Residual-life distributions from component degradation signals: A Bayesian approach

Residual life predictions from vibration-based degradation signals: a neural network approach

Sensory-updated residual life distributions for components with exponential degradation patterns

Residual life predictions in the absence of prior degradation knowledge

A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis

Sensor-driven prognostic models for equipment replacement and spare parts inventory

A neural network integrated decision support system for condition-based optimal predictive maintenance policy

A neural network degradation model for computing and updating residual life distributions

Prognostic degradation models for computing and updating residual life distributions in a time-varying environment

Predictive maintenance management using sensor-based degradation models

Structured replacement policies for components with complex degradation processes and dedicated sensors

Stochastic modeling and real-time prognostics for multi-component systems with degradation rate interactions

Degradation modeling applied to residual lifetime prediction using functional data analysis

Sensor-driven condition-based generator maintenance scheduling—Part I: Maintenance problem

Degradation modeling for real-time estimation of residual lifetimes in dynamic environments

Remaining useful life prediction based on a multi-sensor data fusion model

Integrated predictive analytics and optimization for opportunistic maintenance and operations in wind farms

Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model

Multistream sensor fusion-based prognostics model for systems with single failure modes