A Neural/Fuzzy Approach for Motor Incipient Fault Detection

电机初期故障检测的神经/模糊方法

基本信息

批准号：
9521609
负责人：
Mo-Yuen Chow
金额：
$ 19万
依托单位：
North Carolina State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
1995
资助国家：
美国
起止时间：
1995-08-01 至 1999-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9521609&HistoricalAwards=false
关键词：
Neural Fuzzy Approach Motor Incipient

项目摘要

9521609 Chow In the previous project, the Principal Investigator has demonstrated the potential of artificial neural networks to learn the complicated motor fault detection mappings for a single-phase induction motor under constant load conditions. However, there are many uncertainty factors in the motor operations that can significantly affect the motor incipient fault detection process and which have not been addressed. In this proposed project, the Principal Investigators propose to extend the research to cover more realistic operating environments. The PIs will investigate different factors such as varying load conditions, saturation effects, temperature effects, noise effects, and their influences on the process of motor incipient fault detection for three-phase induction motors. In addition, the PIs will investigate and establish a general theory and principle for motor incipient fault detection using a set theoretic formulation. After setting up the problem in set theoretic formulation, the challenges will lie in the finding of the correct mappings from the appropriate measurements to the estimation of the actual motor faults and their severity. The PIs will investigate and demonstrate the advantages and feasibility of the use of neural network and fuzzy logic technologies to obtain the motor fault mapping from appropriate information to yield accurate motor incipient fault detection in an non-invasive, economical, and reliably manner, along with the ability to provide a qualitative and heuristic explanation of the fault detection process. ***

9521609 Chow 在上一个项目中，首席研究员展示了人工神经网络在恒定负载条件下学习单相感应电机复杂的电机故障检测映射的潜力。然而，电机运行中存在许多不确定因素，这些因素可能会严重影响电机初期故障检测过程，但这些因素尚未得到解决。在这个拟议的项目中，主要研究人员建议将研究范围扩大到涵盖更现实的操作环境。 PI 将研究不同的因素，例如变化的负载条件、饱和效应、温度效应、噪声效应及其对三相感应电机早期故障检测过程的影响。此外，PI 将使用一组理论公式研究并建立电机初期故障检测的一般理论和原理。在集合论公式中提出问题后，挑战将在于找到从适当的测量到实际电机故障及其严重程度的估计的正确映射。 PI 将研究并展示使用神经网络和模糊逻辑技术从适当的信息中获取电机故障映射的优势和可行性，从而以非侵入性、经济且可靠的方式产生准确的电机早期故障检测，以及能够对故障检测过程提供定性和启发式解释。 ***