Intelligent Fault Detection in Electronic Circuits Using AI for Industry 4.0

Electrical faults like short circuits, open circuits, fouling, and scaling pose significant challenges inIndia’s power distribution systems, leading to frequent outages and safety hazards. With over 1.4billion people depending on electricity, power interruptions cost billions annually. The objective is todevelop an efficient machine learning-based fault detection system to accurately classify electricalfaults Traditional manual fault detection relies on periodic physical inspections by technicians whovisually or electrically check equipment for faults. This process includes using handheld meters,visual observation, and trial-and-error testing to identify fault types and locations. It is labor-intensive,slow, and often inaccurate due to human error and environmental conditions, leading to delayedrepairs and increased system downtime. The traditional fault detection system suffers from slowresponse times and limited accuracy due to reliance on manual inspections. Human errors, lack ofreal-time monitoring, and insufficient data analysis capability cause frequent misdiagnosis of faults,resulting in prolonged outages and higher maintenance costs. The motivation behind this research isto overcome the traditional system’s slow, error-prone fault detection by leveraging machine learning.The proposed system uses a Support Vector Classifier (SVC) to automatically classify electrical faultsinto categories: 'Short_circuit', 'Nominal', 'Open_circuit', 'Fouling', and 'Scaling'. The machinelearning model processes sensor data and identifies subtle patterns distinguishing each fault type withhigh accuracy. This automation accelerates fault detection and minimizes human error, enablingprompt maintenance actions. Additionally, SVC’s kernel functions allow it to handle complexnonlinear relationships in the data, improving diagnostic reliability. By continuously learning fromnew data, the system adapts to evolving conditions, offering a scalable and robust solution to enhancepower system stability and reduce outage durations