Fault Diagnosis in V94.2 Power Plant Cooling System Fans Based on Fuzzy-Neural Systems in Real Environment

In this study, Fault Diagnosis in V94.2 Power Plant Cooling System Fans Based on Fuzzy-Neural Systems in Real Environment was investigated. According to the mentioned results, one of the important points in various industries, especially in power plants, is the importance of having an intelligent fault-finding system can be mentioned, and since the presented ANFIS system gives acceptable output and results in comparison with other neural networks, it can be used as a suitable method for automatic fault detection. Understanding the frequencies associated with each component of the rotating apparatus is a fundamental requirement for success in this troubleshooting endeavor. Based on the examination of the findings of this study, it is possible to deduce that the ANFIS network provides a precise approximation of the network output data compared to the true output values, and the error margin in this network is relatively small in most instances. Furthermore, the specific flaw can be readily discerned. One of the most important issues related to the operation of power plant units is the timely and accurate diagnosis of rotating equipment defects. Therefore, any action to accurately and quickly identify defects can play an important role in providing stable electricity in the country. In this research, first, the vibration data of the cooling fans of the Qayen power plant were extracted using a vibrometer, and then in the next step, the ANFIS system was used as a fault diagnosis system. The improvement and diagnosis of mechanical defects in this project reached an average of 95%.