PhD Defense - Kayacan Kestel

Development of signal processing techniques for vibration-based condition monitoring of industrial rotating machines

This dissertation presents innovative signal processing techniques for improving vibration-based condition monitoring of complex industrial rotating machines. Current methods often struggle with real-world signals and lack robustness. The study addresses these limitations by enhancing existing signal processing methods in the literature or proposing new ones. One of the contributions of this thesis is enhancing signal filtering optimization techniques by exploiting the engineering knowledge of the machine. As a result of the proposed improvement, fault detection is achieved on very complex vibration signals. Furthermore, condition indicators utilized to assess the health status of rotating machines are widely discussed. The utilization of several condition indicators recently introduced to the literature is extensively discussed, enhancements for their effective usage are proposed, and such indicators are merged with signal filtering optimization techniques for early fault detection. In addition, this study proposes a new framework to generate new condition indicators that are optimal for early fault detection and their statistical threshold to alarm the end-user for a potential machine fault. Such a framework enables not only the generation of novel indicators but also the recovery of the health indicators actively employed in the field, which explains why they were introduced to the vibration-based condition monitoring domain in the first place. The study finalizes with a discussion on how informative two spectral correlation-based indicators in terms of the severity of a bearing fault in time. The trending ability of two indicators is tested on simulated signals to explain their performances.