Online condition monitoring of turbine pump stations

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One of the fundamental problems facing a wide range of industries is how to effectively identify a machinery defect before it reaches critical levels so as to avoid machinery performance degradation, malfunction, and even catastrophic failures. The current strategy in fault diagnosis is to periodically shut down the machine service for manual inspection. Often, no faults are detected during these routine examinations; consequently, the unnecessary downtimes may add significant costs to the operation of machines. The objective of this research project, in collaboration with the Bare Point Water Treatment Plant in Thunder Bay, Ontario, is to develop new technologies and intelligent tools for real-time condition monitoring of turbine pump stations. It aims to improve operation reliability and reduce maintenance cost. To achieve this goal, advanced research and development will be taken in an interdisciplinary field. Specifically, a new signal processing technique will be proposed for fault detection for rolling element bearings; a more accurate classifier will be developed for automatic fault diagnosis in electric motors and shafts; a new training technique will be suggested to improve classifier's adaptive capability to accommodate different machinery conditions; an intelligent platform will be developed for online condition monitoring of turbine pump workstations. This research will have the potential to change the traditional meaning of maintenance. The developed fault detection technologies and intelligent tools can also be applied in other Canadian industries for real-time machinery health condition monitoring and would help to improve production quality and operational safety, and reduce maintenance cost.