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BLDC motors are widely employed in various applications because of their high efficiency, reliability, and long operational life. For BLDC motors, any electric malfunctions in the commutation signal creation with or without Hall sensors can lead to unexpected vibrations, crashes, and accidents according to application areas. Therefore, fast detection and diagnosis of these faults are crucial for the reliable operation of BLDC motor drive systems. In this paper, a unique approach has been explored for developing fault signatures to detect commutation signal faults accurately and rapidly in the BLDC motor drive system under highly dynamic loads. After the fault detection, a commutation signal is indirectly reconstructed based on healthy commutation signals to continuously drive the motor drive system to avoid serious electrical and mechanical issues due to the faults. The proposed approach and feasibility of the method have been verified both by simulation and experimental studies. The results of the proposed method will significantly improve the accuracy of the commutation signal fault detection and eventually enhance the reliability of the BLDC motor drive.
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A Robust Online Diagnostic Strategy of Inverter Open-Circuit Faults for Robotic Joint BLDC Motors
As robots are increasingly used in remote, safety-critical, and hazardous applications, the reliability of robots is becoming more important than ever before. Robotic arm joint motor-drive systems are vulnerable to hardware failures due to harsh operating environment in many scenarios, which may yield various joint failures and result in significant downtime costs. Targeting the most common robotic joint brushless DC (BLDC) motor-drive systems, this paper proposes a robust online diagnostic method for semiconductor faults for BLDC motor drives. The proposed fault diagnostic technique is based on the stator current signature analysis. Specifically, this paper investigates the performance of the BLDC joint motors under open-circuit faults of the inverter switches using finite element co-simulation tools. Furthermore, the proposed methodology is not only capable of detecting any open-circuit faults but also identifying faulty switches based on a knowledge table by considering various fault conditions. The robustness of the proposed technique was verified through extensive simulations under different speed and load conditions. Moreover, simulations have been carried out on a Kinova Gen-3 robot arm to verify the theoretical findings, highlighting the impacts of locked joints on the robot’s end-effector locations. Finally, experimental results are presented to corroborate the performance of the proposed fault diagnostic strategy.
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- Award ID(s):
- 2205292
- PAR ID:
- 10530177
- Publisher / Repository:
- MDPI AG
- Date Published:
- Journal Name:
- Machines
- Volume:
- 12
- Issue:
- 7
- ISSN:
- 2075-1702
- Page Range / eLocation ID:
- 430
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/machines12070430
