Abstract
Root cause analysis (RCA) is the process through which we find the true cause of a problem. It is a crucial process in manufacturing, as only after finding the root cause and addressing it, it is possible to improve the manufacturing operation. However, this is a very time-consuming process, especially if the amount of data about the manufacturing operation is considerable. With the increase in automation and the advent of Industry 4.0, sensorization of manufacturing environments has expanded, increasing with it the data available. The conjuncture described gives rise to the challenge and the opportunity of automatizing root cause analysis (at least partially), making this process more efficient, using tools from data mining and machine learning to help the analyst find the root cause of a problem. This paper presents an overview of the literature that has been published in the last 17 years on developing automatic root cause analysis (ARCA) solutions in manufacturing. The literature on the topic is disperse and it is currently lacking a connecting thread. As such, this study analyzes how previous studies developed the different elements of an ARCA solution for manufacturing: the types of data used, the methodologies, and the evaluation measures of the methods proposed. The proposed conceptualization establishes the base on which future studies on ARCA can develop results from this analysis, identifying gaps in the literature and future research opportunities.
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Agrawal, V., Panigrahi, B., & Subbarao, P. (2016). Intelligent decision support system for detection and root cause analysis of faults in coal mills. IEEE Transactions on Fuzzy Systems, 25(4), 934–944. https://doi.org/10.1109/TFUZZ.2016.2587325.
Ahn, G., Hur, S., Shin, D., & Park, Y. J. (2019). A graphical model to diagnose product defects with partially shuffled equipment data. Processes,7(12), 12. https://doi.org/10.3390/pr7120934.
Asawachatroj, A., & Banjerdpongchai, D. (2012). Analysis of advanced process control technology and economic assessment improvement. Engineering Journal, 16, 1–4. https://doi.org/10.4186/ej.2012.16.4.1.
Barkia, H., Boucher, X., Le Riche, R., Beaune, P., Girard, M., & Rozier, D. (2013). Semiconductor yield loss’ causes identification: A data mining approach. In 2013 IEEE international conference on industrial engineering and engineering management (pp. 843–847). https://doi.org/10.1109/IEEM.2013.6962530.
Chemweno, P., Pintelon, L., Jongers, L., & Muchiri, P. (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) (pp. 1–7). https://doi.org/10.1109/ICPHM.2016.7542830.
Chen, W. C., Tseng, S. S., & Wang, C. Y. (2005). A novel manufacturing defect detection method using association rule mining techniques. Expert Systems with Applications, 29(4), 807–815. https://doi.org/10.1016/j.eswa.2005.06.004.
Chiang, L. H., Jiang, B., Zhu, X., Huang, D., & Braatz, R. D. (2015). Diagnosis of multiple and unknown faults using the causal map and multivariate statistics. Journal of Process Control, 28, 27–39. https://doi.org/10.1016/j.jprocont.2015.02.004.
Chien, C., & Chuang, S. (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. https://doi.org/10.1109/TSM.2014.2356555.
Chien, C. F., Hsu, C. Y., & Chen, P. N. (2013). Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence. Flexible Services and Manufacturing Journal, 25(3), 367–388. https://doi.org/10.1007/s10696-012-9161-4.
Chien, C. F., Liu, C. W., & Chuang, S. C. (2017). Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement. International Journal of Production Research, 55(17), 5095–5107. https://doi.org/10.1080/00207543.2015.1109153.
Cho, Y. S., & Kim, S. B. (2021). Quality-discriminative localization of multisensor signals for root cause analysis. IEEE Transactions on Systems, Man, and Cybernetics: Systems.https://doi.org/10.1109/TSMC.2021.3096529.
Choudhary, A. K., Harding, J. A., & Tiwari, M. K. (2009). Data mining in manufacturing: a review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20(5), 501.
Djelloul, I., Sari, Z., et al. (2018). Fault diagnosis of manufacturing systems using data mining techniques. In 2018 5th international conference on control, decision and information technologies (CoDIT), IEEE (pp. 198–203). https://doi.org/10.1109/CoDIT.2018.8394807.
Donauer, M., Peças, P., & Azevedo, A. (2015). Identifying nonconformity root causes using applied knowledge discovery. Robotics and Computer-Integrated Manufacturing, 36, 84–92. https://doi.org/10.1016/j.rcim.2014.12.012.
