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On Fault Detection, Diagnosis and Monitoring for Induction Motors
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In this thesis, multiple methods and different approaches have been established and evaluated successfully, in order to detect and diagnose the faults of induction motors (IMs). The aim of this thesis is to present novel fault detection and isolation methods for the case of induction machines that would have the merit to be implemented online and being characterized by specific novel capabilities, when compared with the existing techniques. More specifically both the cases of model based and modeless (model free) fault detection and isolation methods will be considered. The proposed methods have been based on: a) Set Membership Identification, Uncertainty Bounds Violation and a minimum uncertainty boundary violation detection schemes, for multiple cases of broken bars under different load conditions and short circuits in stator windings detection having the merit of exact and fast fault detection an easy straight forward fault isolation and capabilities, b) model based Support Vector Classification for the detection of broken bars under full load conditions, using features based on the spectral analysis of the steady state stator's current, without the need of training steps (an expensive, time consuming and often practically infeasible task) and existing of a priori data sets of healthy and faulty cases, c) fault classification based on robust linear discrimination scheme in the model free case and based on novel extracted features for both short circuit and broken bar, and d) fault detection based on Principal Component Analysis (PCA) fault/anomaly detector in time domain for detecting broken rotor bars under full load conditions, e) fault classification technique for bearings based on a novel Minimum Volume Ellipsoid method for feature extraction. One of additional major contributions of this thesis is the fact that especially for the cases of broken bars, and short circuit in stator windings. All the proposed methodologies have been extensively evaluated in multiple experiments and in multiple payloads and thus it has been realistically demonstrated the merits of all the proposed fault detection and isolation schemes. Furthermore, the obtained results suggest that these novel representations can be used within condition monitoring systems.

Place, publisher, year, edition, pages
Luleå tekniska universitet, 2015.
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keyword [en]
Model based fault detection and diagnosis, Model free fault detection, Set Membership Identification (SMI), Uncertainty Bounds Violation Method, Model based Support Vector Classification, Linear and Nonlinear Classification, Minimum Volume Ellipsoid method, Broken Rotor Bars, Faults of Induction Motors, Three phase induction motor, Information technology - Automatic control
Keyword [sv]
Informationsteknik - Reglerteknik
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-18781Local ID: a4cf3616-c181-4543-b7b8-46c69a0992b8ISBN: 978-91-7583-226-5ISBN: 978-91-7583-227-2 (PDF)OAI: oai:DiVA.org:ltu-18781DiVA: diva2:991792
Note
Godkänd; 2015; 20150120 (mohoba); Tillkännagivande disputation 2015-02-24 Nedanstående person kommer att disputera för avläggande av teknologie doktorsexamen. Namn: Mohammed Obaid Mustafa Ämne: Reglerteknik/Automatic Control Avhandling: On Fault Detection, Diagnosis and Monitoring for Induction Motors Opponent: Professor Mohamed El Hachemi Benbouzid, Laboratoire Brestois de Mécanique et des Systèmes, University of Brest, Brest, France Ordförande: Professor Thomas Gustafsson, Avd för signaler och system, Institutionen för system- och rymdteknik, Luleå tekniska universitet Tid: Tisdag den 17 mars kl 09.30 Plats: A109, Luleå tekniska universitetAvailable from: 2016-09-29 Created: 2016-09-29Bibliographically approved

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