Machine learning

Reliability engineering

Convergence (economics)

Reliability (semiconductor)

Engineering

Artificial intelligence

Economics

Power (physics)

Condition monitoring

Combinatorics

Physics

Economic growth

Kernel (algebra)

Mathematics

Quantum mechanics

Prognostics

Vibration

Bearing (navigation)

Computer science

Support vector machine

Electrical engineering

Rolling-element bearing

IEEE Transactions on Reliability

Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions with different kernel parameters. Then, exponential degradation models coupled with the Fréchet distance are employed to estimate the RUL adaptively. The proposed approach is evaluated using the vibration data from accelerated degradation tests of rolling element bearings and the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving the accuracy and convergence of RUL prediction of rolling element bearings.

A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings