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Faults and Fault Detection Methods in Electric Drives

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Soft Computing in Condition Monitoring and Diagnostics of Electrical and Mechanical Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1096))

Abstract

The chapter presents a review of faults and fault detection methods in electric drives. Typical faults are presented that arises for the induction motor, which is valued in the industry for its robust construction and cost-effective production. Moreover, a summary is presented of detectable faults in conjunction with the required physical information that allows detection of specific faults. In order to address faults of a complete drive system, characteristic failures of its mechanical parts are presented as well. Furthermore, the physical forces, which arise during specific faults (i.e. centrifugal, kinematic) are presented along with dominant harmonics in the frequency spectrum. These dominant harmonics are especially important for the determination of a malfunction of the drive. The detection of the particular could be performed with signal processing methods which are tabularly summarized for introduction purposes. In order to cover a further industry interest, a cost-effectiveness relation is presented, which describes whether a diagnostic system is appreciative or not. Moreover, most important international standards regarding safety, health for human and machinery are summarized that are required to be fulfilled in every industrial application. The next subsection is dedicated to the presentation of fault detection methods that include a review of conventional methods for monitoring the conditions of the electric machine. That includes the monitoring of variables that are based on electrical, chemical, mechanical and thermal changes in the induction motor. The last subsection considers the fault detection methods, which are based on utilization of mathematical models. In this kind of fault detection, the model description is utilized to identify changes in the drive system, which then can be used for a potential fault identification procedure. Such mathematical constructs are mainly based on observer, Kalman filters or neural networks. The chapter is concluded with a short summary of the presented sections.

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Abbreviations

MRAS:

Model Reference Adaptive System

FEM:

Finite Element Method

XRF:

X- Ray Fluorescence

MCSA:

Motor Current Signature Analysis

PD:

Power Density

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Acknowledgements

This work was supported by the National Science Centre of Poland grant no. 2015/19/N/ST7/03078.

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Authors and Affiliations

  1. Faculty of Electrical and Control Engineering, Gdańsk University of Technology, Narutowicza 11/12, 82-900, Gdańsk, Poland

    Patrick D. Strankowski & Jarosław Guziński

Authors
  1. Patrick D. Strankowski
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  2. Jarosław Guziński
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Correspondence to Patrick D. Strankowski .

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Editors and Affiliations

  1. Department of Instrumentation and Control Engineering (ICE), NSUT, Delhi, India

    Hasmat Malik

  2. Department of Electrical Engineering, Qatar University, Doha, Qatar

    Atif Iqbal

  3. Department of Electrical and Electronics Engineering (EEE), National Institute of Technology Sikkim, Sikkim, India

    Amit Kumar Yadav

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Strankowski, P.D., Guziński, J. (2020). Faults and Fault Detection Methods in Electric Drives. In: Malik, H., Iqbal, A., Yadav, A. (eds) Soft Computing in Condition Monitoring and Diagnostics of Electrical and Mechanical Systems. Advances in Intelligent Systems and Computing, vol 1096. Springer, Singapore. https://doi.org/10.1007/978-981-15-1532-3_2

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