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Damage forecasting based on multi-factor fuzzy time series and cloud model

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Abstract

Timely and effective fault forecasting has great significance to guarantee the security of an aircraft, in view of the characteristics of harsh work environment of a flight control system. Based on the forecasting results, we can prevent damages or benefit from the forecasting activities. Fuzzy time series (FTS) forecast which provides a powerful and useful framework to deal with imprecision or ambiguity problems has been widely used in computer science. Many FTS-based forecasting models have been proposed in recent years, and thus the main problems are how to determine the useful interval length and the appropriate window basis size. In this paper, a new method based on multi-factor FTS and a cloud model was presented to predict the trend of aircraft control surface damage (ACSD). The proposed method constructs multi-factor fuzzy logical relationships based on the historical data of ACSD. To handle the uncertainty and vagueness of the ACSD historical data more appropriately, the cloud model is applied to partition the universe of discourse and to build membership functions. Furthermore, a variation forecasting method improved by the cloud model was proposed to compute the forecasting results. The experimental results prove the feasibility and its high forecasting accuracy of the proposed method.

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Acknowledgments

The authors would like to thank the reviewers for their constructive comments and suggestions; their insight and comments led to a better presentation of the ideas expressed in this paper. This paper is sponsored by Joint Fund of the National Natural Science Foundation of China and the Civil Aviation Administration of China (U1533105), Civil Aviation Science and Technology Innovation to Guide Major Financial Projects (MHRD20140103), and Aviation Science and Technology Innovation Guide Funds (MHRD20140208).

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Authors and Affiliations

  1. Tianjin Key Laboratory of Civil Aircraft Airworthiness and Maintenance, Civil Aviation University of China, Tianjin, 300300, People’s Republic of China

    Lei Dong, Peng Wang & Fang Yan

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  1. Lei Dong
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  2. Peng Wang
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  3. Fang Yan
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Correspondence to Lei Dong.

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Dong, L., Wang, P. & Yan, F. Damage forecasting based on multi-factor fuzzy time series and cloud model. J Intell Manuf 30, 521–538 (2019). https://doi.org/10.1007/s10845-016-1264-4

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