608
Views
11
CrossRef citations to date
0
Altmetric
Research Article

On the influence of overlap in automatic root cause analysis in manufacturing

Pages 6491-6507 | Received 05 Jan 2021, Accepted 09 Sep 2021, Published online: 29 Oct 2021

References

  • Bennacer, Leila, Yacine Amirat, Abdelghani Chibani, Abdelhamid Mellouk, and Laurent Ciavaglia. 2015. “Self-Diagnosis Technique for Virtual Private Networks Combining Bayesian Networks and Case-Based Reasoning.” IEEE Transactions on Automation Science and Engineering 12 (1): 354–366.
  • Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1): 5–32.
  • Chemweno, P., L. Pintelon, L. Jongers, and P. Muchiri. 2016. “i-RCAM: Intelligent Expert System for Root Cause Analysis in Maintenance Decision Making.” In 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), June, 1–7.
  • Chen, Chee-Cheng. 2013. “A Developed Autonomous Preventive Maintenance Programme Using RCA and FMEA.” International Journal of Production Research 51 (18): 5404–5412.
  • Chen, Zheyuan, Ying Liu, Agustin Valera-Medina, Fiona Robinson, and Michael Packianather. 2021. “Multi-faceted Modelling for Strip Breakage in Cold Rolling Using Machine Learning.” International Journal of Production Research 59 (21): 6347–6360.
  • Chen, Wei-Chou, Shian-Shyong Tseng, and Ching-Yao Wang. 2005. “A Novel Manufacturing Defect Detection Method Using Association Rule Mining Techniques.” Expert Systems with Applications 29 (4): 807–815.
  • Chien, C., and S. Chuang. 2014. “A Framework for Root Cause Detection of Sub-Batch Processing System for Semiconductor Manufacturing Big Data Analytics.” IEEE Transactions on Semiconductor Manufacturing 27 (4): 475–488.
  • Chien, Chen-Fu, Chia-Yu Hsu, and Pei-Nong Chen. 2013. “Semiconductor Fault Detection and Classification for Yield Enhancement and Manufacturing Intelligence.” Flexible Services and Manufacturing Journal 25 (3): 367–388.
  • Chien, Chen-Fu, Yun-Siang Lin, and Sheng-Kai Lin. 2020. “Deep Reinforcement Learning for Selecting Demand Forecast Models to Empower Industry 3.5 and An Empirical Study for a Semiconductor Component Distributor.” International Journal of Production Research 58 (9): 2784–2804.
  • Chien, Chen-Fu, Chiao-Wen Liu, and Shih-Chung Chuang. 2017. “Analysing Semiconductor Manufacturing Big Data for Root Cause Detection of Excursion for Yield Enhancement.” International Journal of Production Research 55 (17): 5095–5107.
  • Chien, Chen-Fu, Tzu yen Hong, and Hong-Zhi Guo. 2017. “A Conceptual Framework for ‘Industry 3.5’ to Empower Intelligent Manufacturing and Case Studies.” Procedia Manufacturing 11: 2009–2017. 27th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM2017, 27–30 June 2017, Modena, Italy.
  • Choudhary, Alok Kumar, Jenny A. Harding, and Manoj Kumar Tiwari. 2009. “Data Mining in Manufacturing: a Review Based on the Kind of Knowledge.” Journal of Intelligent Manufacturing 20 (5): 501–521.
  • Crámer, Harald. 1999. Mathematical Methods of Statistics (PMS-9). Princeton, NJ: Princeton University Press.
  • Detzner, Alexander, and Martin Eigner. 2021. “Feature Selection Methods for Root-cause Analysis Among Top-level Product Attributes.” Quality and Reliability Engineering International 37 (1): 335–351.
  • Donauer, Michael, Paulo Peças, and Americo Azevedo. 2015. “Identifying Nonconformity Root Causes Using Applied Knowledge Discovery.” Robotics and Computer-Integrated Manufacturing 36: 84–92. Sustaining Resilience in Today's Demanding Environments.
  • Fan, Shu-Kai S., Shou-Chih Lin, and Pei-Fang Tsai. 2016. “Wafer Fault Detection and Key Step Identification for Semiconductor Manufacturing Using Principal Component Analysis, AdaBoost and Decision Tree.” Journal of Industrial and Production Engineering 33 (3): 151–168.
  • Guh, Ruey-Shiang. 2007. “On-line Identification and Quantification of Mean Shifts in Bivariate Processes Using a Neural Network-based Approach.” Quality and Reliability Engineering International 23 (3): 367–385.
  • Hsu, Shao-Chung, and Chen-Fu Chien. 2007. “Hybrid Data Mining Approach for Pattern Extraction From Wafer Bin Map to Improve Yield in Semiconductor Manufacturing.” International Journal of Production Economics 107 (1): 88–103. Special Section on Building Core-Competence through Operational Excellence.
  • Janitza, Silke, Carolin Strobl, and Anne-Laure Boulesteix. 2013. “An AUC-based Permutation Variable Importance Measure for Random Forests.” BMC Bioinformatics 14 (1): 119.
  • Kim, Byunghoon, Young-Seon Jeong, Seung Hoon Tong, and Myong K. Jeong. 2020. “A Generalised Uncertain Decision Tree for Defect Classification of Multiple Wafer Maps.” International Journal of Production Research 58 (9): 2805–2821.
