# Transfer Learning-Based Fault Diagnosis under Data Deficiency
###### 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.  
transfer learning; fault diagnosis; data deficiency; imbalanced data; linear motion guide 2020 10 7768 10.3390/app10217768 10.  
machine learning classification performance. A data fusion approach was also proposed as a means to compensate the absence of data in certain classes, which are addressed by [PERSON] et al. [6], with reviews and challenges for the industrial prognosis; [PERSON] et al. [7], for the gearbox fault diagnosis using the convolutional neural network (CNN) and motor current signature analysis, [PERSON] et al. [8] for the mechanical fault diagnosis based on IoT; [PERSON] et al. [9], for the rotating machine fault classification, to name a few. Over the years, transfer learning (TL) has emerged as one of the solutions. TL is a method of extracting knowledge obtained in one domain (source domain) to solve problems in another domain (target domain), combining with machine learning techniques. The fundamental idea is that if the source and target domain are similar, using the trained weights of the source domain is more efficient than the training of the target domain from scratch. As a result, the TL algorithm compensates for data deficiency in the target domain, such as the insufficient amount and unlabeled samples. Several studies have been made in this direction. For example, [PERSON] et al. [10] proposed transfer learning based on the neural networks (NN) for multiclass fault diagnosis of rolling bearings. Parameters of the NN in the source domain are obtained through the training phase and transferred to the target task which has more classes to classify. Classification accuracy and training time were improved compared to those by the traditional NN. [PERSON] et al. [11] proposed a new deep transfer learning method for fault diagnosis of different working conditions. They extracted domain-invariant features by using sparse auto-encoder and the maximum mean discrepancy between domains. Compared to the results of traditional methods, such as the deep belief network (DBN), SVM, and artificial neural network (ANN), the proposed method has made good improvement. They have also conducted a comparative study for different ratios of samples between the source and target dataset. [PERSON] et al. proposed a transfer learning-based fault diagnosis network combined with high-order Kullback-Leibler (HKL) divergence [12]. The proposed network has extracted features through the sparse filtering and HKL divergence for discrimination and generalization ability.