Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Using Magnetic Leakage Signals

Huang, Fengqin; Zhang, Xiaofei; Qin, Guanshi; Xie, Jinping; Peng, Jian; Huang, Shoudao; Long, Zhuo; Tang, Yao

doi:10.1109/tii.2022.3165283

Cited by 38 publications

(17 citation statements)

References 35 publications

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“…Implementing demagnetization faults is often carried out through partial removal of the magnets and substitution with non-magnetic materials or by adding weaker magnets [23], [24]. Nevertheless, these solutions are unable to mimic the local demagnetization that arises from thermal cycling during the operation of PMSM drives, particularly in traction applications such as electric vehicles.…”

Section: B Faults Implementationmentioning

confidence: 99%

Robust Multiple-Fault Diagnosis of PMSM Drives Under Variant Operations and Noisy Conditions

Mahmoud,

Huynh,

Senanyaka

et al. 2023

IEEE Open J. Ind. Electron. Soc.

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With the rapid development of industrial applications using permanent magnet synchronous drives (PMSM) and the Internet of Things, the demand for using robust fault diagnosis methods working in noisy conditions has increased significantly. The current data-driven methods depend mainly on deep learning (DL) models due to the effectiveness of automated feature extraction. However, these models have shallow depths compared with benchmark convolution neural networks, limiting their accuracy in final predictions, and they are established based on the hypothesis that the measured data is noiseless. Despite this, electric machinery is subjected to various noise sources that interfere with measurements during operation. This article proposes a new scheme combining a transfer-learned pre-trained residual neural network (ResNet) and supervised machine learning (S-ML) to enhance the performance of deep learning models in noisy industrial environments. The effectiveness of the proposed scheme is validated using an in-house setup of a PMSM drive with demagnetization and intern-short circuit faults at variant operating conditions. The results show that the proposed method significantly reduced the computational burden by tenfold on average while improving the average accuracy to 96.84 % across all the datasets compared with other DL and S-ML methods, with high robustness in noisy working conditions.INDEX TERMS Deep transfer learning, data-driven fault diagnosis, hybrid learning, permanent magnet synchronous motors (PMSMs), pre-trained deep models, wavelet analysis.

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Section: B Faults Implementationmentioning

confidence: 99%

Robust Multiple-Fault Diagnosis of PMSM Drives Under Variant Operations and Noisy Conditions

Mahmoud,

Huynh,

Senanyaka

et al. 2023

IEEE Open J. Ind. Electron. Soc.

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“…Until now, there are three kinds of DF diagnosis methods, namely signal processing-based [3][4][5][6][7][8][9], artificial intelligence (AI)-based [10][11][12] and model analysis-based [13][14][15]. For the signal processing-based method, fault diagnosis is achieved by extracting the fault characteristics from the fault signal using signal processing technique, which mainly includes fast Fourier transform, Hilbert-Huang transform, wavelet transform and empirical mode decomposition.…”

Section: Introductionmentioning

confidence: 99%

“…In ref. [12], a non‐contact DF diagnosis method using magnetic leakage signal based on wavelet scattering convolution network (WSCN) and semi‐supervised deep rule‐based (SSDRB) classifier is proposed. Although these methods have great advantages and robustness in dealing with complex problems, it takes up a lot of computing resources.…”

Section: Introductionmentioning

confidence: 99%

Enhanced robust diagnosis of multiple‐type demagnetisation fault for permanent magnet synchronous motor based on D‐axis magnetic network model using magnetic field reconstruction

