IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i18p2813-d1475904.html
   My bibliography  Save this article

An Outlier Detection Approach to Recognize the Sources of a Process Failure within a Multivariate Poisson Process

Author

Listed:
  • Chia-Ding Hou

    (Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan)

  • Rung-Hung Su

    (Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan)

Abstract

Among attribute processes, the number of nonconformities conforming to a Poisson distribution is among the most crucial quality attributes. Furthermore, owing to the variety of quality attributes, the significance of the multivariate Poisson process in industry cannot be overstated. An out-of-control multivariate Poisson process can be detected using an alarm on a multivariate control chart. Nevertheless, pinpointing the specific quality attributes that led to the process shifts is complex. The study focuses on the causes that lead to process shifts in multivariate Poisson processes, unlike the majority of studies examining shifts in multivariate normal processes. This paper initially presents a statistical method for detecting outliers in a multivariate Poisson distribution. Furthermore, a progressive testing algorithm is then developed to identify the variables responsible for a failure within a multivariate Poisson process. According to simulation results, the proposed approach can effectively determine the sources of a process fault within a multivariate Poisson process.

Suggested Citation

  • Chia-Ding Hou & Rung-Hung Su, 2024. "An Outlier Detection Approach to Recognize the Sources of a Process Failure within a Multivariate Poisson Process," Mathematics, MDPI, vol. 12(18), pages 1-10, September.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2813-:d:1475904
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/18/2813/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/18/2813/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chia-Ding Hou & Jengtung Chiang & John Jen Tai, 2001. "Identifying Chromosomal Fragile Sites from a Hierarchical-Clustering Point of View," Biometrics, The International Biometric Society, vol. 57(2), pages 435-440, June.
    2. Yuehjen E. Shao & Chi-Jie Lu & Yu-Chiun Wang, 2012. "A Hybrid ICA-SVM Approach for Determining the Quality Variables at Fault in a Multivariate Process," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-12, September.
    3. Jinho Kim & Myong K. Jeong & Elsayed A. Elsayed & K.N. Al-Khalifa & A.M.S. Hamouda, 2016. "An adaptive step-down procedure for fault variable identification," International Journal of Production Research, Taylor & Francis Journals, vol. 54(11), pages 3187-3200, June.
    4. Yuehjen E. Shao & Chia-Ding Hou, 2013. "Fault Identification in Industrial Processes Using an Integrated Approach of Neural Network and Analysis of Variance," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-7, June.
    5. Yuehjen E. Shao, 2014. "Recognition of Process Disturbances for an SPC/EPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-9, June.
    6. Hamed Sabahno & Seyed Taghi Akhavan Niaki, 2023. "New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and Identification," Mathematics, MDPI, vol. 11(16), pages 1-31, August.
    7. Hongying Jing & Jian Li & Kaizong Bai, 2022. "Directional monitoring and diagnosis for covariance matrices," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(6), pages 1449-1464, April.
    8. Jian Li & Fugee Tsung & Changliang Zou, 2014. "Multivariate binomial/multinomial control chart," IISE Transactions, Taylor & Francis Journals, vol. 46(5), pages 526-542.
    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. Yuehjen E. Shao & Shih-Chieh Lin, 2019. "Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance Shifts," Mathematics, MDPI, vol. 7(10), pages 1-14, October.
    2. Sotirios Bersimis & Athanasios Sachlas & Philippe Castagliola, 2017. "Controlling Bivariate Categorical Processes using Scan Rules," Methodology and Computing in Applied Probability, Springer, vol. 19(4), pages 1135-1149, December.
    3. Shengjin Gan & Su-Fen Yang & Li-Pang Chen, 2023. "A New EWMA Control Chart for Monitoring Multinomial Proportions," Sustainability, MDPI, vol. 15(15), pages 1-19, July.
    4. Hou, Chia-Ding & Chiang, Jengtung & Tai, John Jen, 2003. "A family of simultaneous confidence intervals for multinomial proportions," Computational Statistics & Data Analysis, Elsevier, vol. 43(1), pages 29-45, May.
    5. Ricardo Saldanha Morais & Roberto da Costa Quinino & Emilio Suyama & Linda Lee Ho, 2019. "Estimators of parameters of a mixture of three multinomial distributions based on simple majority results," Statistical Papers, Springer, vol. 60(4), pages 1283-1316, August.
    6. Hou, Chia-Ding & Huang, Sheng, 2013. "Identifying the source of proportion shifts in a multinomial process using a simple statistical test procedure," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 1100-1105.
    7. P. Fred Dahm & Ann W. Olmsted & Ira F. Greenbaum, 2002. "Probability Models and the Applicability of Statistical Procedures in the Identification of Chromosomal Fragile Sites," Biometrics, The International Biometric Society, vol. 58(4), pages 1028-1031, December.

    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:gam:jmathe:v:12:y:2024:i:18:p:2813-:d:1475904. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.