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Volume 7 Issue 2
Mar.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(2): 330-345
Kai Zhong, Min Han and Bing Han, "Data-Driven Based Fault Prognosis for Industrial Systems: A Concise Overview," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 330-345, Mar. 2020. doi: 10.1109/JAS.2019.1911804
Citation: Kai Zhong, Min Han and Bing Han, "Data-Driven Based Fault Prognosis for Industrial Systems: A Concise Overview," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 330-345, Mar. 2020. doi: 10.1109/JAS.2019.1911804
shu

Data-Driven Based Fault Prognosis for Industrial Systems: A Concise Overview

doi: 10.1109/JAS.2019.1911804
Funds:  This work was supported by the National Natural Science Foundation of China (61773087), in part by the National Key Research and Development Program of China (2018YFB1601500), and High-tech Ship Research Project of Ministry of Industry and Information Technology-Research of Intelligent Ship Testing and Verifacation ([2018]473)
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    Abstract
  • Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs, which is vital for ensuring the stability, safety and long lifetime of degrading industrial systems. According to the results of fault prognosis, the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance. With the increased complexity and the improved automation level of industrial systems, fault prognosis techniques have become more and more indispensable. Particularly, the data-driven based prognosis approaches, which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data, gain great attention from different industrial sectors. In this context, the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems. Firstly, the characteristics of different prognosis methods are revealed with the data-based ones being highlighted. Moreover, based on the different data characteristics that exist in industrial systems, the corresponding fault prognosis methodologies are illustrated, with emphasis on analyses and comparisons of different prognosis methods. Finally, we reveal the current research trends and look forward to the future challenges in this field. This review is expected to serve as a tutorial and source of references for fault prognosis researchers.

     

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    Highlights

    • Firstly, the characteristics of different prognosis methods are revealed with the data-based ones being highlighted.
    • Moreover, based on the different data characteristics that exist in industrial systems, the corresponding fault prognosis methodologies are illustrated, with emphasis on analyses and comparisons of different prognosis methods.
    • Finally, we reveal the current research trends and look forward to the future challenges in this field. This review is expected to serve as a tutorial and source of references for fault prognosis researchers.

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