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Current status of machine prognostics in condition-based maintenance: a review

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Abstract

Condition-based maintenance (CBM) is a decision-making strategy based on real-time diagnosis of impending failures and prognosis of future equipment health. It is a proactive process that requires the development of a predictive model that can trigger the alarm for corresponding maintenance. Prognostic methodologies for CBM have only recently been introduced into the technical literature and become such a focus in the field of maintenance research and development. There are many research and development on a variety of technologies and algorithms that can be regarded as the steps toward prognostic maintenance. They are needed in order to support decision making and manage operational reliability. In this paper, recent literature that focuses on the machine prognostics has been reviewed. Generally, prognostic models can be classified into four categories: physical model, knowledge-based model, data-driven model, and combination model. Various techniques and algorithms have been developed depending on what models they usually adopt. Based on the review of some typical approaches and new introduced methods, advantages and disadvantages of these methodologies are discussed. From the literature review, some increasing trends appeared in the research field of machine prognostics are summarized. Furthermore, the future research directions have been explored.

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Authors and Affiliations

  1. Antai College of Economics & Management, Shanghai Jiao Tong University, 535 Fahua Zhen Road, Shanghai, 200052, People’s Republic of China

    Ying Peng & Ming Dong

  2. Department of Mechanical Engineering, University of Alberta, Edmonton, AB, T6G 2G8, Canada

    Ming Jian Zuo

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  1. Ying Peng
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  2. Ming Dong
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Correspondence to Ming Dong.

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Peng, Y., Dong, M. & Zuo, M.J. Current status of machine prognostics in condition-based maintenance: a review. Int J Adv Manuf Technol 50, 297–313 (2010). https://doi.org/10.1007/s00170-009-2482-0

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