Neural network-based diagnostic tool for detecting stator inter-turn faults in line start permanent magnet synchronous motors

Three-phase line-start permanent magnet synchronous motors are considered among the most promising types of motors in industrial applications. However, these motors experience several faults, which may cause significant financial losses. This paper proposed a feed-forward neural network-based diagnostic tool for accurate and fast detection of the location and severity of stator inter-turn faults. The input to the neural network is a group of representative statistical and frequency-based features extracted from the steady-state three-phase stator current signals. The current signals with different numbers of shorted turns and loading conditions are captured using the developed finite element JMAG ™ model for interior mount LSPMSM. In addition, an experimental set-up was built to validate the finite element model and the proposed diagnostics tool. The simulation and experimental test results showed an overall accuracy of 93.125% in detecting the location and the size of inter-turn, whereas, the accuracy in detecting the location of the fault is 100%

Proceedings of the 10th international conference on energy efficiency in motor driven systems (EEMODS' 2017)

The 10th International Conference on Energy Efficiency in Motor Driven Systems (EEMODS'17) was be held in Rome (Italy) on 6-8 September, 2017. The EEMODS conferences have been very successful in attracting distinguished and international presenters and attendees. The wide variety of stakeholders has included professionals involved in manufacturing, marketing, and promotion of energy efficient motors and motor driven systems and representatives from research labs, academia, and public policy. EEMODS’15 provided a forum to discuss and debate the latest developments in the impacts of electrical motor systems (advanced motors and drives, compressors, pumps, and fans) on energy and the environment, the policies and programmes adopted and planned, and the technical and commercial advances made in the dissemination and penetration of energy-efficient motor systems. In addition EEMODS covered also energy management in organizations, international harmonization of test method and financing of energy efficiency in motor systems. The Book of Proceedings contains the peer reviewed paper that have been presented at the conference.JRC.C.2-Energy Efficiency and Renewable

Artificial Intelligence Supported EV Electric Powertrain for Safety Improvement

As an environmentally friendly transport option, electric vehicles (EVs) are endowed with the characteristics of low fossil energy consumption and low pollutant emissions. In today's growing market share of EVs, the safety and reliability of the powertrain system will be directly related to the safety of human life. Reliability problems of EV powertrains may occur in any power electronic (PE) component and mechanical part, both sudden and cumulative. These faults in different locations and degrees will continuously threaten the life of drivers and pedestrians, bringing irreparable consequences. Therefore, monitoring and predicting the real-time health status of EV powertrain is a high-priority, arduous and challenging task. The purposes of this study are to develop AI-supported effective safety improvement techniques for EV powertrains. In the first place, a literature review is carried out to illustrate the up-to-date AI applications for solving condition monitoring and fault detection issues of EV powertrains, where recent case studies between conventional methods and AI-based methods in EV applications are compared and analysed. On this ground this study, then, focuses on the theories and techniques concerning this topic so as to tackle different challenges encountered in the actual applications. In detail, first, as for diagnosing the bearing system in the earlier fault period, a novel inferable deep distilled attention network is designed to detect multiple bearing faults. Second, a deep learning and simulation driven approach that combines the domain-adversarial neural network and the lumped-parameter thermal network (LPTN) is proposed for achieve IPMSM permanent magnet temperature estimation work. Finally, to ensure the use safety of the IGBT module, deep learning -based IGBT modules’ double pulse test (DPT) efficiency enhancement is proposed and achieved via multimodal fusion networks and graph convolution networks

An Accurate Tool for Detecting Stator Inter-Turn Fault in LSPMSM

© 2013 IEEE. This paper proposes an accurate diagnosing tool that can predict the stator inter-turn size in line start permanent magnetic synchronous motor (LSPMSM). The proposed diagnosing approach is developed based on an experimentally validated mathematical model of the motor under inter-turn fault. The developed model has been tested using MATLAB® under different loading and fault size conditions. Since the stator currents and voltages are easily accessible, it is decided to use them as the key signatures for developing the diagnostic tool. Several time and frequency-based features have been extracted using motor current and voltage waveforms under different loading and fault size conditions. The developed tool has been designed to correlate the extracted features with its corresponding size of stator inter-turn fault. Finally, testing of the developed diagnosis tool shows a high accuracy of 96% in detecting the size. Moreover, the proposed diagnostic tool is examined against motor parameter variations. The results confirm the robustness of the proposed approach where the accuracy is slightly affected under a wide range of motor parameter variations