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fault-Diagnosis and fault-Recovery system of Hall Sensors in Brushless DC Motor Based on Neural Networks

SENSORS 2023年第9期23卷 4330页

作者： Chu, Kenny Sau Kang Chew, Kuew Wai Chang, Yoong Choon Univ Tunku Abdul Rahman Lee Kong Chian Fac Engn & Sci Jalan Sungai Long Kajang 43000 Malaysia

This paper proposes a neural-network-based framework using Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) for detecting faults and recovering signals from Hall sensors in brushless DC motors. Hall sensors are critical components in determining the position and speed of motors, and faults in these sensors can disrupt their normal operation. Traditional fault-diagnosis methods, such as state-sensitive and transition-sensitive approaches, and fault-recovery methods, such as vector tracking observer, have been widely used in the industry but can be inflexible when applied to different models. The proposed fault diagnosis using the CNN-LSTM model was trained on the signal sequences of Hall sensors and can effectively distinguish between normal and faulty signals, achieving an accuracy of the fault-diagnosis system of around 99.3% for identifying the type of fault. Additionally, the proposed fault recovery using the CNN-LSTM model was trained on the signal sequences of Hall sensors and the output of the fault-detection system, achieving an efficiency of determining the position of the phase in the sequence of the Hall sensor signal at around 97%. This work has three main contributions: (1) a CNN-LSTM neural network structure is proposed to be implemented in both the fault-diagnosis and fault-recovery systems for efficient learning and feature extraction from the Hall sensor data. (2) The proposed fault-diagnosis system is equipped with a sensitive and accurate fault-diagnosis system that can achieve an accuracy exceeding 98%. (3) The proposed fault-recovery system is capable of recovering the position in the sequence states of the Hall sensors, achieving an accuracy of 95% or higher.

关键词： Hall sensor fault-detection system signal-recovery system CNNLSTM neural network brushless direct current motor (BLDC)

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Proposal of an Embedded Distributed system for fault detection *

引用

IFAC Proceedings Volumes 2013年第7期46卷 144-149页

作者： Leonardo B. Piccoli Carlos S. Guimaraes Renato Ventura B. Henriques Carlos E. Pereira Electrical Engineering Department at the Universidade Federal do Rio Grande do Sul - UFRGS. Control Automation and Robotics Group - Av. Osvaldo Aranha 103 Bom Fim CEP:90031-190 Porto Alegre RS Brasil

This paper proposes a Distributed fault-detection system for fault detection and prediction in electrical actuators used in pipelines for oil and gas transportation. The proposed system incorporates a signal processing flow that requires low complex mathematical operations using C-ANSI language. For the embedded fault-detection system was created a network of real-time on the RS-485 physical layer to application in electric actuators. The system is composed of Master and Slave modules using the Modbus protocol in order to communicate with other devices. To prove its functionalities a testbench was developed, which aims to reproduce in a lab some common faults and degradation processes that may occur in real world field applications. The monitoring of the electric actuators operation through this system can identify problems and faults before they become severe, providing greater efficiencies on the production line and avoid expressive financial losses. The prototype developed in this paper is based on software and hardware platforms flexible and open-source. The slave module equipment was used to collect the sensors information from specific points of the actuator. The sensor data collected and emulation were used to validate the propose fault detection methodology.

关键词： fault Tolerance fault-detection system Electrical Valves

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