Diagnosis of Mechanical Unbalance Fault in Permanent Magnet Synchronous Machine Drives

Zhang, Jianzhong; Hang, Jun; Cheng, Ming

doi:10.1080/15325008.2016.1169463

Cited by 18 publications

(13 citation statements)

References 24 publications

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“…The effectiveness of the unbalance detection is verified for a PMSM with modelled demagnetization and dynamic eccentricity. In [165] and [166], the stator current signal is analyzed, which, in order to eliminate the dominant fundamental component visible in the FFT analysis results, is subjected to the Park transform. The obtained signal was analyzed using DWT.…”

Section: Unbalance Faultsmentioning

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: Unbalance Faultsmentioning

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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“…By using Park's vector approach, the fundamental component is removed from the Fourier spectrum of the current, and then, fault components are analysed using DWT. Extracting the eccentricity fault index from PMSM has been carried out under non‐stationary conditions [86]. This passive index is on‐line, non‐invasive, and independent of machine parameters and has no need for an additional sensor.…”

Section: Eccentricity Fault Diagnosis Indices For Radial Flux Pm Mamentioning

confidence: 99%

Eccentricity fault diagnosis indices for permanent magnet machines: state‐of‐the‐art

Faiz

Nejadi-Koti

2019

IET Electric Power Applications

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Eccentricity fault with 10% severity is probably the only existing fault in a brand new healthy electrical machine that is acceptable as a manufacturing tolerance. This fault can be developed due to a continuous pull between stator and rotor, even in a de-energized machine. So, it must be diagnosed,and its severity monitored. The authors introduced and criticized various indices for eccentricity fault diagnosis. Advantages, drawbacks, and ambiguous points of each index and potential ground for improvement of these techniques are addressed. Moreover, different eccentric PM machine modelling techniques are reviewed and environmental factors affecting the eccentricity fault detection process are discussed. This survey covers all direct current and alternating current PM machines. It provides information about the researches performed since 1986 and it is helpful to newcomers to this field, artisans, and protection system designers. Considering the given information, the readers achieve enough understanding of the weaknesses and strong points of each eccentricity fault diagnosis index. Then, these indices are evaluated through assigning weight coefficients under various scenarios. Interested individuals can modify these weight coefficients based on the specifics of the application and decide which index is the most appropriate.

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“…It is necessary to determine the number of levels of decomposition for DWT to extract the features of fault. The number of levels is depended on the sampling period according to the equation below [37]:…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

“…Each high-frequency signal d i iincludes some information corresponded to the signal components. Indicating the sampling time period by T s , the frequency span, which this high-frequency signal covers, is obtained as below [37]:…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

See 1 more Smart Citation

Stator fault diagnosis of a BLDC motor based on discrete wavelet analysis using ADAMS simulation

Hosseini

Abedi

2019

SN Appl. Sci.

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In this paper, a method is considered to observe the stator inter-turn fault (SITF) in brushless DC motors (BLDCM). This is a crucial subject to deal with, since the fault may cause expensive replacement of parts in case of late diagnosis. The approach used here is the discrete wavelet transform, which is one of the many kinds of time-frequency analysis approaches. Taking advantage of a model closely related to a real BLDCM, the stator current is simulated and the aforesaid signal-based method is applied to it. The feature used as a parameter to determine whether SITF has occurred or not is the average change or deviation in the energy amount of four signals named high frequency (detail) signals. In other words, first, the difference percentage of the energy parameters between those four signals in healthy and faulty operation mode would be measured. Then, the average percentage of the energy variation amounts are to be compared with a threshold to determine the fault occurrence. Having a precise Simulink/Matlab plus ADAMS model of the motor, the designed algorithm will be validated.

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Diagnosis of Mechanical Unbalance Fault in Permanent Magnet Synchronous Machine Drives

Cited by 18 publications

References 24 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

Eccentricity fault diagnosis indices for permanent magnet machines: state‐of‐the‐art

Stator fault diagnosis of a BLDC motor based on discrete wavelet analysis using ADAMS simulation

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