Abstract
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.
Highlights
Fault diagnosis of mechanical components has been studied very actively and many important advances have been made primarily based on the machine learning techniques such as k-nearest neighbor (KNN) [1], support vector machine (SVM) [2], convolutional neural network (CNN) [3], and others
A transfer learning-based fault diagnosis is proposed to solve this problem, which is to pre-train the NN using the large datasets as the source domain, and transfer the knowledge to train the NN using the imbalanced datasets in the real target domain
Normal and fault datasets are acquired from the run-to-fail test of the linear motion (LM) guides, in which the data at high and low speeds are regarded as those for the real operation and maintenance inspection, respectively
Summary
Fault diagnosis of mechanical components has been studied very actively and many important advances have been made primarily based on the machine learning techniques such as k-nearest neighbor (KNN) [1], support vector machine (SVM) [2], convolutional neural network (CNN) [3], and others. Guo et al [14] proposed a deep convolutional transfer learning network (DCTLN) for intelligent fault diagnosis of machines with unlabeled data They optimized their network models in the direction of minimizing the maximum mean discrepancy (MMD) between the source and target domain datasets for learning domain-invariant features from raw signals. Recalling that the fault does not occur often in the real operation conditions because of the periodic maintenance or the costly effort to make by intention, the imbalance issue between the normal and fault data is of great importance for practical use For this reason, the effect of transfer learning in the fault diagnosis of imbalanced data is investigated in this study
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