Remaining Useful Life
Remaining Useful Life (RUL) is the estimated time or number of operating cycles an asset can continue to function before reaching a defined failure threshold, measured forward from the current moment. It is the core output of predictive maintenance programs using sensor data, degradation models, or machine learning to forecast intervention timing.
In shop floor maintenance, RUL transforms condition monitoring data into a concrete time horizon for maintenance planning, parts procurement, and shutdown scheduling within CMMS systems like ServiceGrid. Work orders are triggered when RUL drops to a defined intervention threshold, allowing repairs to be planned rather than reactive. RUL is estimated via three method categories: model-based (physics), data-driven, or hybrid, with selection depending on accuracy requirements and data availability. For non-linear/non-Gaussian systems, State Space Model methods with Bayesian estimation perform recursive online RUL assessment for condition-based maintenance and prognostics and health management.
How is RUL mathematically defined in PHM?
RUL = T - t, where T is the failure time, t is current time, and M(t) represents all status information (conditions, environment, pressure, temperature, humidity) within [t, T].
What distinguishes Mean Remaining Useful Life (MRUL) from point-estimate RUL?
MRUL is a statistical index evaluating maintenance value for PM decisions, representing the expected average remaining life rather than a deterministic point estimate.
Which method is preferred for hydraulic pumps with unknown degradation models and no failure data?
A method using limited degradation data with stochastic modeling and particle filtering (Monte Carlo) provides robust RUL estimation for such cases.