Predictive Maintenance

Predictive maintenance studies how to use sensor readings, operating conditions, and maintenance records to detect faults, track equipment health, and estimate remaining useful life before failure. Common approaches include signal processing and statistical models, machine-learning classifiers, recurrent and Transformer-based sequence models, and physics-informed or survival models; newer work also explores self-supervision, foundation models, and federated learning when labels or shared data are limited. These forecasts can feed maintenance scheduling, risk assessment, and digital-twin or edge-monitoring systems, where uncertainty and decision costs matter alongside predictive accuracy. A central challenge is whether models and benchmarks remain reliable across machines, sites, failure modes, and noisy operating conditions, especially when data are scarce and evaluation protocols can hide leakage or poor calibration.