  • Kim, Jinho, Youngmin Lee, and Heeyoung Kim. 2018. “Detection and Clustering of Mixed-type Defect Patterns in Wafer Bin Maps.” IISE Transactions 50 (2): 99–111.
  • Ku, Chien-Chun, Chen-Fu Chien, and Kang-Ting Ma. 2020. “Digital Transformation to Empower Smart Production for Industry 3.5 and An Empirical Study for Textile Dyeing.” Computers & Industrial Engineering 142: 106297.
  • Lee, Chia-Yen, and Chen-Fu Chien. 2020. “Pitfalls and Protocols of Data Science in Manufacturing Practice.” Journal of Intelligent Manufacturing. doi:10.1007/s10845-020-01711-w.
  • Li, Zhiguo, and Shiyu Zhou. 2005. “Robust Method of Multiple Variation Sources Identification in Manufacturing Processes For Quality Improvement.” Journal of Manufacturing Science and Engineering 128 (1): 326–336.
  • Lima, Alexandre, Valeria Borodin, Stéphane Dauzère-Pérès, and Philippe Vialletelle. 2021. “A Sampling-based Approach for Managing Lot Release in Time Constraint Tunnels in Semiconductor Manufacturing.” International Journal of Production Research 59 (3): 860–884.
  • Montgomery, Douglas C. 2019. Introduction to Statistical Quality Control. 8th ed. Hoboken, NJ: John Wiley & Sons.
  • Ong, Phaik-Ling, Yun-Huoy Choo, and Azah Kamilah Muda. 2015. “A Manufacturing Failure Root Cause Analysis in Imbalance Data Set Using PCA Weighted Association Rule Mining.” Jurnal Teknologi 77 (18): 103–111.
  • Quinlan, J. Ross. 1993. C4.5: Programs for Machine Learning. San Francisco, CA: Morgan Kaufmann Publishers, Inc.
  • Roberts, S. W. 1959. “Control Chart Tests Based on Geometric Moving Averages.” Technometrics 1 (3): 239–250.
  • Rokach, Lior, and Dan Hutter. 2012. “Automatic Discovery of the Root Causes for Quality Drift in High Dimensionality Manufacturing Processes.” Journal of Intelligent Manufacturing 23 (5): 1915–1930.
  • Saez, Miguel A., Francisco P. Maturana, Kira Barton, and Dawn M. Tilbury. 2020. “Context-sensitive Modeling and Analysis of Cyber-physical Manufacturing Systems for Anomaly Detection and Diagnosis.” IEEE Transactions on Automation Science and Engineering.17: 29–40.
  • Sahoo, Saumyaranjan. 2021. “Big Data Analytics in Manufacturing: a Bibliometric Analysis of Research in the Field of Business Management.” International Journal of Production Research. doi:10.1080/00207543.2021.1919333.
  • Sales-Setién, Ester, Ignacio Peñarrocha-Alos, and José V. Abellán-Nebot. 2019. “Estimation of Nonstationary Process Variance in Multistage Manufacturing Processes Using a Model-Based Observer.” IEEE Transactions on Automation Science and Engineering 16 (2): 741–754.
  • Shi, Dongfeng, and Fugee Tsung. 2003. “Modelling and Diagnosis of Feedback-controlled Processes Using Dynamic PCA and Neural Networks.” International Journal of Production Research 41 (2): 365–379.
  • Springer-Norden, Mona R., Rainer Olbrich, Philipp Brüggemann, and Carsten D. Schultz. 2021. “Product Variety and Loyalty to National Brands – A Combined Measurement of Purchase Sequence and Coverage of Demand.” In Advances in National Brand and Private Label Marketing, edited by Francisco J. Martínez-López and Juan Carlos Gázquez-Abad, 1–11. Cham: Springer International Publishing.
  • Steinhauer, H. Joe, A. Karlsson, G. Mathiason, and T. Helldin. 2016. “Root-cause Localization Using Restricted Boltzmann Machines.” In 2016 19th International Conference on Information Fusion (FUSION), Heidelberg, Germany, July, 248–255.
  • Strobl, Carolin, Anne-Laure Boulesteix, Achim Zeileis, and Torsten Hothorn. 2007. “Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution.” BMC Bioinformatics 8 (1): 25.
  • Sun, Zhao-Hui, Renjun Liu, and Xinguo Ming. 2018. “A Fault Diagnosis and Maintenance Decision System for Production Line Based on Human-Machine Multi- Information Fusion.” In Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference, AICCC '18, 151–156. New York, NY: Association for Computing Machinery.
  • Sun, Yanning, Wei Qin, Zilong Zhuang, and Hongwei Xu. 2021. “An Adaptive Fault Detection and Root-cause Analysis Scheme for Complex Industrial Processes Using Moving Window KPCA and Information Geometric Causal Inference.” Journal of Intelligent Manufacturing 32: 2007–2021.
  • Tarakci, Hakan. 2016. “Two Types of Learning Effects on Maintenance Activities.” International Journal of Production Research 54 (6): 1721–1734.
  • Xu, Zhaoguang, and Yanzhong Dang. 2020. “Automated Digital Cause-and-effect Diagrams to Assist Causal Analysis in Problem-solving: a Data-driven Approach.” International Journal of Production Research 58 (17): 5359–5379.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.