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

Author

Listed:
  • Lei Dong

    (Civil Aviation University of China)

  • Peng Wang

    (Civil Aviation University of China)

  • Fang Yan

    (Civil Aviation University of China)

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.

Suggested Citation

  • Lei Dong & Peng Wang & Fang Yan, 2019. "Damage forecasting based on multi-factor fuzzy time series and cloud model," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 521-538, February.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:2:d:10.1007_s10845-016-1264-4
    DOI: 10.1007/s10845-016-1264-4
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    References listed on IDEAS

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    1. Chen, Tai-Liang & Cheng, Ching-Hsue & Teoh, Hia-Jong, 2008. "High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 876-888.
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    Cited by:

    1. Athanasopoulos, George & Kourentzes, Nikolaos, 2023. "On the evaluation of hierarchical forecasts," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1502-1511.
    2. Eren Bas & Erol Egrioglu & Taner Tunc, 2023. "Multivariate Picture Fuzzy Time Series: New Definitions and a New Forecasting Method Based on Pi-Sigma Artificial Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 139-164, January.
    3. Peng Zhan & Shaokun Wang & Jun Wang & Leigang Qu & Kun Wang & Yupeng Hu & Xueqing Li, 2021. "Temporal anomaly detection on IIoT-enabled manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1669-1678, August.
    4. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    5. Meira, Erick & Cyrino Oliveira, Fernando Luiz & Jeon, Jooyoung, 2021. "Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals," International Journal of Forecasting, Elsevier, vol. 37(2), pages 547-568.
    6. Xinlong Li & Yan Ran & Genbao Zhang & Yan He, 2020. "A failure mode and risk assessment method based on cloud model," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1339-1352, August.
    7. Xiao-kang Wang & Sheng-hui Wang & Hong-yu Zhang & Jian-qiang Wang & Lin Li, 2021. "The Recommendation Method for Hotel Selection Under Traveller Preference Characteristics: A Cloud-Based Multi-Criteria Group Decision Support Model," Group Decision and Negotiation, Springer, vol. 30(6), pages 1433-1469, December.

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