Deep subclass alignment transfer network based on time–frequency features for intelligent fault diagnosis of planetary gearboxes under time-varying speeds. (1st October 2022)

• Record Type: Journal Article
• Authors: Han, Songjun; Feng, Zhipeng
• Is Part Of: Measurement science & technology. Volume 33:Number 10(2022)
• Publication Date: 2022-10-01
• Subjects: planetary gearbox -- fault diagnosis -- nonstationary condition -- time-frequency representation -- subclass alignment; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac7b14
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Vibration signals of planetary gearboxes have complex components and time-varying characteristics. As the unstable operation of planetary gearboxes leads to unbalanced data distribution within vibration signals, it is difficult to extract gearbox fault information hidden in a large amount of data. Therefore, fault diagnosis of planetary gearboxes under nonstationary conditions is highly challenging. For the past few years, intelligent diagnosis methods have been extensively studied in the fault diagnosis field. However, inappropriate signal representations, inadequate training samples, and data differences increase the difficulty of diagnosing planetary gearbox faults. To address the above issues, this paper proposes an intelligent diagnostic framework based on time–frequency features and a deep residual joint subclass alignment transfer network (DSATN) for planetary gearbox fault diagnosis under nonstationary conditions. One-dimensional vibration signals are converted into time–frequency representation through signal processing techniques to reflect the variation of vibration frequency components within the time–frequency domain with time. During network training, the DSATN evaluates the data distributions between relevant subclasses in source and target tasks by using the local maximum mean discrepancy. Also, it utilizes a nonlinear transformation to align the global data distributions between both tasks, thus improving the generalization of the trained model for small sample sets. The proposed method is validated through planetary gearbox experiments and achieves good fault classification in the time–frequency domain of nonstationary vibration signals. Different gear and planet bearing fault categories are successfully identified.