Remaining Useful Life is the central problem of prognostics: given everything you know about a component right now, how much longer will it last? It’s the question on which condition-based maintenance, fleet scheduling, and warranty modeling all depend, and there’s a substantial gap between being able to define RUL crisply and actually estimating it well from real telemetry.
This primer focuses on the four main algorithm families that dominate the field. Each has its own theory of what “predicting failure time” means, and each has the kind of data and operating regime in which it tends to win.
Sits alongside the PHM monograph in your reading order — that one covered the conceptual frame; this one goes deep on the algorithms.
What it covers
Six sections, four live figures, about fifteen minutes to read.
§1 — Why the question is hard. What makes RUL different from regression. The censoring problem inherited from survival analysis. The drift between operating regimes. The fact that “failure” itself is often a threshold-crossing event, not a binary one.
§2 — Degradation and its threshold. The threshold-crossing framing. Live figure showing degradation trajectories and the moment they cross the failure threshold.
§3 — The similarity-based approach. Match the current trajectory against your historical library of run-to-failure curves. The remaining length of the best match is your RUL estimate. Why this works when you have rich historical data and why it doesn’t when you don’t.
§4 — Matching to history. Live figure showing similarity matching in action — drag the current trajectory, watch the algorithm find its closest historical match.
§5 — Windowed features and ML regression. Slide a window over the time series, compute features, train a regressor to predict RUL from the feature vector. The bread-and-butter approach when historical run-to-failure curves are scarce.
§6 — A model that counts down. Sequence models (LSTM, Transformer) trained to emit a decreasing RUL estimate at every timestep. When this beats windowed-feature regression, when it doesn’t, and what it costs to deploy.
Read it
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