# Tipping point analysis of electrical resistance data with early warning signals of failure for predictive maintenance

## TL;DR

This paper introduces a novel tipping point analysis method for electrical resistance data, providing early failure warnings in electronic components, which enhances predictive maintenance capabilities in automotive and aviation industries.

## Contribution

The study applies a statistical physics framework to resistance time series, enabling earlier failure detection than traditional threshold-based methods.

## Key findings

- Early warning signals detected significantly before conventional methods
- Scaling properties of resistance data reveal critical transition points
- Applicable to various electromagnetic measurements in power systems

## 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 utilises the statistical physics framework with stochastic modelling by representing the measured 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 predictive maintenance of power electronics, with industrial applications. We suggest that this approach can be applied to various electromagnetic measurements in power systems and energy applications.