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Predict Remaining Useful Life (RUL)

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

One way of analyzing condition indicators is to use them in detecting faults, but you can also use a different type of condition-indicator analysis for predicting the RUL of a system. RUL of a machine is the expected life or usage time remaining before the machine requires repair or replacement.

Typically, you estimate the 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.

Functions

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monotonicityQuantify monotonic trend in condition indicators
prognosabilityMeasure of variability of condition indicators at failure
trendabilityMeasure of similarity between trajectories of condition indicators

RUL Models

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

Training and Prediction

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 survival function for covariate survival remaining useful life model
restartReset remaining useful life degradation model
updateUpdate posterior parameter distribution of degradation remaining useful life model

Topics

RUL Basics

Prediction Using RUL Models

Prediction Using Identified Models or State Estimators

Prediction Using Artificial Intelligence