Fault-Tolerant Control of Five-Phase Permanent Magnet Synchronous Hub Motor Based on Improved Model Predictive Current Control

Li Teng,Yao Ming,Sun Xiaodong 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.1

To further improve the reliability of wheel-driven electric vehicles, this paper studies the fault-tolerant control operation of a fve-phase permanent magnet synchronous hub motor (PMSHM). A fault-tolerant scheme based on model predictive current control (MPCC) is proposed for the fve-phase PMSHM under single-phase open-circuit fault and two-phase open-circuit fault operation. In the implementation of this scheme, the fve-phase PMSHM model during fault operation is discussed, and the coordinate transformation matrices for single-phase fault, adjacent two-phase fault and non-adjacent two-phase fault are derived respectively. Through further analysis, the ofset voltage vector at the time of open-circuit fault can be obtained, and the newly obtained voltage vector can be used as the candidate set for model predictive control. The MPCC method combines duty cycle control and the vector preselection method. Compared with the traditional MPCC scheme, the improved MPCC scheme not only reduces the computation time but also enhances the steady-state performance of the control scheme. Finally, it is verifed that the proposed fault-tolerant scheme based on MPCC can efectively address the diference in open-loop fault operation and improve the reliability of the hub drive system.

Model Predictive Current Control of Fault-Tolerant Permanent Magnet Rim Drive Motor Based on Six-Phase Stationary Coordinate System

Zhao Tianrui,Zhu Jingwei,Li Mingxuan,Zang Kun,Liao Haibo 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.4

Aiming at the problems of large computation and complex fault-tolerant control strategy in the application of traditional model predictive current control (MPCC) strategy for six-phase fault-tolerant permanent magnet rim drive motor (FTPMRDM), a six-phase stationary coordinate system based MPCC (SPSCS-MPCC) strategy is proposed. At a fi xed sampling frequency, the six-phase current is calculated from the current moment rotor position, speed, and fault-tolerant control strategy. Furthermore, the six-phase current is predicted independently in two rounds. The optimal switching combination of the six-phase H-bridge inverter circuit is determined by cost function to achieve the MPCC of the FTPM-RDM. Then, the hardware experimental platform based on StarSim is designed. The experimental results demonstrate that the SPSCS-MPCC proposed in this paper not only has the advantages of a simple algorithm and low motor torque pulsation under fault-free and open-circuit faults but also takes into account the fast dynamic response of traditional MPCC algorithms.

LPC와 DTW 기법을 이용한 유도전동기의 고장검출 및 진단

황철희(Chul-Hee Hwang),김용민(Yong-Min Kim),김철홍(Cheol-Hong Kim),김종면(Jong-Myon Kim) 한국컴퓨터정보학회 2011 韓國컴퓨터情報學會論文誌 Vol.16 No.3

본 논문은 유도전동기의 고장검출 및 진단을 위한 효율적인 2-단계 고장예측 알고리즘을 제안한다. 첫 번째 단계에서는 고장 패턴 추출을 위해 선형 예측 부호화 (Linear Predictive Coding: LPC) 기법을 사용하고, 두 번째 단계에서는 고장 패턴 매칭을 위해 동적시간교정법 (Dynamic Time Warping: DTW)을 사용한다. 유도전동기에서 정상 및 각종 이상 상태의 조건을 발생시켜 추출한 샘플링 주파수 8kHz, 샘플링 시간 2.2초의 정상상태 및 비정상 상태의 진동데이터 8개를 사용하여 모의 실험한 결과, 제안한 고장예측 알고리즘은 기존의 고장진단 알고리즘보다 약 45%의 정확도 향상을 보였다. 또한 TI사의 TMS320F2812 DSP를 내장한 테스트베드 시스템을 제작하여 제안한 고장예측 알고리즘을 구현하고 검증하였다. This paper proposes an efficient two-stage fault prediction algorithm for fault detection and diagnosis of induction motors. In the first phase, we use a linear predictive coding (LPC) method to extract fault patterns. In the second phase, we use a dynamic time warping (DTW) method to match fault patterns. Experiment results using eight vibration data, which were collected from an induction motor of normal fault states with sampling frequency of 8 kHz and sampling time of 2.2 second, showed that our proposed fault prediction algorithm provides about 45% better accuracy than a conventional fault diagnosis algorithm. In addition, we implemented and tested the proposed fault prediction algorithm on a testbed system including TI's TMS320F2812 DSP that we developed.

