Transfer Learning-Based Fault Diagnosis under Data Deficiency

• Published in: Applied sciences Vol. 10; no. 21; p. 7768
• Main Authors: Cho, Seong Hee, Kim, Seokgoo, Choi, Joo-Ho
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2020
• Subjects: Accuracy; Classification; data deficiency; Datasets; Domains; Fault diagnosis; imbalanced data; Inspection; Knowledge; Learning algorithms; linear motion guide; Lower bounds; Machine learning; Neural networks; Performance evaluation; Sample size; Support vector machines; Transfer learning
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10217768

In the fault diagnosis study, data deficiency, meaning that the fault data for the training are scarce, is often encountered, and it may deteriorate the performance of the fault diagnosis greatly. To solve this issue, the transfer learning (TL) approach is employed to exploit the neural network (NN) trained in another (source) domain where enough fault data are available in order to improve the NN performance of the real (target) domain. While there have been similar attempts of TL in the literature to solve the imbalance issue, they were about the sample imbalance between the source and target domain, whereas the present study considers the imbalance between the normal and fault data. To illustrate this, normal and fault datasets are acquired from the linear motion guide, in which the data at high and low speeds represent the real operation (target) and maintenance inspection (source), respectively. The effect of data deficiency is studied by reducing the number of fault data in the target domain, and comparing the performance of TL, which exploits the knowledge of the source domain and the ordinary machine learning (ML) approach without it. By examining the accuracy of the fault diagnosis as a function of imbalance ratio, it is found that the lower bound and interquartile range (IQR) of the accuracy are improved greatly by employing the TL approach. Therefore, it can be concluded that TL is truly more effective than the ordinary ML when there is a large imbalance between the fault and normal data, such as smaller than 0.1.