IDEAS home Printed from https://ideas.repec.org/a/wly/jnljam/v2012y2012i1n615618.html

The Chaotic Prediction for Aero‐Engine Performance Parameters Based on Nonlinear PLS Regression

Author

Listed:
  • Chunxiao Zhang
  • Junjie Yue

Abstract

The prediction of the aero‐engine performance parameters is very important for aero‐engine condition monitoring and fault diagnosis. In this paper, the chaotic phase space of engine exhaust temperature (EGT) time series which come from actual air‐borne ACARS data is reconstructed through selecting some suitable nearby points. The partial least square (PLS) based on the cubic spline function or the kernel function transformation is adopted to obtain chaotic predictive function of EGT series. The experiment results indicate that the proposed PLS chaotic prediction algorithm based on biweight kernel function transformation has significant advantage in overcoming multicollinearity of the independent variables and solve the stability of regression model. Our predictive NMSE is 16.5 percent less than that of the traditional linear least squares (OLS) method and 10.38 percent less than that of the linear PLS approach. At the same time, the forecast error is less than that of nonlinear PLS algorithm through bootstrap test screening.

Suggested Citation

  • Chunxiao Zhang & Junjie Yue, 2012. "The Chaotic Prediction for Aero‐Engine Performance Parameters Based on Nonlinear PLS Regression," Journal of Applied Mathematics, John Wiley & Sons, vol. 2012(1).
    • Handle: RePEc:wly:jnljam:v:2012:y:2012:i:1:n:615618
    DOI: 10.1155/2012/615618
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2012/615618
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2012/615618?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Thomas S. Parker & Leon O. Chua, 1989. "Practical Numerical Algorithms for Chaotic Systems," Springer Books, Springer, number 978-1-4612-3486-9, March.
    2. Douglas M. Hawkins, 1980. "Critical Values for Identifying Outliers," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 95-96, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Karol Pilot & Alicja Ganczarek-Gamrot & Krzysztof Kania, 2024. "Dealing with Anomalies in Day-Ahead Market Prediction Using Machine Learning Hybrid Model," Energies, MDPI, vol. 17(17), pages 1-20, September.
    2. Damian Przekop, 2020. "Feature Engineering for Anti-Fraud Models Based on Anomaly Detection," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(3), pages 301-316, September.
    3. Francesca Ieva & Anna Maria Paganoni, 2020. "Component-wise outlier detection methods for robustifying multivariate functional samples," Statistical Papers, Springer, vol. 61(2), pages 595-614, April.
    4. Gaucher, Solenne & Klopp, Olga & Robin, Geneviève, 2021. "Outlier detection in networks with missing links," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
    5. Andrzej Chmielowiec, 2021. "Algorithm for error-free determination of the variance of all contiguous subsequences and fixed-length contiguous subsequences for a sequence of industrial measurement data," Computational Statistics, Springer, vol. 36(4), pages 2813-2840, December.
    6. Marc Chataigner & Stéphane Crépey & Jiang Pu, 2020. "Nowcasting Networks," Post-Print hal-03910123, HAL.
    7. Greco, Salvatore & Ishizaka, Alessio & Tasiou, Menelaos & Torrisi, Gianpiero, 2019. "Sigma-Mu efficiency analysis: A methodology for evaluating units through composite indicators," European Journal of Operational Research, Elsevier, vol. 278(3), pages 942-960.
    8. David Juárez-Varón & Victoria Tur-Viñes & Alejandro Rabasa-Dolado & Kristina Polotskaya, 2020. "An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy," Social Sciences, MDPI, vol. 9(9), pages 1-23, September.
    9. Stéphane Crépey & Lehdili Noureddine & Nisrine Madhar & Maud Thomas, 2022. "Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural Networks," Working Papers hal-03777995, HAL.
    10. Zhongqiu Wang & Guan Yuan & Haoran Pei & Yanmei Zhang & Xiao Liu, 2020. "Unsupervised learning trajectory anomaly detection algorithm based on deep representation," International Journal of Distributed Sensor Networks, , vol. 16(12), pages 15501477209, December.
    11. Arata, Linda & Fabrizi, Enrico & Sckokai, Paolo, 2020. "A worldwide analysis of trend in crop yields and yield variability: Evidence from FAO data," Economic Modelling, Elsevier, vol. 90(C), pages 190-208.
    12. Wentao Yang & Huaxi He & Dongsheng Wei & Hao Chen, 2022. "Generating pseudo-absence samples of invasive species based on outlier detection in the geographical characteristic space," Journal of Geographical Systems, Springer, vol. 24(2), pages 261-279, April.
    13. Fournier, Nicholas PhD & Farid, Yashar Zeinali PhD & Patire, Anthony David PhD, 2021. "Potential Erroneous Degradation of High Occupancy Vehicle (HOV) Facilities," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3z76r7tj, Institute of Transportation Studies, UC Berkeley.
    14. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    15. Li, Peidu & Luo, Yong & Xia, Xin & Gao, Xiaoqing & Chang, Rui & Li, Zhenchao & Zheng, Junqing & Shi, Wen & Liao, Zhouyi, 2024. "Factors and quantitative impact on electrical yield in fishery complementary photovoltaic power plant under different cloud cover conditions," Energy, Elsevier, vol. 309(C).
    16. Tommaso Barbariol & Enrico Feltresi & Gian Antonio Susto, 2020. "Self-Diagnosis of Multiphase Flow Meters through Machine Learning-Based Anomaly Detection," Energies, MDPI, vol. 13(12), pages 1-24, June.
    17. Lucas Lopes Ferreira de Souza & Francesca Bassi & Ana Augusta Ferreira de Freitas & Márcio de Oliveira Mota & Samuel Façanha Câmara, 2025. "The Capital Structure of Microfinance Borrowers: A Dynamic Market Segmentation Analysis," Journal of International Development, John Wiley & Sons, Ltd., vol. 37(5), pages 1154-1167, July.
    18. Hendrik Engbers & Michael Freitag, 2025. "Automated model selection for multivariate anomaly detection in manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 36(7), pages 5015-5033, October.
    19. Puteri Paramita & Zuduo Zheng & Md Mazharul Haque & Simon Washington & Paul Hyland, 2018. "User satisfaction with train fares: A comparative analysis in five Australian cities," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-26, June.
    20. Sunil Kumar & Sudeep Varshney & Usha Jain & Prashant Johri & Abdulaziz S Almazyad & Ali Wagdy Mohamed & Mehdi Hosseinzadeh & Mohammad Shokouhifar, 2025. "EODA: A three-stage efficient outlier detection approach using Boruta-RF feature selection and enhanced KNN-based clustering algorithm," PLOS ONE, Public Library of Science, vol. 20(5), pages 1-25, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jnljam:v:2012:y:2012:i:1:n:615618. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/4185 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.