Du, S., Lv, J., & Xi, L. (2012). A robust approach for root causes identification in machining processes using hybrid learning algorithm and engineering knowledge. Journal of Intelligent Manufacturing, 23(5), 1833–1847. https://doi.org/10.1007/s10845-010-0498-9.
e Oliveira, E., Miguéis, V. L., & Borges, J. L. (2021). On the influence of overlap in automatic root cause analysis in manufacturing. International Journal of Production Research, 0(0), 1–17. https://doi.org/10.1080/00207543.2021.1992680.
e Oliveira, E., Miguéis, V. L., & Borges, J. L. (2022). Understanding overlap in automatic root cause analysis in manufacturing using causal inference. IEEE Access, 10, 191–201. https://doi.org/10.1109/ACCESS.2021.3139199.
Fan, S. K. S., Lin, S. C., & Tsai, P. F. (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. https://doi.org/10.1080/21681015.2015.1126654.
Gins, G., Van den Kerkhof, P., Vanlaer, J., & Van Impe, J. F. (2015). Improving classification-based diagnosis of batch processes through data selection and appropriate pretreatment. Journal of Process Control, 26, 90–101. https://doi.org/10.1016/j.jprocont.2015.01.006.
Gomez-Andrades, A., Barco, R., Serrano, I., Delgado, P., Caro-Oliver, P., & Munoz, P. (2016). Automatic root cause analysis based on traces forR LTE self-organizing networks. IEEE Wireless Communications, 23(3), 20–28. https://doi.org/10.1109/MWC.2016.7498071.
Gomez-Andrades, A., Munoz, P., Serrano, I., & Barco, R. (2016). Automatic root cause analysis for LTE networks based on unsupervised techniques. IEEE Transactions on Vehicular Technology, 65(4), 2369–2386. https://doi.org/10.1109/TVT.2015.2431742.
Hessinger, U., Chan, W. K., & Schafman, B. T. (2014). Data mining for significance in yield-defect correlation analysis. IEEE Transactions on Semiconductor Manufacturing, 27(3), 347–356. https://doi.org/10.1109/TSM.2014.2337251.
He, Z., Xu, X., & Deng, S. (2002). Squeezer: An efficient algorithm for clustering categorical data. Journal of Computer Science and Technology, 17, 611–624. https://doi.org/10.1007/BF02948829.
He, Y., Zhu, C., He, Z., Gu, C., & Cui, J. (2017). Big data oriented root cause identification approach based on axiomatic domain mapping and weighted association rule mining for product infant failure. Computers & Industrial Engineering, 109, 253–265. https://doi.org/10.1016/j.cie.2017.05.012.
Hsu, S. C., & Chien, C. F. (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. https://doi.org/10.1016/j.ijpe.2006.05.015.
Kitcharoen, N., Kamolsantisuk, S., Angsomboon, R., & Achalakul, T. (2013). Rapidminer framework for manufacturing data analysis on the cloud. In The 2013 10th international joint conference on computer science and software engineering (JCSSE), IEEE (pp. 149–154). https://doi.org/10.1109/JCSSE.2013.6567336.
Lee, C., Choy, K. L., Ho, G. T., Chin, K. S., Law, K., & Tse, Y. K. (2013). A hybrid olap-association rule mining based quality management system for extracting defect patterns in the garment industry. Expert Systems with Applications, 40(7), 2435–2446. https://doi.org/10.1016/j.eswa.2012.10.057.
Li, J., Khoo, L. P., & Tor, S. B. (2006). RMINE: A rough set based data mining prototype for the reasoning of incomplete data in condition-based fault diagnosis. Journal of Intelligent Manufacturing, 17(1), 163–176. https://doi.org/10.1007/s10845-005-5519-8.
Lin, M., Lucas, H. C., Jr., & Shmueli, G. (2013). Too big to fail: large samples and the p-value problem. Information Systems Research, 24(4), 906–917.
Liu, J., Hu, Y., Wu, B., & Wang, Y. (2018). An improved fault diagnosis approach for fdm process with acoustic emission. Journal of Manufacturing Processes, 35, 570–579. https://doi.org/10.1016/j.jmapro.2018.08.038.
Ma, Q., Li, H., & Thorstenson, A. (2021). A big data-driven root cause analysis system: Application of machine learning in quality problem solving. Computers & Industrial Engineering. https://doi.org/10.1016/j.cie.2021.107580.
Molnar, C. (2019). Interpretable Machine Learning.
