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Permanent link (DOI): https://doi.org/10.7939/R3FT52

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Data-Driven Fault Detection, Isolation and Identification of Rotating Machinery: with Applications to Pumps and Gearboxes Open Access

Descriptions

Other title
Subject/Keyword
Rotating Machinery
Fault Diagnosis
Fault Identification
Fault Detection
Fault Isolation
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Zhao, Xiaomin
Supervisor and department
Zuo, Ming J (Mechanical Engineering, University of Alberta)
Examining committee member and department
Ma, Yongsheng (Mechanical Engineering, University of Alberta)
Lange, Carlos (Mechanical Engineering, University of Alberta)
Lipsett, Mike (Mechanical Engineering, University of Alberta)
Jiang, Jin (Electrical and Computer Engineering, University of Western Ontario)
Pedrycz, Witold (Electrical and Computer Engineering, University of Alberta)
Department
Department of Mechanical Engineering
Specialization

Date accepted
2012-08-28T09:05:04Z
Graduation date
2012-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
Fault diagnosis plays an important role in the reliable operation of rotating machinery. Data-driven approaches for fault diagnosis rely purely on historical data. Depending on how a diagnosis decision is made, this thesis divides data-driven fault diagnosis approaches into two groups: signal-based approaches and machine-learning-based approaches. Signal-based approaches make diagnosis decisions directly using signal processing techniques. Machine-learning-based approaches resort to machine learning techniques for decision making. There are three main tasks in fault diagnosis: fault detection (detect the presence of a fault), fault isolation (isolate the location / type of the fault), and fault identification (identify the severity of the fault). This PhD research studies signal-based approaches for fault identification and machine-learning-based approaches for fault detection, isolation and identification. In signal-based approaches for fault identification, generating an indicator that monotonically changes with fault progression is a challenging issue. This thesis proposes two methods to generate such indicators. The first method uses multivariate signal processing techniques to integrate information from two sensors. The second method uses fuzzy preference based rough set and principal component analysis to integrate information from any number of sensors. In machine-learning-based approaches, feature selection is an important step because it improves the diagnosis results. For fault detection and isolation, classification is often used as the machine learning algorithm. In this thesis, a feature selection method based on neighbourhood rough sets is proposed for classification. Coming to fault identification, classification is not suitable because classification does not utilize the ordinal information within the different fault severity levels. Therefore, this thesis proposes to use another machine learning algorithm, ordinal ranking, for fault identification. A feature selection method based on correlation coefficient is proposed for ordinal ranking as well. Moreover, an integrated method which is capable of conducting fault detection, isolation and identification is proposed by combining classification and ordinal ranking. The proposed methods are applied to fault diagnosis of impellers in slurry pumps and fault diagnosis of gears in planetary gearboxes. Experimental results demonstrate the effectiveness of the proposed methods.
Language
English
DOI
doi:10.7939/R3FT52
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Xiaomin Zhao, Ming J Zuo and Ramin Moghaddass, Generating indicators for diagnosis of fault levels by integrating information from two or more sensors, Diagnostics and Prognostics of Engineering Systems, IGI Global, 2012 (in press)Xiaomin Zhao, Ming J Zuo, Zhiliang Liu and Mohammad Hoseini, Diagnosis of pitting damage levels of planet gears using ordinal ranking, Measurement, 2012 (in press)Xiaomin Zhao, Ming J Zuo and T. Patel, Generating an indicator for pump impeller damage levels using half and full spectra, fuzzy preference based rough sets, and PCA, Measurement Science and Technology, 2012, 23, 4, 1-10Xiaomin Zhao, Tejas Patel and Ming J Zuo, Multivariate EMD and full spectrum based condition monitoring for rotating machinery, Mechanical System and Signal Processing, 2011, 27, 712-728Xiaomin Zhao, Qinghua Hu, Y. Lei and Ming J Zuo, Vibration-based fault diagnosis of slurry pump impellers using neighbourhood rough set models, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2010, 224, 995-1006

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