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Predict Remaining Useful Life

Predict RUL using specialized models designed for computing RUL from system data, state estimators, or identified models

Typically, you estimate the remaining useful life (RUL) of a system by developing a model that can perform the estimation based on the time evolution or statistical properties of condition indicator values. Predictions from such models are statistical estimates with associated uncertainty. They provide a probability distribution of the RUL of the test machine.

The model you use can be a dynamic model such as those you obtain using System Identification Toolbox™ commands. Predictive Maintenance Toolbox™ also includes some specialized models designed for computing RUL from different types of measured system data. For an overview of the types of models you can use, see Models for Predicting Remaining Useful Life.

Developing a model for RUL prediction is the next step in the algorithm-design process after identifying promising condition indicators. Because the model you develop uses the time evolution of condition indicator values to predict RUL, this step is often iterative with the step of identifying condition indicators.


expand all

monotonicityQuantify monotonic trend in condition indicators
prognosabilityMeasure of variability of condition indicators at failure
trendabilityMeasure of similarity between trajectories of condition indicators
exponentialDegradationModelExponential degradation model for estimating remaining useful life
linearDegradationModelLinear degradation model for estimating remaining useful life
hashSimilarityModelHashed-feature similarity model for estimating remaining useful life
pairwiseSimilarityModelPairwise comparison-based similarity model for estimating remaining useful life
residualSimilarityModelResidual comparison-based similarity model for estimating remaining useful life
covariateSurvivalModelProportional hazard survival model for estimating remaining useful life
reliabilitySurvivalModelProbabilistic failure-time model for estimating remaining useful life
predictRULEstimate remaining useful life for a test component
compareCompare test data to historical data ensemble for similarity models
fitEstimate parameters of remaining useful life model using historical data
plotPlot survivor function for covariate survival remaining useful life model
restartReset remaining useful life degradation model
updateUpdate posterior parameter distribution of degradation remaining useful life model


RUL Basics

Models for Predicting Remaining Useful Life

You can use recursive models, identified models, or state estimators to predict remaining useful life (RUL). There are also specialized models designed for computing RUL from system data.

Feature Selection for Remaining Useful Life Prediction

Rank features to determine best indicators of system degradation and improve accuracy of remaining useful life (RUL) predictions.

Prediction Using RUL Models

Update RUL Prediction as Data Arrives

As data arrives from a machine under test, you can update the RUL prediction with each new data point.

Similarity-Based Remaining Useful Life Estimation

Build a complete Remaining Useful Life (RUL) estimation algorithm from preprocessing, selecting trendable features, constructing health indicator by sensor fusion, training similarity RUL estimators, and validating prognostics.

Wind Turbine High-Speed Bearing Prognosis

Build an exponential degradation model to predict the Remaining Useful Life (RUL) of a wind turbine bearing in real time. The exponential degradation model predicts the RUL based on its parameter priors and the latest measurements.

Prediction Using Identified Models or State Estimators

Nonlinear State Estimation of a Degrading Battery System

Estimate the states of a nonlinear system using an unscented Kalman filter in Simulink.

Condition Monitoring and Prognostics Using Vibration Signals

Extract features from vibration signals from a ball bearing, conduct health monitoring, and perform prognostics.