Abstract
This paper presents a non-intrusive, real-time, online Condition Monitoring and Fault
Diagnosis system for Permanent Magnet Synchronous Machines. The system utilizes
only the motor drive's built-in sensors, such as current and voltage sensors, to detect
three types of faults: inter-turn short circuit, partial demagnetization, and static
eccentricity. The proposed solution adopts a hardware-free approach, utilizing
current/voltage signature analysis to optimize cost-effectiveness. It requires a small
memory and short execution time, allowing it to be implemented on a simple motor
controller with limited memory and calculation power. The system is designed for
critical mission applications, and therefore, computation load, code size, memory
allocation, and run-time optimization are key focuses for real-time operation. The
proposed method has a high detection accuracy of 98%, is computationally efficient,
and can accurately detect and classify the fault. The system provides immediate
insights into motor health without interrupting the drive operation.
Diagnosis system for Permanent Magnet Synchronous Machines. The system utilizes
only the motor drive's built-in sensors, such as current and voltage sensors, to detect
three types of faults: inter-turn short circuit, partial demagnetization, and static
eccentricity. The proposed solution adopts a hardware-free approach, utilizing
current/voltage signature analysis to optimize cost-effectiveness. It requires a small
memory and short execution time, allowing it to be implemented on a simple motor
controller with limited memory and calculation power. The system is designed for
critical mission applications, and therefore, computation load, code size, memory
allocation, and run-time optimization are key focuses for real-time operation. The
proposed method has a high detection accuracy of 98%, is computationally efficient,
and can accurately detect and classify the fault. The system provides immediate
insights into motor health without interrupting the drive operation.
| Original language | English |
|---|---|
| Article number | 114684 |
| Journal | Measurement: Journal of the International Measurement Confederation |
| DOIs | |
| Publication status | Published - 9 Apr 2024 |