Fault Prediction Using Fuzzy Convolution Neural Network on IOT Environment with Heterogeneous Sensing Data Fusion

Abstract

Because of the developing worldwide familiarity with natural issues, the expansion of sun-based power plants has turned into an unmistakable element of the energy scene. Nonetheless, keeping up with these sunlights-based offices, especially with regards to recognizing breaking down photovoltaic (PV) cells inside huge scope or far off establishments, presents critical difficulties. The focal goal of our exploration project is to resolve this issue by empowering the convenient identification of flaws in PV cells, consequently possibly saving significant time, exertion, and upkeep costs, especially as for the pivoting gear ordinarily utilized in sun-based power plants. I have developed a non-contact vibration pickup system that makes it possible to collect vibration data from PV cells operating at various speeds and loads without having to physically connect them to machine tools. In addition, I rank and select the most relevant features for accurate fault detection using the Sequential Floating Forward Selection (SFFS) method and Principal Component Analysis (PCA) to reduce the extracted features dimensionality. This thorough methodology offers a promising answer for improve the effectiveness and dependability of sun-oriented power plant upkeep while adding to the more extensive objectives of supportable energy creation and natural safeguarding.