Predictive Maintenance of Electrical Connectors

Predictive maintenance of electrical connectors encompasses strategies that forecast failure and schedule interventions before critical breakdowns occur. Electrical connectors, from low- and medium-voltage power joints to relay contacts in transport and industrial systems, degrade over time through mechanisms such as contact wear, oxidation and thermal cycling. Modern approaches rely on continuous or periodic sensing of key indicators—most notably electrical resistance—whose gradual increase signals the onset of degradation. Data-driven models then translate these measurements into estimates of a connector’s remaining useful life or state of health, integrating methods from statistical time-series analysis to machine learning and deep neural networks. Advances in sensor miniaturisation, Internet of Things connectivity and edge computing have enabled remote, real-time monitoring in harsh environments. Finite-element simulations and laboratory ageing tests underpin the physical understanding of failure modes, while prognostic algorithms leverage both simulated and field data. The result is a global shift towards condition-based upkeep in power distribution, electric vehicle systems, rail networks and industrial automation, reducing unscheduled downtime, enhancing safety margins and optimising lifecycle costs.

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Predictive Maintenance of Electrical Connectors publication trend

The graph below shows the total number of articles in predictive maintenance of electrical connectors across all publications each year (not limited to Nature Index journals).

Technical terms

Predictive maintenance: A strategy that uses condition monitoring and data analysis to forecast equipment failures and schedule timely repairs.

Remaining useful life (RUL): The estimated duration during which a component continues to perform within acceptable limits.

State of health (SoH): A metric expressing the current condition of a component relative to its original, ideal performance.

Autoregressive integrated moving average (ARIMA): A statistical time-series model used to analyse and forecast trends in sequential data.

Electrical resistance: A measure of opposition to current flow; its progressive increase can signal connector degradation.

Internet of Things (IoT): A network of interconnected sensors and devices enabling remote monitoring and data exchange.

References

1. Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans. Applied Sciences (2020).
2. State of Health Prediction of Power Connectors by Analyzing the Degradation Trajectory of the Electrical Resistance. Electronics (2021).
3. On-Line Remaining Useful Life Estimation of Power Connectors Focused on Predictive Maintenance. Sensors (2021).