Date of Award
Spring 2013
Degree Type
Thesis
Degree Name
Master of Applied Science (MASc)
Department
Mechanical Engineering
Supervisor
Saeid Habibi
Co-Supervisor
Samir Ziada
Language
English
Abstract
Condition monitoring and fault diagnosis of mechanical systems are two important issues that have received considerable attention from both academia and industry. Several techniques have been developed to date to address these issues. One category of these techniques which has been successfully applied in many industrial plants is based on the multiresolution multivariate analysis algorithms and more specifically the multi-scale principal component analysis (MSPCA). The present research aims to develop a multi-resolution multivariate analysis technique which can be effectively used for fault diagnosis of an internal combustion engine. Crank Angle Domain (CAD) Analysis is the most intuitive strategy for monitoring internal combustion engines. \comment{ as a cyclic system in which events at each cycle is correlated to a particular position of the crankshaft, this leads to analyzing the engine performance in angle domain (i.e. Crank Angle domain for engine) as very logical and intuitive strategy.} Therefore, MSPCA and CAD analysis were combined and a new technique, named CAD-MSPCA, was developed. In addition to this contribution, two indices were defined based on estimation of covariance matrices of scores and fault matrices. These indices were then employed for both fault localization and isolation purposes. In addition to this development, an interesting discovery made through this research was to use the statistical indices , calculated by MSPCA, for fault identification. It is mathematically shown that in case these indices detect a fault in the system, one can determine the spectral characteristics of the fault by performing the spectrum analysis of these indices. This analysis demonstrated the MSPCA as an attractive and reliable alternative for bearing fault diagnosis. These new contributions were validated through simulation examples as well as real measurement data.
Recommended Citation
Haqshenas, Seyyed Reza, "MULTIRESOLUTION-MULTIVARIATE ANALYSIS OF VIBRATION SIGNALS; APPLICATION IN FAULT DIAGNOSIS OF INTERNAL COMBUSTION ENGINES" (2013). Open Access Dissertations and Theses. Paper 7602.
http://digitalcommons.mcmaster.ca/opendissertations/7602
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