A smart monitor for pump system fault diagnostics

用于泵系统故障诊断的智能监视器

• 批准号: 485762-2015
• 负责人: Wang, WilsonQuansheng
• 金额: $ 2.91万
• 依托单位: Lakehead University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=585192
• 关键词: smart; monitor; pump; system; fault

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.

广泛的行业所面临的基本问题之一是如何在机器缺陷达到临界水平之前有效地识别机器缺陷，以避免机器性能下降、故障甚至灾难性故障。目前的故障诊断策略是定期关闭机器服务进行人工检查。通常，这些例行检查可能会导致不必要的停机时间，这将大大增加机器运行的成本。该研究项目的目标是与安大略雷霆湾的Bare Point水处理厂合作，开发新技术和智能监控器，用于泵站的远程诊断。它旨在提高运行可靠性并降低维护成本。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，用于电动机和水泵的故障检测;将开发一种更精确的分类器，用于水泵的自动故障诊断;提出新的训练算法，提高分类器的收敛性和适应能力，以适应不同的机械条件;将开发一个智能监测平台，用于电机和泵工作站的在线状态监测。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，进行实时的机械健康状况监测，这将有助于相关公司提高生产质量和运营安全性，同时降低维护成本。