A hybrid diagnosis method for inverter open-circuit faults in PMSM drives

Zhang, Zeliang; Luo, Guangzhao; Zhengbin, Zhang; Tao, Xiaoming

doi:10.30941/cestems.2020.00023

Cited by 27 publications

(8 citation statements)

References 17 publications

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“…Transistors' abnormalities result mainly from aging changes, the speed of which may increase significantly with the increase of the intensity of the drive operation, i.e. its frequent overloads [19], [243], [244].…”

Section: Fault Detection Of Pmsm Drive Components and Fault-tolerant ...mentioning

confidence: 99%

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

et al. 2022

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The issues of monitoring and fault diagnosis of drives with permanent magnet synchronous motors (PMSM) are currently very topical due to the increasing use of these drives in safety-critical devices. Every year, more and more articles on this subject are published. Therefore, the aim of this article is to update the overview of diagnostic methods and techniques for PMSM drives. Each of the main chapters of the article focuses on a specific element of the drive system (motor, power converter, measuring sensors), with particular emphasis on the components of the motor (stator windings, magnets, bearings, rotor). The main sections on PMSM fault diagnosis are divided according to the type of methods used to obtain the symptoms of the damage. In addition, a review of methods using the analysis of control structure signals for the diagnosis of damage to a vector-controlled motor is presented, as well as the latest achievements of researchers in the field of shallow and deep neural networks for the detection and classification of PMSM drives failures. Based on the presented literature analyses, some development trends and challenges related to the development of diagnostics and fault-tolerant control of PMSM drives are discussed in the conclusion part.

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Section: Fault Detection Of Pmsm Drive Components and Fault-tolerant ...mentioning

confidence: 99%

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

et al. 2022

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“…It is widely used in various industries and fields such as aerospace, power conversion, and industrial automation [2]. As its core driving unit, the inverter has the advantages of large output capacity, low switching device stress, low harmonic content of output voltage, and easy modular control [3,4]. The wide application of inverters in motor drive systems makes their reliability and safety monitoring a primary concern [5].…”

Section: Introductionmentioning

confidence: 99%

Inverter open circuit fault diagnosis based on residual performance evaluation

Sun,

Chen,

Wang

et al. 2023

IET Power Electronics

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Because of uncertain random noise and unknown disturbance, the observer‐based inverter fault diagnosis meets the problems of large deviation of current observation value and high false alarm rate. An inverter open‐circuit fault diagnosis method is proposed via residual performance evaluation. Firstly, the interval sliding mode observer is constructed by combining the interval observer with the sliding mode observer. Three phase current estimates are obtained and current residuals are calculated. Secondly, the residual performance indicators are calculated using the evaluation function. The fault detection is derived by comparing residual performance with the threshold. Thirdly, a residual information table is established based on the fault information contained in the residual for fault isolation. Finally, the proposed algorithm is validated on simulation and physical platforms. Compared with traditional observer‐based diagnostic methods, the introduction of residual performance evaluation improves the diagnostic speed by more than 50%. The interval sliding mode observer has higher estimation accuracy and anti‐interference ability in the presence of noise and external disturbances.

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“…Consequently, numerous machine and deep learning-based techniques are employed for accurate classification of faults [18]. Principal component analysis and support vector machine are used to classify faults using current residuals in PMSM [19]. Wavelet packet along with 1-D convolutional neural network was used to classify three types of faults in PMSM [20].…”

Section: Introductionmentioning

confidence: 99%

Detection and Separation of Faults in Permanent Magnet Synchronous Machines using Hybrid Fault-Signatures

Ullah

Ahmed

2022

2022 IEEE Energy Conversion Congress and Exposition (ECCE)

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As digitalization in electric motors accelerates, online condition monitoring systems are becoming more popular, allowing unplanned downtime to be detected at its initial stage. Individual faults in motors are effectively diagnosed. However, due to identical signatures, fault separation and/or identification remain a challenge. This study presents the detection and separation of inter-turn short, demagnetization, static eccentricity, bearing, and the combination of these faults in permanent magnet synchronous machines. Hybrid fault signatures are used by monitoring the frequency spectrum of stator current, vibration, and induced voltage in the airgap. A planer-shaped airgap search coil (PASC) is employed to extract the induced voltage of each stator tooth. Faults-related anomalies in the induced-voltage, vibration, and the stator current caused are monitored. Any deviation in either signal is considered as generic fault indicator. Furthermore, specific fault features in all signals are used to classify these faults with improved accuracy. The PASC used in this study can also identify the location of the inter-turn short fault and the severity of demagnetization fault. The proposed method is verified using the finite element method simulation and experiments.

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A hybrid diagnosis method for inverter open-circuit faults in PMSM drives

Cited by 27 publications

References 17 publications

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

Inverter open circuit fault diagnosis based on residual performance evaluation

Detection and Separation of Faults in Permanent Magnet Synchronous Machines using Hybrid Fault-Signatures

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