AI-Driven Condition Monitoring and Fault Detection in Electrical Power and Industrial Control Systems

Abstract

This study examined intelligent condition monitoring and fault diagnosis in electrical power and control systems using a quantitative predictive analytics framework grounded in machine learning and construct-based modeling. A total of 268 responses were collected from professionals working in power distribution, industrial control, and power electronics environments, and after data screening 241 responses were retained for analysis, representing an 89.9% retention rate. The final sample was dominated by engineers (38.6%) and maintenance specialists (27.0%), with 73.9% reporting daily interaction with condition monitoring tools. Five constructs were evaluated: Condition Monitoring Effectiveness, Fault Detection Accuracy, Predictive Maintenance Capability, System Integration Quality, and Operational Performance Impact. Descriptive results indicated generally high perceptions of monitoring and diagnostic performance, with construct means ranging from 3.62 to 4.08 on a five-point scale and moderate dispersion (SD range = 0.58–0.72). Distribution diagnostics supported normality, with skewness values between −0.48 and −0.21 and kurtosis between −0.37 and 0.42. Internal consistency reliability was strong across constructs, with Cronbach’s alpha values ranging from .81 to .88 and two items removed during refinement due to weak item-total contribution. Multiple regression analysis was performed to evaluate the predictive influence of the independent constructs on Operational Performance Impact. The regression model was statistically significant, F(4, 236) = 67.84, p < .001, and explained 53.5% of the variance in the dependent construct (R² = .535; adjusted R² = .527), with independence of errors supported (Durbin–Watson = 1.94). Predictive Maintenance Capability demonstrated the strongest effect (β = .34, p < .001), followed by Condition Monitoring Effectiveness (β = .26, p < .001) and System Integration Quality (β = .19, p = .002), while Fault Detection Accuracy produced a smaller but significant contribution (β = .14, p = .018). Multicollinearity was not problematic (VIF range = 1.31–1.58). Overall, the findings confirmed that operational performance outcomes were significantly associated with the effectiveness of monitoring, diagnostic accuracy, predictive maintenance capability, and integration quality, providing quantitative evidence that structured machine learning–driven condition monitoring systems contribute meaningfully to measurable operational performance improvement in electrical power and control environments.