Application of Spectral and Wavelet Analysis of Stator Current to Detect Angular Misalignment in PMSM Drive Systems

Pietrzak, Przemysław; Wolkiewicz, Marcin

doi:10.2478/pead-2021-0004

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…Furthermore, in [171], the authors proposed a proportionalintegral resonant controller algorithm to suppress the periodic ripples of the speed signal caused by misalignment. In [172] several diagnostic methods based on stator current analysis are discussed: RMS, FFT and DWT analysis of the stator current. DWT of the stator current ENV and its space vector modulus were used to detect angular misalignment.…”

Section: Misalignment 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: Misalignment 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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“…Similarly, the fault-related frequency components of the problems produced by misalignments where the induction motor shaft takes part of the coupling have been defined. Thus, the misalignment problem in induction motors may be detected through a spectral analysis following Equation ( 5) [37]:…”

Section: Fault-related Frequency Components For Detecting Faults In I...mentioning

confidence: 99%

Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors

et al. 2022

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The incorporation of new technologies as training methods, such as virtual reality (VR), facilitates instruction when compared to traditional approaches, which have shown strong limitations in their ability to engage young students who have grown up in the smartphone culture of continuous entertainment. Moreover, not all educational centers or organizations are able to incorporate specialized labs or equipment for training and instruction. Using VR applications, it is possible to reproduce training programs with a high rate of similarity to real programs, filling the gap in traditional training. In addition, it reduces unnecessary investment and prevents economic losses, avoiding unnecessary damage to laboratory equipment. The contribution of this work focuses on the development of a VR-based teaching and training application for the condition-based maintenance of induction motors. The novelty of this research relies mainly on the use of natural interactions with the VR environment and the design’s optimization of the VR application in terms of the proposed teaching topics. The application is comprised of two training modules. The first module is focused on the main components of induction motors, the assembly of workbenches and familiarization with induction motor components. The second module employs motor current signature analysis (MCSA) to detect induction motor failures, such as broken rotor bars, misalignments, unbalances, and gradual wear on gear case teeth. Finally, the usability of this VR tool has been validated with both graduate and undergraduate students, assuring the suitability of this tool for: (1) learning basic knowledge and (2) training in practical skills related to the condition-based maintenance of induction motors.

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Detection of Misalignment in a Synchronous Reluctance Machine via Transient Analysis of Current Signals

Navarro-Navarro,

Biot-Monterde,

Ruiz-Sarrió

et al. 2023

2023 IEEE Energy Conversion Congress and Exposition (ECCE)

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Application of Spectral and Wavelet Analysis of Stator Current to Detect Angular Misalignment in PMSM Drive Systems

Cited by 5 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

Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors

Detection of Misalignment in a Synchronous Reluctance Machine via Transient Analysis of Current Signals

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