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

• Published in: Journal of quality in maintenance engineering 2024-11, Vol.30 (4), p.565-583
• Main Authors: Wang, Ling, Gao, Jianqiu, Chen, Changjun, Mei, Congli, Gao, Yanfeng
• Format: Article
• Language: English
• Subjects: Algorithms; Back propagation networks; Belief networks; Deep learning; Electric currents; Failure; Fault diagnosis; Machine learning; Methods; Neural networks; Robotics; Robots; Servomotors; Support vector machines; Wavelet transforms
• ISSN: 1355-2511; 1758-7832
• DOI: 10.1108/JQME-08-2023-0080

PurposeHarmonic 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/approachIn 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.FindingsIn 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/valueThe 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.