Ong, P. L., Choo, Y. H., & Muda, A. K. (2015). A manufacturing failure root cause analysis in imbalance data set using pca weighted association rule mining. Jurnal Teknologi, 77(18), 103–111. https://doi.org/10.11113/jt.v77.6496.
Pearl, J. (2009). Causal inference in statistics: An overview. Statistics Surveys, 3, 96–146. https://doi.org/10.1214/09-SS057.
Radziwill, N. (2019). Root cause analysis and the tools you need to drive continuous improvement. https://blog.intelex.com/2019/03/20/root-cause-analysis-rca-central-to-continuous-improvement/, Accessed on 2019/11/21.
Rato, T. J., & Reis, M. S. (2015). On-line process monitoring using local measures of association. part ii: Design issues and fault diagnosis. Chemometrics and Intelligent Laboratory Systems, 142, 265–275. https://doi.org/10.1016/j.chemolab.2015.02.007.
Relihan, K., Geraghty, S., & O’Dwyer, A. (2007). Some aspects of process control in semiconductor manufacturing. In Proceedings of IMC-24; the 24th international manufacturing conference (pp. 1097–1104). Waterford Institute of Technology, August, 2007.
Richter, F., Aymelek, T., & Mattfeld, D. C. (2017). Automatic root cause analysis by integrating heterogeneous data sources. In Doerner, K. F., Ljubic, I., Pflug, G., Tragler, G. (ed) Operations Research Proceedings 2015, Austrian Operat Res Soc; German Operat Res Soc; Swiss OR Soc, Operations Research Proceedings (pp. 469–474). https://doi.org/10.1007/978-3-319-42902-1_63, Operations Research Conference (OR), Univ Vienna, Vienna, AUSTRIA, SEP 01-04, 2015.
Rokach, L., & Hutter, D. (2012). Automatic discovery of the root causes for quality drift in high dimensionality manufacturing processes. Journal of Intelligent Manufacturing, 23(5), 1915–1930. https://doi.org/10.1007/s10845-011-0517-5.
Sabet, S., Moniri, A., & Mohebbi, F. (2017). Root-cause and defect analysis based on a fuzzy data mining algorithm. International Journal of Advanced Computer Science and Applications, 8(9), 21–28. https://doi.org/10.14569/IJACSA.2017.080903.
Saez, M. A., Maturana, F. P., Barton, K., & Tilbury, D. M. (2019). Context-sensitive modeling and analysis of cyber-physical manufacturing systems for anomaly detection and diagnosis. IEEE Transactions on Automation Science and Engineering. https://doi.org/10.1109/TASE.2019.2918562
Sim, H., Choi, D., & Kim, C. O. (2014). A data mining approach to the causal analysis of product faults in multi-stage pcb manufacturing. International Journal of Precision Engineering and Manufacturing, 15(8), 1563–1573. https://doi.org/10.1007/s12541-014-0505-8.
Stasko, J., Görg, C., & Liu, Z. (2008). Jigsaw: Supporting investigative analysis through interactive visualization. Information Visualization, 7(2), 118–132. https://doi.org/10.1057/palgrave.ivs.9500180.
Steinhauer, H. J., Karlsson, A., Mathiason, G., & Helldin, T. (2016). Root-cause localization using restricted boltzmann machines. In 2016 19th international conference on information fusion (FUSION) (pp. 248–255).
Sun, Z. H, Liu, R., & Ming, X. (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, association for computing machinery, New York, NY, USA, AICCC ’18 (pp. 151–156). https://doi.org/10.1145/3299819.3299824.
Sun, Y., Qin, W., Zhuang, Z., & Xu, H. (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. https://doi.org/10.1007/s10845-021-01752-9.
Wang, Y., Liu, Y., Khan, F., & Imtiaz, S. (2017). Semiparametric pca and bayesian network based process fault diagnosis technique. The Canadian Journal of Chemical Engineering, 95(9), 1800–1816. https://doi.org/10.1002/cjce.22829.
Zanon, M., Susto, G. A., & McLoone, S. (2014). Root cause analysis by a combined sparse classification and monte carlo approach. IFAC Proceedings Volumes, 47(3), 1947–1952. https://doi.org/10.3182/20140824-6-ZA-1003.01885.
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e Oliveira, E., Miguéis, V.L. & Borges, J.L. Automatic root cause analysis in manufacturing: an overview & conceptualization. J Intell Manuf 34, 2061–2078 (2023). https://doi.org/10.1007/s10845-022-01914-3
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DOI: https://doi.org/10.1007/s10845-022-01914-3