MCSA를 활용한 3상 유도전동기의 고장진단과 예지보전

김기동(Ki Dong Kim),김영일(Young Il Kim) 대한설비공학회 2021 설비공학 논문집 Vol.33 No.12

Three-phase induction motors are widely used as driving parts of rotating machines in industrial fields because they are relatively inexpensive, easy to install, and easy to maintain. However, defects may occur due to various factors such as problems with the motor itself, structural problems with facilities, or problem with operation. Failure of the motor can have a huge impact on the productivity, quality, and safety in addition to the repair cost of the motor. Generally, vibration monitoring, lubrication analysis, temperature measurement, and infrared sensing are used to diagnose motor faults in the past. Recently, there has been a growing number of studies on the motor current signature analysis (MCSA) method, which has a wide range of fault diagnosis that enables real time monitoring of the motor status online. This paper is a study on fault diagnosis and predictive maintenance of motor using model-based MCSA in laboratory and fields. In conclusion, the fault of the motor can be accurately diagnosed with MCSA. In particular, by monitoring the trend value, it is possible to easily determine the degree of the defect. This work confirms that CBM-based predictive maintenance can be used for diagnosis of three-phase induction motors.

이상치 데이터를 고려한 DT-CNN 기반의 전동기 고장 예측

한지훈(Ji-Hoon Han),최동진(Dong-Jin Choi),박상욱(Sang-Uk Park),홍선기(Sun-Ki Hong) 제어로봇시스템학회 2020 제어·로봇·시스템학회 논문지 Vol.26 No.11

One of the major problems with the existing motor failure prediction system is to assume that all motors with the same fault condition have the same or a similar signal. This is a problem that arises because it is impossible to measure all the countless types of motors and data of driving conditions and failures. It is difficult to implement a general-purpose failure prediction system with an existing system having limited data and limited output. Data that have a large difference because they do not exist in the existing system are called outlier data. In previous studies, the problem arising from the outlier data has not been considered. To solve this problem, a system designed by separating the failure diagnosis model and the failure prediction model is proposed. The diagnostic model of the proposed system can detect data that are not inside big data using a decision-tree convolution neural network (DT-CNN). By using the diagnostic model and the predictive model in series, it is possible to analyze data in a non-measured state more efficiently. Additionally, a method for averaging the outputs of the diagnostic and predictive models is proposed. Through this, the deep learning algorithm can obtain in effect of applying the filter. Furthermore, the average values can be used to confirm the long-term signal change trend. The proposed system improves the problems of the existing failure prediction and enables more practical failure prediction.

Fault-tolerant Model Predictive Current Control of Six-Phase Permanent Magnet Synchronous Motors with Pulse Width Modulation

Yao Ming,Peng Jingyao,Sun Xiaodong,Sun Yueping 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.3

A model predictive current control strategy with pulse width modulation is proposed for a single-phase open circuit fault. This method decomposes the change matrix based on the normal vector space to ensure that the decoupling matrix remains unchanged so that there is no need to reconfigure the controller topology. By analysing the fault phase voltage difference under normal and fault conditions, 24 synthetic virtual voltage vectors are used to compensate the voltage vector during a single-phase fault and eliminate the current coupling problem. In addition, a standard pulse width modulation switching sequence is implemented to achieve the purpose of unifying the switching frequency, which makes the method easy to implement. Finally, the effectiveness of the proposed method is verified by experiments.

