IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v52y2008i6p3208-3219.html

Detection of chatter vibration in a drilling process using multivariate control charts

Author

Listed:
  • Messaoud, Amor
  • Weihs, Claus
  • Hering, Franz

Abstract

Time series analysis and multivariate control charts are used to devise a real-time monitoring strategy in a drilling process. The process is used to produce holes with high length-to-diameter ratio, good surface finish and straightness. It is subject to dynamic disturbances that are classified as either chatter vibration or spiralling. A new nonparametric control chart for multivariate processes is proposed. It is used to detect chatter vibration which is dominated by single frequencies. The results showed that the proposed monitoring strategy can detect chatter vibration and that some alarm signals are related to changing physical conditions of the process.

Suggested Citation

  • Messaoud, Amor & Weihs, Claus & Hering, Franz, 2008. "Detection of chatter vibration in a drilling process using multivariate control charts," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3208-3219, February.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:6:p:3208-3219
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(07)00361-1
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Jarrett, Jeffrey E. & Pan, Xia, 2007. "The quality control chart for monitoring multivariate autocorrelated processes," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3862-3870, May.
    2. Timo Teräsvirta & Chien‐Fu Lin & Clive W. J. Granger, 1993. "Power Of The Neural Network Linearity Test," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(2), pages 209-220, March.
    3. Alwan, Layth C & Roberts, Harry V, 1988. "Time-Series Modeling for Statistical Process Control," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(1), pages 87-95, January.
    4. Peter J. Rousseeuw & Ida Ruts, 1996. "Bivariate Location Depth," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(4), pages 516-526, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bodnar, Olha & Bodnar, Taras & Okhrin, Yarema, 2009. "Surveillance of the covariance matrix based on the properties of the singular Wishart distribution," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3372-3385, July.
    2. Rostyslav Bodnar & Taras Bodnar & Wolfgang Schmid, 2023. "Sequential monitoring of high‐dimensional time series," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(3), pages 962-992, September.
    3. Wu, Zhang & Yang, Mei & Jiang, Wei & Khoo, Michael B.C., 2008. "Optimization designs of the combined Shewhart-CUSUM control charts," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 496-506, December.

    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. A. Snoussi, 2011. "SPC for short-run multivariate autocorrelated processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2303-2312.
    2. Li, Yanting & Wu, Zhenyu, 2020. "A condition monitoring approach of multi-turbine based on VAR model at farm level," Renewable Energy, Elsevier, vol. 166(C), pages 66-80.
    3. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2015. "Multivariate functional outlier detection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 177-202, July.
    4. Ioulia Papageorgiou, 2016. "Sampling from Correlated Populations: Optimal Strategies and Comparison Study," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 78(1), pages 119-151, May.
    5. Zuo, Yijun & Lai, Shaoyong, 2011. "Exact computation of bivariate projection depth and the Stahel-Donoho estimator," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1173-1179, March.
    6. Canaydin, Ada & Fu, Chun & Balint, Attila & Khalil, Mohamad & Miller, Clayton & Kazmi, Hussain, 2024. "Interpretable domain-informed and domain-agnostic features for supervised and unsupervised learning on building energy demand data," Applied Energy, Elsevier, vol. 360(C).
    7. Heather M. Anderson & Farshid Vahid, 2005. "Nonlinear Correlograms and Partial Autocorrelograms," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 957-982, December.
    8. Anoop S. KUMAR & Bandi KAMAIAH, 2016. "Efficiency, non-linearity and chaos: evidences from BRICS foreign exchange markets," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(1(606), S), pages 103-118, Spring.
    9. Samari, Goleen & Catalano, Ralph & Alcalá, Héctor E. & Gemmill, Alison, 2020. "The Muslim Ban and preterm birth: Analysis of U.S. vital statistics data from 2009 to 2018," Social Science & Medicine, Elsevier, vol. 265(C).
    10. I. Civantos & J. García-Algarra, 2020. "Analysis of telecom service operation behavior with time series," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 25-34, March.
    11. Amira Dridi & Mohamed El Ghourabi & Mohamed Limam, 2012. "On monitoring financial stress index with extreme value theory," Quantitative Finance, Taylor & Francis Journals, vol. 12(3), pages 329-339, March.
    12. Ngene, Geoffrey M. & Lee Kim, Yea & Wang, Jinghua, 2019. "Who poisons the pool? Time-varying asymmetric and nonlinear causal inference between low-risk and high-risk bonds markets," Economic Modelling, Elsevier, vol. 81(C), pages 136-147.
    13. Croux, C. & Gelper, S. & Mahieu, K., 2010. "Robust Control Charts for Time Series Data," Discussion Paper 2010-107, Tilburg University, Center for Economic Research.
    14. Ali Taiebnia & Shapour Mohammadi, 2023. "Forecast accuracy of the linear and nonlinear autoregressive models in macroeconomic modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2045-2062, December.
    15. Weihs, Claus & Theis, Winfried & Messaoud, Amor & Hering, Franz, 2004. "Monitoring of the BTA Deep Hole Drilling Process Using Residual Control Charts," Technical Reports 2004,60, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    16. Chen, Gong & Fricke, Hartmut & Okhrin, Ostap & Rosenow, Judith, 2024. "Flight delay propagation inference in air transport networks using the multilayer perceptron," Journal of Air Transport Management, Elsevier, vol. 114(C).
    17. Marta Benková & Dagmar Bednárová & Gabriela Bogdanovská & Marcela Pavlíčková, 2023. "Use of Statistical Process Control for Coking Time Monitoring," Mathematics, MDPI, vol. 11(16), pages 1-30, August.
    18. Farman Ullah Khan & Faridoon Khan & Parvez Ahmed Shaikh, 2023. "Forecasting returns volatility of cryptocurrency by applying various deep learning algorithms," Future Business Journal, Springer, vol. 9(1), pages 1-11, December.
    19. Jinho Kim & Myong K. Jeong & Elsayed A. Elsayed, 2017. "Monitoring multistage processes with autocorrelated observations," International Journal of Production Research, Taylor & Francis Journals, vol. 55(8), pages 2385-2396, April.
    20. Schmid Wolfgang & Okhrin Yarema, 2003. "Tail behaviour of a general family of control charts," Statistics & Risk Modeling, De Gruyter, vol. 21(1), pages 79-92, January.

    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:eee:csdana:v:52:y:2008:i:6:p:3208-3219. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.