Machinery Health Prognosis - Data Driven Approach Using Threshold Regression

Main Article Content

H. MURTAZA
A. MANSOOR
A. S. SOOMRO
H. MUSHTAQ

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.

Article Details

How to Cite
H. MURTAZA, A. MANSOOR, A. S. SOOMRO, & H. MUSHTAQ. (2020). Machinery Health Prognosis - Data Driven Approach Using Threshold Regression . Sindh University Research Journal - SURJ (Science Series), 50(1). Retrieved from https://sujo.usindh.edu.pk/index.php/SURJ/article/view/1321
Section
Articles

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.