A planetary gearbox fault diagnosis method based on time-series imaging feature fusion and a transformer model. (1st February 2023)

• Record Type: Journal Article
• Authors: Wu, Rui; Liu, Chao; Han, Te; Yao, Jiachi; Jiang, Dongxiang
• Is Part Of: Measurement science & technology. Volume 34:Number 2(2023)
• Publication Date: 2023-02-01
• Subjects: transformer -- feature fusion -- fault diagnosis -- deep learning -- planetary gearbox; 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/ac9e6c
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: As a crucial component in the transmission system, a planetary gearbox has a relatively complicated structure and usually operates under complex working conditions and a severe noisy environment, making it challenging to achieve precise and efficient fault diagnosis. Along with the development of artificial intelligence techniques, end-to-end fault diagnosis frameworks have been widely studied, among which convolutional and recurrent neural networks are the mainstream backbone networks. However, these networks have shortcomings in computational efficiency and feature extraction, which lead to the application of a self-attention mechanism. This paper presents a fault diagnosis method based on frequency domain Gramian angular field (GAF) and Markov transition field (MTF) features for planetary gearboxes by combining the characteristics of vibration signal fault diagnosis and transformer network structure. The experiments show that the frequency domain GAF-MTF features can effectively reduce the influence of time shifting between samples and improve diagnostic accuracy. Furthermore, comparisons with other mainstream models indicate that the proposed method can obtain competitive results and achieve more accurate and robust performance under noisy conditions.