Fault diagnosis of axial movement for harmonic drive based on deep belief network by using current data of driving servomotor

Purpose Harmonic drives are used widely in aviation, robotics and instrumentation due to their benefits including high transmission ratio, compact structure and zero backlash. One of the common faults of a harmonic drive is the axial movement of the input shaft. In such a case, its input shaft moves in the axial direction relative to the body of the harmonic drive. The purpose of this study is to propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives. Design/methodology/approach In the two proposed fault diagnosis methods, the wavelet threshold algorithm is firstly used for filtering noises of the motor current signal. Then, the feature of the denoised current signal is extracted by the empirical mode decomposition (EMD) method and the wavelet packet energy-entropy (WPEE) theory, respectively, obtaining two kinds of feature sets. After a deep learning model based on the deep belief network (DBN) is constructed and trained by using these feature sets, we finally identify the normal harmonic drives and the ones with the axial movement fault. Findings In contrast to the traditional back propagation (BP) neural network model and support vector machine (SVM) model, the fault diagnosis methods based on the combination of the EMD (as well as the WPEE) and the DBN model can obtain higher accuracy rates of fault diagnosis for axial movement of harmonic drives, which can be greater than or equal to 97% based on the data of the performed experiment. Originality/value The authors propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives, which are verified by the experiment. The presented study may be beneficial for the development of self-diagnosis and self-repair systems of different robots and precision machines using harmonic drives.

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Publication Details

Journal
Journal of Quality in Maintenance Engineering
Published
2024-08-30
DOI
https://doi.org/10.1108/jqme-08-2023-0080
Primary Topic
Advanced Sensor and Control Systems
Type
article
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article

Fault diagnosis of axial movement for harmonic drive based on deep belief network by using current data of driving servomotor

Yanfeng Gao, Jianqiu Gao, Changjun Chen, Congli Mei et al.
Journal of Quality in Maintenance Engineering
Advanced Sensor and Control Systems
article

Fault diagnosis of axial movement for harmonic drive based on deep belief network by using current data of driving servomotor

Yanfeng Gao, Jianqiu Gao, Changjun Chen, Congli Mei, Ling Wang
article en

Abstract

Purpose Harmonic drives are used widely in aviation, robotics and instrumentation due to their benefits including high transmission ratio, compact structure and zero backlash. One of the common faults of a harmonic drive is the axial movement of the input shaft. In such a case, its input shaft moves in the axial direction relative to the body of the harmonic drive. The purpose of this study is to propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives. Design/methodology/approach In the two proposed fault diagnosis methods, the wavelet threshold algorithm is firstly used for filtering noises of the motor current signal. Then, the feature of the denoised current signal is extracted by the empirical mode decomposition (EMD) method and the wavelet packet energy-entropy (WPEE) theory, respectively, obtaining two kinds of feature sets. After a deep learning model based on the deep belief network (DBN) is constructed and trained by using these feature sets, we finally identify the normal harmonic drives and the ones with the axial movement fault. Findings In contrast to the traditional back propagation (BP) neural network model and support vector machine (SVM) model, the fault diagnosis methods based on the combination of the EMD (as well as the WPEE) and the DBN model can obtain higher accuracy rates of fault diagnosis for axial movement of harmonic drives, which can be greater than or equal to 97% based on the data of the performed experiment. Originality/value The authors propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives, which are verified by the experiment. The presented study may be beneficial for the development of self-diagnosis and self-repair systems of different robots and precision machines using harmonic drives.

Journal of Quality in Maintenance EngineeringVol. 30(4)
Second Affiliated Hospital of Zhejiang University (CN), Zhejiang University of Water Resource and Electric Power (CN), China Jiliang University (CN)
Openalex Percentile: Top 85%
Advanced Sensor and Control Systems
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