Machinery Health Prognosis - Data Driven Approach Using Threshold Regression

Abstract

Machinery health data is the backbone of prognostics. Effective prognostic, from the machinery data, leads towards operational reliability, reduced machinery downtime, cost savings, secondary/catastrophic failures etc. Various methodologies have been adopted by the researchers in an effort to precisely forecast/predict machinery health. In this study, Threshold Regression Methodology has been applied to a machinery vibration data to estimate future health state of machinery. The results show that the proposed method is an effective and reliable approach for data driven prognostics.