Mining Machine Reliability Analysis Using
Ensembled Support Vector Machine

Abstract

Estimation of reliability plays an important role in performance assessment of any system. Reliability predictions are important for various purposes, like production planning, maintenance planning, reliability assessment, fault detection in manufacturing processes, and risk and liability evaluation. In this study, a Support vector machine (SVM)-based model for forecasting reliability was developed. A genetic algorithm was applied for selecting SVM parameters. The developed model was validated by applying two benchmark data sets. A comparative study reveals that the proposed method performs better than existing methods on benchmark data sets. A case study was conducted on a Dumper’s past time-to-failure data, and cumulative time-to-failure was calculated for reliability modeling. The results demonstrate that the developed model performs well with high accuracy (R2 = 0.99) in the failure prediction of a Dumper. These accurate predictions can help a company in making accurate preventive maintenance and accordingly production and equipment planning can help in increasing production.