency-domain Gramian angular difference field and deep convolutional neural network

Zhang

Feng

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…As essential transmission components in rotating machinery, rolling bearings and gears are susceptible to various faults under harsh conditions [1]. The faults of such components can easily trigger abnormal equipment shutdowns and even catastrophic accidents [2][3][4][5][6]. Therefore, it is necessary to * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

“…Elwany and Gebraeel [31] used the average amplitude of the bearing outer race fault frequency and its harmonic as a feature to track the changes in the spectrum from weak fault to strong fault. Due to its ability to demodulate fault signals, the envelope spectrum is commonly used to reveal fault characteristics and focus on fault components [3,32]. The fault frequency amplitude in the envelope spectrum can also be considered as a feature extraction indicator.…”

Section: Introductionmentioning

confidence: 99%

A novel performance degradation assessment method for rotating machinery based on the fault information and the dynamic simulation

Zhang,

Qin

et al. 2024

Meas. Sci. Technol.

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The performance degradation assessment (PDA) of key components such as gears and rolling bearings is the core technology of prognostics and health management for rotating machinery. Most existing PDA methods may have two deficiencies: (1) the assessment indicator constructed does not consider capturing fault characteristics directly; (2) feature selection is generally based on the measured data of different fault levels, which is difficult to obtain in actual processes; moreover, the selection results lack universality and are difficult to extend to other equipment. To address these issues, this paper proposes a novel PDA method based on fault information and dynamic simulation. First, anomaly detection is performed using four well-known indicators in combination with Mahalanobis distance. Secondly, fault identification is performed using envelope spectrum analysis on anomaly signals to determine the fault type, e.g., gear fault and outer race fault. Thirdly, based on the fault type information, the candidate feature set including fault-domain indicators is selected based on the established dynamic simulation signals to obtain a preliminary assessment vector for the first stage. The stability of the fault domain indicators which capture fault characteristics directly is tested through actual measured normal data. It is used as the second stage of selecting to obtain the assessment vector. Finally, the PDA indicator is calculated based on the assessment vector and Mahalanobis distance. Four experiment case studies demonstrate the proposed PDA method can effectively isolate faults with different defect sizes as well as track the whole performance degradation. The above analysis indicates that the proposed PDA method is expected to be used for the actual rotating machinery.

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Adaptive feature mode decomposition: a fault-oriented vibration signal decomposition method for identification of multiple localized faults in rotating machinery

Zhou

et al. 2023

Nonlinear Dyn

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Gearbox compound fault diagnosis based on a combined MSGMD–MOMEDA method

Cited by 18 publications

References 33 publications

Gearbox fault diagnosis based on frequency-domain Gramian angular difference field and deep convolutional neural network

Gearbox fault diagnosis based on frequency-domain Gramian angular difference field and deep convolutional neural network

A novel performance degradation assessment method for rotating machinery based on the fault information and the dynamic simulation

Adaptive feature mode decomposition: a fault-oriented vibration signal decomposition method for identification of multiple localized faults in rotating machinery

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