A Data-Driven Maintenance Framework Incorporating Prognostics Distance and Prognostics and Health Management Methods

Prognostics and health management transform predictive maintenance by identifying which components need maintenance and when they should be performed. While there are several metrics to quantitatively assess the accuracy of remaining useful life predictions, fewer studies have explicitly modeled the economic benefits of implementing prognostics and health management, such as return on investment, cost reduction in the life cycle, and maintenance metrics driven by data over a period. We extend data-driven metrics from renewal theory, such as average cost per unit time, utilization per unit time, safety, and availability, rendering them suitable for application in the context of PHM methods. Furthermore, a comparative framework provides systematic evaluation and selection of PHM methods for multi-objective decision analysis. We explicitly decouple degradation models and include an unscented Kalman filter and particle filtering algorithms that iteratively update estimates of a model’s parameters. This decoupling approach enables direct comparison of alternative combinations of degradation models and PHM algorithms and a method to select a time horizon that balances tradeoffs between competing metrics according to stakeholder preference. The approach is applied to lithium-ion batteries. Based on data-driven maintenance metrics, the framework can be applied to select a combination of model and algorithm that balances tradeoffs between competing objectives such as cost and utilization. In addition, the framework is general and accommodates both existing and future degradation models and algorithms.