Transfer Learning-Based Fault Diagnosis under Data Deficiency

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.

A STUDY OF MULTI CRITERIA DECISION MAKING FOR SELECTING SUPPLIERS OF LINEAR MOTION GUIDE

Supplier evaluation and selection is becoming more and more important for companies in today’s logistics and supply chain management. Decision making in supplier selection domain, as an essential component of supply chain management, is a complex process due to the fact that a wide range of diverse criteria, stakeholders and possible solutions are embedded into this process. This paper focuses on the application of some single and hybrid multi criteria decision making approaches for the selection of suppliers of transportation and logistics equipment. The analytic hierarchy process (AHP), stepwise weight assessment ratio analysis (SWARA) and technique for the order preference by similarity to ideal solution (TOPSIS) have been implemented in the "Lagerton" company in Serbia for evaluation and selection of the supplier in the case of procurement of THK Linear motion guide components. The best ranked supplier has been suggested to the company and the sensitivity analysis of ranking orders according to the criteria weights variations has been done.