Volume 8 Number 1 (Jan. 2013)
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JCP 2013 Vol.8(1): 225-231 ISSN: 1796-203X
doi: 10.4304/jcp.8.1.225-231

Data-driven Machinery Prognostics Approach using in a Predictive Maintenance Model

Wenzhu Liao1 and Ying Wang2
1 Department of Industrial Engineering, Chongqing University, Chongqing, P.R. China
2 Department of Industrial Engineering and Logistics Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China


Abstract—Nowadays, more and more manufacturers realize the importance of adopting new maintenance technologies to enable systems to achieve near-zero downtime, so machinery prognostics that enables this paradigm shift from traditional fail-and-fix maintenance to a predict-and-prevent paradigm has arose interests from researchers. Machinery prognostics which could estimate machine condition and degradation strongly support predictive maintenance policy. This paper develops a novel data-driven machine prognostics approach to predict machine’s health condition and describe machine degradation. Based on machine’s prognostics information, a predictive maintenance model is well constructed to decide machine’s optimal maintenance threshold and maintenance cycles. Through a case study, this predictive maintenance model is verified, and the computational results show that this proposed model is efficient and practical.

Index Terms—prognostics, predictive maintenance, cost, optimization

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Cite: Wenzhu Liao and Ying Wang, " Data-driven Machinery Prognostics Approach using in a Predictive Maintenance Model," Journal of Computers vol. 8, no. 1, pp. 225-231, 2013.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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