Statistical Feature Based Identification of Rotor Fault Indicators for Three Phase Induction Motor

Ghosh, Amrita; Barman, Pabitra Kumar; Das, Santanu

doi:10.1109/hydcon48903.2020.9242691

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…15 If a motor is judged to be unhealthy by the ZSVC method, a high-frequency (HF) signal is then injected to discern the fault type by its amplitude and component. In earlier research, 6,[16][17][18] various fault indicators were developed for fault diagnosis by collecting data during motor operations, where the detection appears to be prompt with high accuracy.…”

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confidence: 99%

A simple signal extraction‐based online real‐time diagnosis approach for interturn short‐circuit fault of permanent magnet motor

Hsu

Hsieh

2023

Circuit Theory & Apps

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Summary A fast and reliable interturn short circuit (ITSC) diagnosis system is necessary to secure safe operations of permanent magnet synchronous motor (PMSM) for its widespread applications in industrial automation and electric vehicles. This paper develops a simple online real‐time technique based on signal extraction to detect the ITSC fault in the PMSM. Some popular diagnosis methods involving machine learning or instantaneous power information may require large computing resources, data storage, or extra sensing devices for data collection. This makes it difficult to fulfill online and self‐detection of ITSC faults in motor drive systems. This paper makes use of the second‐order harmonic components in dq‐axis current for diagnosis when they occur due to ITSC faults. Through the developed amplitude‐tracking algorithm, the diagnosis can be implemented by using a common digital signal processor without complex algorithms and additional hardware or sensors. Therefore, it is suitable for low‐cost and remote applications where self‐detection is needed. The proposed method is validated by finite element analysis and measurements with a hardware‐in‐the‐loop toolkit. It is found that the method successfully detects a short circuit fault with only one out of 54 turns in one phase of the motor (1.9%).

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confidence: 99%