Online condition monitoring of turbine pump stations

如何在机械故障达到临界水平之前有效地识别机械故障，从而避免机械性能退化、故障甚至灾难性故障，是众多行业面临的基本问题之一。当前的故障诊断策略是定期关闭机器服务以进行人工检查。通常，在这些例行检查中没有检测到故障；因此，不必要的停机时间可能会显著增加机器的运行成本。该研究项目的目标是与安大略省桑德贝的Bare Point水处理厂合作，开发用于涡轮机泵站实时状态监控的新技术和智能工具。其目的是提高运行可靠性，降低维护成本。为了实现这一目标，将在跨学科领域进行先进的研究和开发。具体来说，将提出一种新的信号处理技术用于滚动轴承的故障检测；将开发一种更准确的分类器用于电机和轴的自动故障诊断；将提出一种新的训练技术以提高分类器的适应不同机械条件的能力；将开发一个用于涡轮泵工作站在线状态监测的智能平台。这项研究将有可能改变维修的传统含义。开发的故障检测技术和智能工具也可以应用于加拿大其他行业的实时机械健康状态监测，有助于提高生产质量和操作安全，降低维护成本。

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.