Transfer learning with convolutional neural networks for small sample size problem in machinery fault diagnosis. (July 2019)

• Record Type: Journal Article
• Authors: Xiao, Dengyu; Huang, Yixiang; Qin, Chengjin; Liu, Zhiyu; Li, Yanming; Liu, Chengliang
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 14(2019)
• Page Start: 5131
• Page End: 5143
• Publication Date: 2019-07
• Subjects: Transfer learning -- fault diagnosis -- convolutional neural network -- small sample size; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/0954406219840381
• Languages: English
• ISSNs: 0954-4062

Abstract

Data-driven machinery fault diagnosis has gained much attention from academic research and industry to guarantee the machinery reliability. Traditional fault diagnosis frameworks are commonly under a default assumption: the training and test samples share the similar distribution. However, it is nearly impossible in real industrial applications, where the operating condition always changes over time and the quantity of the same-distribution samples is often not sufficient to build a qualified diagnostic model. Therefore, transfer learning, which possesses the capacity to leverage the knowledge learnt from the massive source data to establish a diagnosis model for the similar but small target data, has shown potential value in machine fault diagnosis with small sample size. In this paper, we propose a novel fault diagnosis framework for the small amount of target data based on transfer learning, using a modified TrAdaBoost algorithm and convolutional neural networks. First, the massive source data with different distributions is added to the target data as the training data. Then, a convolutional neural network is selected as the base learner and the modified TrAdaBoost algorithm is employed for the weight update of each training sample to form a stronger diagnostic model. The whole proposition is experimentally demonstrated and discussed by carrying out the tests of six three-phase induction motors under different operating conditions and fault types. Results show that compared with other methods, the proposed framework can achieve the highest fault diagnostic accuracy with inadequate target data.