Fault Diagnosis and Prognosis in Industrial Systems Using Machine Learning Techniques

Date

2018-05

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Abstract

This dissertation concerns the development and usage of advanced machine learning and signal processing methods for fault diagnosis and prognosis in industrial systems. It establishes a mathematical framework for detecting and predicting faults in industrial systems.

Description

Keywords

Machine learning, Signal processing, Bayesian statistical decision theory, Wavelets (Mathematics)

Citation

URI

https://hdl.handle.net/2249.1/157294

Collections

University of Texas at Dallas