A Neural/Fuzzy Approach for Motor Incipient Fault Detection

9521609周在上一个项目中，首席调查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故障检测映射的潜力。然而，在电机运行中存在着许多不确定因素，这些不确定性因素会对电机早期故障检测过程产生重大影响，而这些因素尚未得到解决。在这个拟议的项目中，首席调查员建议扩大研究范围，以涵盖更现实的运营环境。PI将调查不同的因素，如变化的负载条件、饱和效应、温度效应、噪声效应，以及它们对三相感应电机早期故障检测过程的影响。此外，PIS将使用一套理论公式来研究和建立电机早期故障检测的一般理论和原则。在集合论公式中建立问题后，挑战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。PIS将调查和演示使用神经网络和模糊逻辑技术从适当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、经济和可靠的方式提供准确的电机早期故障检测，并能够对故障检测过程提供定性和启发式的解释。***

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. ***