Detect and Predict Faults
Condition monitoring includes discriminating between faulty and healthy states (fault detection) or, when a fault state is present, determining the source of the fault (fault diagnosis). To design an algorithm for condition monitoring, you use condition indicators extracted from system data to train a decision model that can analyze test data to determine the current system state. For more information, see Decision Models for Fault Detection and Diagnosis.
Another way to analyze condition indicators is to use them to predict the remaining useful life (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. For more information, see Models for Predicting Remaining Useful Life.
- Detect and Diagnose Faults
Train classifiers or regression models for condition monitoring
- Predict Remaining Useful Life
Predict RUL using specialized models designed for computing RUL from system data, state estimators, or identified models