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剩余使用寿命预测

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, seeModels for Predicting Remaining Useful Life.

荷重软化模型预测未来发展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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monotonicity Quantify monotonic trend in condition indicators
prognosability Measure of variability of condition indicators at failure
trendability Measure of similarity between trajectories of condition indicators

RUL Models

exponentialDegradationModel Exponential degradation model for estimating remaining useful life
linearDegradationModel Linear degradation model for estimating remaining useful life
hashSimilarityModel Hashed-feature similarity model for estimating remaining useful life
pairwiseSimilarityModel Pairwise comparison-based similarity model for estimating remaining useful life
residualSimilarityModel Residual comparison-based similarity model for estimating remaining useful life
covariateSurvivalModel Proportional hazard survival model for estimating remaining useful life
reliabilitySurvivalModel Probabilistic failure-time model for estimating remaining useful life

Training and Prediction

predictRUL Estimate remaining useful life for a test component
compare Compare test data to historical data ensemble for similarity models
fit Estimate parameters of remaining useful life model using historical data
plot Plot survival function for covariate survival remaining useful life model
restart Reset remaining useful life degradation model
update Update posterior parameter distribution of degradation remaining useful life model

Topics

RUL Basics

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

Prediction Using Artificial Intelligence