Mode Recognition and Fault Positioning of Permanent Magnet Demagnetization for PMSM

Gao, Caixia; Nie, Yanjie; Si, Jikai; Zhang, Fu; Feng, Haichao

doi:10.3390/en12091644

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…To construct a phase tracker, the training dataset of the phase tracker needs to be generated first. According to statistics of the collected experimental data, since the load was constant and the amplitude of stator current did not change much, it was assumed to be [5,30]; the fundamental frequency range of the current was [25,130]. Considering the requirement of diagnostic rapidity, the diagnostic period was set to 0.1s, so the input sequence length of the phase tracker was 2500.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…The two most common faults in PMSMs are the inter-turn short circuit fault (ITSF) and the irreversible demagnetization fault (IDF) [3]. ITSF refers to a short circuit between two or more turns winding coils in the same phase of the stator [4], whereas IDF refers to the weakening of the permanent magnets on the rotor [5]. The occurrence of this fault will cause the output torque of the motor to become oscillatory, and at the same time the increase in the heat may lead to further deterioration related to the fault.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Condition PMSM Fault Diagnosis Based on Convolutional Neural Network Phase Tracker

et al. 2022

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In many industrial systems, symmetry is the key to ensuring efficiency and reliability. For example, in electric vehicles, the driving system often requires high symmetry. As widely used motors, permanent magnet synchronous motors (PMSMs) are often used in highly symmetrical structures as the driving devices. Consequently, maintaining the symmetry of the system relies on the normal and stable operation of the PMSM, and it is necessary to diagnose faults in the PMSM in a timely manner. In PMSM fault diagnosis methods, frequency domain features of the stator current are extensively used. However, these features change with the switching of motor operating conditions, leading to difficulty of diagnosis in multiple operating conditions. Therefore, a fault diagnosis method based on a convolutional neural network (CNN) phase tracker is proposed in this paper. Through phase tracking and angular domain resampling, the fundamental frequency of stator currents in different operating conditions are aligned, so as to fix the distribution of frequency domain features and solve the problem of features changing with operating conditions. Experimental results show that the proposed method can resample the stator current signals with a small error, detect faults in a relatively short time with high accuracy, and diagnose fault type and severity level under multiple operating conditions.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Condition PMSM Fault Diagnosis Based on Convolutional Neural Network Phase Tracker

et al. 2022

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“…When analyzing the designs of PMSM motors intended for electric vehicles, it is also necessary to focus on possible motor damage and motor durability. The number of publications concerning the diagnostics of electric vehicle drives proves that this topic is still important [39][40][41][42]. Most of those works focused on methods of machine diagnostics in terms of the occurrence of the phenomenon of permanent magnet demagnetization as a result of improper power supply, control, emergency short-circuits, and other unfavorable operating conditions.…”

Section: Study On Winding Short-circuit Faultmentioning

confidence: 99%

Influence of a Winding Short-Circuit Fault on Demagnetization Risk and Local Magnetic Forces in V-Shaped Interior PMSM with Distributed and Concentrated Winding

et al. 2021

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This paper presents a comparison of 30/8 and 12/8 AC permanent magnet motors with distributed (DW) and concentrated winding (CW) designed for electric vehicle traction. Both prototypes are based on an interior permanent magnet (IPM) motor topology and contain V-shape magnets. The radial flux AC IPM motors were designed for an 80 kW propulsion system to achieve 125 N·m. Finite element models (FEM) used to design the geometry of IPM motors and the required useful parameters of electric motors are widely investigated. The accuracy of finite element models is verified and validated on the basis of test data. Numerical simulations of healthy and faulty operation states, and studies of winding faults based on the FEM offer a deeper understanding of the associated phenomena. Therefore, in this paper, a short-circuit fault in a stator winding was simulated to investigate the transient currents under an external load collapse, for all winding phases. These simulations were used to define other important machine parameters to improve mechanical reliability of the motors and to assess the potential risk of permanent magnet (PM) demagnetization. Furthermore, the analysis of local magnetic forces affecting the PMs in the rotor and their possible displacement in a short-circuit situation were performed, also taking into account the centrifugal force. Lastly, it is demonstrated that the choice of winding configuration has a significant impact on the uncontrolled displacement of magnets in the rotor.

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“…This method has generated a proper fault diagnosis index, which can efficiently detect the defect. The carried-out work in [23] presented an analytical model of the DMF single-coil no-load EMF for PMSM. The method can not only carry out the detection and evaluation of the degree of DMF but also the recognition of the demagnetization mode and the positioning of the magnetic demagnetization pole.…”

Section: Introductionmentioning

confidence: 99%

Motor Current Signature Analysis-Based Permanent Magnet Synchronous Motor Demagnetization Characterization and Detection

et al. 2020

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Neodymium-boron (NdFeB) permanent magnets (PMs) have been widely studied in the past years since they became the material of choice in permanent magnet synchronous machines (PMSMs). Although NdFeB PMs have a better energy density than other types of magnets and are cost-effective, their magnetization is very sensitive to the PMSM operating conditions, in particular temperature, where the irreversible demagnetization degree increases over time. Therefore, it is important to characterize and diagnose demagnetization at an early stage. In this context, this paper proposes a two-step analysis study dealing with both uniform and partial demagnetization. A 2D finite element method-based (FEM) approach is used for demagnetization characterization, and then a PMSM motor current signature analysis (MCSA) approach, based on fast Fourier transform (FFT), is considered where fault cases harmonics are considered as faults indices to detect demagnetization. In some situations, the proposed two-step approach achieved results that clearly allow distinguishing and characterizing demagnetization. Indeed, a local demagnetization introduces specific sub-harmonics while a uniform demagnetization leads to the current amplitude increase for a given torque.

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Mode Recognition and Fault Positioning of Permanent Magnet Demagnetization for PMSM

Cited by 12 publications

References 14 publications

Multi-Condition PMSM Fault Diagnosis Based on Convolutional Neural Network Phase Tracker

Multi-Condition PMSM Fault Diagnosis Based on Convolutional Neural Network Phase Tracker

Influence of a Winding Short-Circuit Fault on Demagnetization Risk and Local Magnetic Forces in V-Shaped Interior PMSM with Distributed and Concentrated Winding

Motor Current Signature Analysis-Based Permanent Magnet Synchronous Motor Demagnetization Characterization and Detection

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