A prognostic algorithm for machine performance assessment and its application

Jihong Yan, Muammer Koç, Jay Lee

Research output: Contribution to journalArticle

139 Citations (Scopus)

Abstract

This paper explores a method to assess assets performance and predict the remaining useful life, which would lead to proactive maintenance processes to minimize downtime of machinery and production in various industries, thus increasing efficiency of operations and manufacturing. At first, a performance model is established by taking advantage of logistic regression analysis with maximum-likelihood technique. Two kinds of application situations, with or without enough historical data, are discussed in detail. Then, real-time performance is evaluated by inputting features of online data to the logistic model. Finally, the remaining life is estimated using an ARMA model based on machine performance history; degradation predictions are also upgraded dynamically. The results such as current machine running condition and the remaining useful life, are output to the maintenance decision module to determine a window of appropriate maintenance before the machine fails. An application of the method on an elevator door motion system is demonstrated.

Original languageEnglish
Pages (from-to)796-801
Number of pages6
JournalProduction Planning and Control
Volume15
Issue number8
DOIs
Publication statusPublished - Dec 2004
Externally publishedYes

Fingerprint

Logistics
Elevators
Regression analysis
Maximum likelihood
Machinery
Degradation
Performance assessment
Industry
Logistic regression analysis
Module
Manufacturing
Logistic model
ARMA model
Prediction
Assets

Keywords

  • Elevator door system
  • Logistic regression
  • Performance assessment
  • Remaining life prediction

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Management Science and Operations Research

Cite this

A prognostic algorithm for machine performance assessment and its application. / Yan, Jihong; Koç, Muammer; Lee, Jay.

In: Production Planning and Control, Vol. 15, No. 8, 12.2004, p. 796-801.

Research output: Contribution to journalArticle

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