He,

Hang,

Ding

2023

IET Electric Power Appl

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Demagnetisation fault (DF) is a common rotor fault in the permanent magnet synchronous motor (PMSM). DF can cause obvious changes in the magnetic field of PMSMs. As a result, previous DF diagnosis methods mainly depends on magnetic field analysis. However, conventional analysis methods based on the magnetic potential‐permeability method, sub‐domain method and magnetic equivalent circuit method are not suitable for DF condition. In addition, DF diagnosis is further complicated by various operating conditions. To address these issues, a robust DF diagnosis method is proposed for PMSM based on d‐axis magnetic network model using magnetic field reconstruction. The proposed fault diagnosis method employs magnetic field reconstruction to obtain the radial air‐gap flux density under the open‐circuit condition. Subsequently, a d‐axis magnetic network is established to solve the demagnetisation coefficient matrix in the DF state. Finally, the effectiveness of the proposed method is validated through simulations and experiments. Both results demonstrate that the proposed method can accurately recognise uniform DF and partial DF with multiple demagnetised permanent magnets, exhibiting great robustness against different operating conditions.

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“…Permanent magnet synchronous motors (PMSMs) have found widespread use in industrial production, electric vehicles, aerospace, and other industries thanks to the development of high-quality rare-earth permanent magnetic materials [1,2] and power electronics technology [3,4]. Generally, the use of PMs in electrical machines has some advantages, such as fewer losses, more torque (power) per volume, better dynamic performance, simple structure, and simple maintenance [5].…”

Section: Introductionmentioning

confidence: 99%

“…This method can achieve significant advantages if the operating point of the motor comes at the knee point of the demagnetization curve of the PM; otherwise, a large current is required to saturate the core, which limits the application scenarios of this approach. With the further improvement in computing power, many intelligent algorithm-based methods have been proposed for detecting demagnetization faults [3,12,35]. Although the detection accuracies of these methods are increasing, a large amount of data accumulation and calculation are necessary prerequisites for accurate detection.…”

Section: Introductionmentioning

confidence: 99%

A Period Energy Method for Demagnetization Detection in Surface Permanent Magnet Motors with Search Coils

Huang,

Chen,

et al. 2023

Electronics

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Irreversible demagnetization of permanent magnets (PMs) in PM synchronous motors (PMSMs) degrades the performance and efficiency of a machine and its drive system. There are numerous fault diagnosis methods for detecting demagnetization under steady-state conditions. However, only a few works could be found on fault diagnosis under dynamic conditions, whereas the dynamic operation of a motor is a very common scenario, e.g., electric vehicles. The voltage and current signal-based traditional fault detection method is not only affected by the structure of the motor, but it also becomes complicated to extract signals involving fault characteristics. Hence, this paper proposes a search coil-based online method for detecting demagnetization faults in PMSMs under dynamic conditions, which are not affected by the motor structure. To gather the flux of the stator tooth, flexible Printed circuit board (FPCB) search coils are positioned at the stator slot. The search coil is made up of two branches that are one pole apart and arranged in reverse sequence. In this installation option, the output signal in the fault state cannot be eliminated, and the output signal in the health state is zero. This paper defines only that characteristic value related to the position angle of the rotor. Further, the aim was to simultaneously eliminate the influence of elements like the search coil installation error and the inherent dispersion of the permanent magnet on the detection results. To characterize the fault degree, the measurement differential between the health state and the fault state is further integrated according to a predetermined angle range. Last but not least, speed-independent detection of individual permanent magnet demagnetization faults is possible using rotor position and stator tooth flux. A six-phase PMSM was used in experiments to show the efficiency of the suggested approach. The findings of the experiment demonstrate that the suggested strategy may precisely ascertain when a defect will occur.

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Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Using Magnetic Leakage Signals

Cited by 38 publications

References 35 publications

Robust Multiple-Fault Diagnosis of PMSM Drives Under Variant Operations and Noisy Conditions

Robust Multiple-Fault Diagnosis of PMSM Drives Under Variant Operations and Noisy Conditions

Enhanced robust diagnosis of multiple‐type demagnetisation fault for permanent magnet synchronous motor based on D‐axis magnetic network model using magnetic field reconstruction

A Period Energy Method for Demagnetization Detection in Surface Permanent Magnet Motors with Search Coils

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