The aim of this research is to develop a novel machine learning approach for fault detection in power electronics circuit boards used in textile mills. Power electronics circuit boards play a critical role in the smooth operation of textile mills. However, the failure of these boards can cause significant downtime and loss of productivity, resulting in substantial financial losses for the textile industry. Therefore, developing an efficient and accurate fault detection system for power electronics circuit boards is of utmost importance. The proposed approach employs machine learning algorithms to detect faults in real-time and mitigate the risk of downtime. The approach utilizes several techniques, such as signal processing, feature extraction, and classification, to analyze the power electronics circuit board's behavior and detect faults before they cause any significant damage. The machine learning models used in the proposed approach are trained using a vast dataset of power electronics circuit board signals and fault data, which are collected from various textile mills. Additionally, the research will evaluate the proposed approach's ability to detect faults in real-time, which is crucial for minimizing downtime and maximizing productivity in textile mills. The outcomes of this research will benefit the textile industry by reducing downtime, minimizing production losses, and improving the overall efficiency and productivity of textile mills.