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This paper introduces a sensitivity-enhanced fault detection algorithm, termed the Fault Diagnosis and Amplifying (FDnA) method, designed to amplify fault signals in induction motors with broken rotor bars. The FDnA algorithm employs a cascaded state observer structure, integrating stator flux linkage and stator current observers based on an analytical model of an induction motor. By multiplying the output signals of these observers, the algorithm significantly enhances the sensitivity of fault detection. The FDnA method is robust against external environmental conditions and excels in accurately detecting broken rotor bar faults. Additionally, it offers a cost-effective and retrofittable solution by replacing traditional sensors and interfacing circuits with state observers, eliminating the need for additional hardware. Comprehensive validation through simulations and rapid control prototyping highlights the superior fault detection sensitivity of the FDnA method compared to conventional approaches, establishing it as a reliable and efficient advancement in fault detection for induction motors.

A permanent magnet synchronous motor (PMSM) drive has been widely used for various industrial applications such as electric vehicles (EVs) and home appliances due to its attractive features such as high power/torque density and high efficiency. While a PMSM has various attractive features, it also includes some drawbacks such as parameter sensitivity with respect to operating conditions of PMSMs. For example, permanent magnet (PM) flux linkage of an IPMSM is decreased as magnet temperature of in a rotor of PMSM increases. As known, a characteristic current of a PMSM is determined by its PM flux linkage and an operating region and operating characteristic are affected by the characteristic current. In this paper, a control algorithm is proposed for a PMSM drive to adjust its operating region actively by reflecting PM variations in real time. PM flux linkage of a PMSM is estimated through a stator flux linkage observer in real time and the estimated PM flux linkage is applied to the active operating region adjustment (AORA) algorithm for more effective utilization of an IPMSM drive operation. The proposed AORA algorithm for a PMSM drive is implemented and verified experimentally in this paper.

This study attempted to provide the basic data for developing a system to identify the role of medical technologists and ensure an efficient response for quick and accurate diagnostic tests in the COVID-19 era. The research method involved using focus group interviews for a survey and analysis of 15 medical institutions. Eleven sample collection institutions, 10.4 medical technologists, 2.1 minutes of collection time, 5.4 hours of test time, 9,670 tests, 6.2 member test workforce size, and 7 screening center operating institutions were surveyed. The results of the focus group interview analysis revealed that there were no standardized guidelines covering working hours, area, and environment to protect sample collectors and testers in relation to the COVID-19 tests. Also, legal protection measures were insufficient in the event of accidental infections and there were no personnel regulations related to COVID-19. In addition, the professional training of sample collectors and molecular diagnostic testers was required for reliable COVID-19 testing. In conclusion, it is necessary to provide professional education through special test short-term training institutions to cope with emergency infectious diseases such as COVID-19. Legal systems should be put in place to protect the workforce and ensure stability.