System and method for predicting remaining useful life of device components

A system for accurately predicting the remaining useful life of a device component, said system comprising:

• a processor;
• a data bus coupled to said processor;
• and
• a computer-usable medium embodying computer program code, said computer-usable medium being coupled to said data bus, said computer program code comprising instructions executable by said processor and configured for:
• generating time-to-failure data that shares the same characteristics with use data of a device component;
• analyzing at least one operation condition and a device operation profile associated with said device component utilizing a statistical analysis based on a Monte Carlo technique;
• analyzing the effects of suspension percentage and heavy-tailed behavior on said remaining useful life prediction;
• analyzing a fixed-time predictor and a time-dependent covariate as indicators of remaining useful life of said device component;
• and
• generating prediction data indicative of a remaining useful life of said device component in order to thereafter perform a condition-based preventive maintenance on said device component based on said remaining useful life of said device component.

A system for accurately predicting the remaining useful life of a device component, said system comprising:

• a processor;
• a data bus coupled to said processor;
• and
• a computer-usable medium embodying computer program code, said computer-usable medium being coupled to said data bus, said computer program code comprising instructions executable by said processor and configured for:
• generating time-to-failure data that shares the same characteristics with use data of a device component;
• analyzing at least one operation condition and a device operation profile associated with said device component utilizing a statistical analysis based on a Monte Carlo technique, said statistical analysis further comprising a Random Forest Model of analysis, wherein said Random Forest Model comprises analyzing independent training decision trees on a set of data points to determine said remaining useful life of said device component;
• and
• generating prediction data indicative of a remaining useful life of said device component in order to thereafter perform a condition-based preventive maintenance on said device component based on said remaining useful life of said device component.