A smart monitor for pump system fault diagnostics

如何在机械缺陷达到临界水平之前有效地识别机械缺陷，以避免机械性能下降、故障甚至灾难性故障，是众多行业面临的基本问题之一。目前的故障诊断策略是定期停止机器服务进行人工检测。通常，这些例行检查可能会导致不必要的停机时间，这将大大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湾的Bare Point水处理厂合作，开发用于远程诊断泵站的新技术和智能监测器。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，将在多学科领域进行先进的研究和开发，包括机械系统建模、信号处理、人工智能、电子和机电一体化。具体而言，将开发一种新的通用传感器接口和智能传感器，用于振动、电流和温度信号的远程数据采集；将提出新的信号处理技术用于电机和泵的故障检测；为实现水泵故障的自动诊断，将开发更精确的分类器；将提出新的训练算法，以提高分类器的收敛性和适应能力，以适应不同的机械条件；开发用于电机和泵工作站在线状态监测的智能监测平台。开发的智能传感器、故障检测技术和智能工具也可以应用于加拿大其他行业，实时监测机械健康状态，帮助相关企业提高生产质量和操作安全，同时降低维护成本。

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, these routine examinations may lead to unnecessary downtime, which will 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 a smart monitor for remote diagnostics of pump stations. It aims to improve operation reliability and reduce maintenance costs. To achieve this goal, advanced research and development will be undertaken in multidisciplinary fields including machinery system modeling, signal processing, artificial intelligence, electronics and mechatronics. Specifically, a new universal transducer interface and smart sensors will be developed for remote data acquisition of vibration, current and temperature signals; new signal processing techniques will be proposed for fault detection in electric motors and pumps; a more accurate classifier will be developed for automatic fault diagnosis in water pumps; new training algorithms will be proposed to improve classifier's convergence and adaptive capability to accommodate different machinery conditions; a smart monitor platform will be developed for online condition monitoring of motors and pump workstations. The developed smart sensors, fault detection technologies and intelligent tools can also be applied in other Canadian industries for real-time machinery health condition monitoring, which will help the related companies improve production quality and operational safety, but reduce maintenance costs.