World Wide Web

Process (computing)

Engineering

Reliability engineering

Downtime

Construct (python library)

Similarity (geometry)

Operating system

Data mining

Computer science

Mechanical engineering

Index (typography)

Image (mathematics)

Artificial intelligence

Factory (object-oriented programming)

Programming language

Machining

Mahalanobis distance

Reliability Engineering & System Safety

Condition-based maintenance and the prediction of the remaining useful life (RUL) of cutting tools are of crucial importance to reduce unexpected downtime and ensure quality. Our paper proposes an original RUL prediction model based on health index (HI) similarity, where both distance similarity and spatial direction similarity are considered. Data mining is carried out to the large and messy original monitoring data to construct the HIs of the cutting tools, which are then used to predict the RUL. The novelty of our method is that it can make full use of limited historical datasets to achieve more accurate prediction results. The model is applied to the data obtained from a turbine factory's slotting cutter machining process and is compared to one of the most popular prognostic method - least squares support vector regression. Our proposed approach is also applied to two further case studies –a GaAs-based semiconductor laser and simulated data. The comparative results show the effectiveness and practicability of our proposed method, even when the data fluctuate a lot and show distinctive trends.

Remaining useful life prediction based on health index similarity