Tipping Point Analysis of Electrical Resistance Data with Early Warning Signals of Failure for Predictive Maintenance

Abstract

We apply tipping point analysis to measurements of electronic components commonly used in applications in the automotive or aviation industries and demonstrate early warning signals based on scaling properties of resistance time series. The analysis is based on a statistical physics framework with a stochastic model representing the system time series as a composition of deterministic and stochastic components estimated from measurements. The early warning signals are observed much earlier than those estimated from conventional techniques, such as threshold-based failure detection, or bulk estimates used in Weibull failure analysis. The introduced techniques may be useful for real-time predictive maintenance of power electronics, with industrial applications. We suggest that this approach can be applied to various electric measurements in power systems and energy applications.