Open‑circuit fault diagnosis for OEW‑PMSM drives based on finite‑control‑set model predictive control

Tuhuan Li,Guiping Du,Yanxiong Lei,Siqiang Chen 전력전자학회 2022 JOURNAL OF POWER ELECTRONICS Vol.22 No.2

An open-end winding permanent magnet synchronous motor (OEW-PMSM) fed by dual inverters has good application value in the drive system of electric vehicles due to the motor’s advantages, such as high output power, wide speed range and excellent fault-tolerant ability. However, the symmetry of dual-inverter topology causes difficulty in identifying the specific faulty switch when an open-circuit fault occurs. Thus, this study proposes a switch open-circuit fault diagnosis strategy for OEW-PMSM based on finite-control-set model predictive control (FCS-MPC). With the known and unchanged switching state in each control period of FCS-MPC, the proposed strategy uses the predicted switching state to predict the phase voltage. According to the error of predicted voltage and measured voltage under switch failure, the faulty phase and faulty switch pair are identified. Finally, the relationship between switching states and error voltage is analyzed, and a diagnosis function is constructed to identify the specific faulty switch. The validity of the proposed diagnosis strategy is confirmed by the simulation and experimental results.

Diagnosis of motor aging through CNN model using signal correlation

Ji-Hoon Han,Dong-Jin Choi,Sang-Uk Park,Sun-Ki Hong 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10

Unlike motor failure diagnosis, motor failure prediction is more difficult to collect data and develop algorithms. The classic motor failure prediction is a method of determining the characteristics of a motor failure and finding a signal accordingly. However, this requires specialized knowledge of the motor, and this results in poor versatility. Also, it is not easy to classify the signal change due to the aging of the motor and the signal change due to the failure. To solve this problem, a correlation analysis algorithm is proposed between the motor steady-state signal and the collected signal. The proposed algorithm is composed of deep learning algorithms using the values rather than simple correlation analysis. It is data-driven, unlike the classic method based on prior knowledge. In addition, this algorithm can detect the change of the motor signal by FFT the motor vibration signal by 100 ms. Through the proposed algorithm, the classification of the signal of aging and the failure signal of the motor will proceed.

Screening of False Induction Motor Fault Alarms Produced by Axial Air Ducts Based on the Space-Harmonic-Induced Current Components

Chanseung Yang,Tae-June Kang,Sang Bin Lee,Ji-Yoon Yoo,Bellini, Alberto,Zarri, Luca,Filippetti, Fiorenzo Institute of Electrical and Electronics Engineers 2015 IEEE transactions on industrial electronics Vol. No.

<P>Motor current signature analysis (MCSA) based on the 50/60-Hz sidebands has become a common test in industry for monitoring the condition of the induction motor rotor cage. However, many cases of unnecessary motor inspection or outage due to false alarms produced by rotor axial duct interference have been reported. If the number of axial ducts and poles is identical, this can produce 50/60-Hz sideband frequency components in MCSA that overlap with that of rotor faults, resulting in false alarms. However, there currently is no practical test method available for distinguishing rotor faults and false indications other than testing the rotor offline or under the startup transient. In this paper, the feasibility of using the rotor fault frequency component produced by the space harmonic waves is evaluated as a solution for the first time. Since the fifth or seventh space harmonics have a spatial distribution of flux that does not penetrate in the rotor yoke to reach the axial ducts, they do not produce false alarms. The proposed method is verified on 6.6-kV motors misdiagnosed with broken bars via the 50/60-Hz sidebands of MCSA. It is shown that it provides reliable online indication of rotor faults independent of axial duct influence and can be used for screening out false alarms.</P>

모터펌프의 지능형 진단시스템 구현에 관한 연구

안재현,양오 한국반도체디스플레이기술학회 2019 반도체디스플레이기술학회지 Vol.18 No.4

The diagnosis of the failure for the existing electrical facilities was based on regular preventive maintenance, but this preventive maintenance was limited in preventing a lot of cost loss and sudden system failure. To overcome these shortcomings, fault prediction and diagnostic techniques are critical to increasing system reliability by monitoring electrical installations in real time and detecting abnormal conditions in the facility early. As the performance and quality deterioration problem occurs frequently due to the increase in the number of users of the motor pump, the purpose is to build an intelligent control system that can control the motor pump to maximize the performance and to improve the quality and reliability. To this end, a vibration sensor, temperature sensor, pressure sensor, and low water level sensor are used to detect vibrations, temperatures, pressures, and low water levels that can occur in the motor pump, and to build a system that can identify and diagnose information to users in real time.