Unanticipated Fault Detection and Isolation of Telescope Drive System Based on Luenberger Observer and Axes Transformation

IF 0.6 Q4 AUTOMATION & CONTROL SYSTEMS AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2024-05-06 DOI:10.3103/S0146411624700032
Zhuangzhuang Deng, Shihai Yang, Yun Li, Xiaojie Gu, Lingzhe Xu, Ruiqiang Liu
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Abstract

This paper puts forward a new strategy current sensor unanticipated faults detection and isolation (FDI) for permanent magnet synchronous motor (PMSM) in a telescope drive system. This approach uses axes transformation, PMSM model and Luenberger observer to generate residuals, and the influence of unanticipated faults (UFs) in different phases on the current components is analyzed. The sensor UFs detection is performed by processing residuals obtained from the observer. In addition, based on the information provided by fault detection, an innovative logic judgment algorithm is devised to realize fault isolation. The proposed method can discriminate between single UF of different types and multiple simultaneous UFs of various categories as well as faulty current sensors. Extensive simulation experiments prove that the designed logic judgment algorithm is effective, and the FDI can be implemented successfully.

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基于卢恩贝格尔观测器和轴变换的望远镜驱动系统的意外故障检测与隔离
摘要 本文针对望远镜驱动系统中的永磁同步电机(PMSM)提出了一种新的电流传感器非预期故障检测和隔离(FDI)策略。该方法利用轴变换、PMSM 模型和 Luenberger 观察器生成残差,并分析了不同阶段的非预期故障(UFs)对电流分量的影响。传感器 UFs 检测是通过处理从观测器获得的残差进行的。此外,根据故障检测提供的信息,设计了一种创新的逻辑判断算法来实现故障隔离。所提出的方法可以区分不同类型的单个 UF、不同类型的多个同时出现的 UF 以及故障电流传感器。大量的仿真实验证明,所设计的逻辑判断算法是有效的,而且 FDI 可以成功实施。
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来源期刊
AUTOMATIC CONTROL AND COMPUTER SCIENCES
AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.70
自引率
22.20%
发文量
47
期刊介绍: Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision
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