Data Analysis for Condition Monitoring and Fault Detection in Induction Motors

3-三相感应电动机是阿尔伯塔许多工业的关键部分，因为它们是许多工业过程的成本有效工具，特别是在阿尔伯塔，因为石油和天然气、林业和石油化工工业。通常，工业部门60-80%的电力由感应电机消耗。因此，从经济和环境角度来看，感应电机的能源效率、可靠运行都是一个主要关注点。电机故障通过减少生产时间而产生显著的成本。因此，在阿尔伯塔工业中，对设备完整性进行大规模连续监测的潜在好处是显著的，包括：提高操作过程的效率;减少计划外停机时间;延长设备的使用寿命;增强对故障原因的理解。该项目是一个延续以前从事赠款相同的主题与一个新的重点数据测量和收集techniques适合自定义低成本的数据采集系统，而不是使用历史的工厂，在previousEngage赠款。该项目的目的是提供一个更具成本效益的手段，比现有的商业产品，更大规模的状态监测。这将是通过实施数据analysistechniques从实验室设备在给定的约束条件下收集的几个电气签名。本文的研究成果将为开发定制的低成本数据分析选择系统以及人工智能监测技术提供基础。本项目所开发的硬件和软件将为低成本大规模状态监测技术提供解决方案。

3-phase induction motors are a critical part of many Alberta industries as they are a cost effective tool for manyindustrial processes, especially relevant in Alberta because of the oil and gas, forestry and petro-chemicalindustries. Typically, 60-80% of electricity in the industrial sector is consumed by induction motors. Hence,energy efficiency reliable operation of induction motors are a major interest from both economic andenvironmental perspectives. Motors failures incur significant costs by decreasing production time. Therefore,the potential benefits in Alberta industries for large-scale, continuous monitoring of equipment integrity aresignificant, and including: increased efficiency in the operating process; reduction of un-planned downtimes;increased useful lifetime of equipment; enhanced understanding of failure causes. This project is a continuationof a previous Engage grant on the same topic with a new focus on data measurement and collection techniquessuitable for custom low cost data acquisition system rather than using historical plant as stated in the previousEngage grant. The intention of this project is to provide a more cost effective means, than existing commercialproducts, for condition monitoring on a larger scale. This will be accomplished by implementing data analysistechniques on several electrical signatures collected from laboratory equipment under given constraints. Theresults of this work will provide the foundation for both the development of a custom low cost data analysiscollection system, as well as the development of Artificial Intelligence monitoring techniques. It is the hopethat the hardware and software developed in this project will provide solutions to the problem of low cost largescale condition monitoring techniques.