Abstract
Production line calibration is a critical industrial task that requires thoroughly planned actions. Even tiny deviations from the optimal settings can cause dramatic deficiencies. Automated Root Cause Analysis can be employed to suggest the actions that result in faulty states, and therefore, to resolve situations and prevent recurrence. This work presents a methodology for Root Cause Analysis focused on the calibration process of a valve block in an elevator system. The causalities (weighted interconnections) between oil flow control (actions) and system velocity (output) are estimated using Pearson Correlation. The produced weight matrix is evaluated by exploiting expert knowledge. An FCM model for Root Cause Analysis is developed to study the system behavior and explore the root causes of deficiencies. The proposed approach eliminates the need for labeled root causes. Results support the efficiency of the proposed FCM model for correcting the sub-optimal configurations; the proposed approach seems to work even when the calibration actions are unknown.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Inglis, J.: Hydraulic lifts. In: Elevator Technology 6 Proceedings of Elevcon 1995, p. 153 (1995)
Kumar, R., Dwivedi, P.K., Praveen Reddy, D., Das, A.S.: Design and implementation of hydraulic motor based elevator system. In: 2014 IEEE 6th India International Conference on Power Electronics (IICPE), pp. 1–6 (2014). https://doi.org/10.1109/IICPE.2014.7115821
Xu, X., Wang, Q.: Speed control of hydraulic elevator by using PID controller and self-tuning fuzzy PID controller. In: 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 812–817 (2017). https://doi.org/10.1109/YAC.2017.7967521
Murthy, A.S., Taylor, D.G.: Control of a hydraulic elevator with a variable-speed pump. In: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, pp. 2245–2250 (2018). https://doi.org/10.1109/IECON.2018.8591577
Tziolas, T., Papageorgiou, K., Theodosiou, T., Papageorgiou, E., Mastos, T., Papadopoulos, A.: Autoencoders for anomaly detection in an industrial multivariate time series dataset. Eng. Proc. 18 (2022). https://doi.org/10.3390/engproc2022018023
Solé, M., Muntés-Mulero, V., Rana, A.I., Estrada, G.: Survey on models and techniques for root-cause analysis. arXiv preprint arXiv:1701.08546 (2017)
Martin-Delgado, J., MartÃnez-GarcÃa, A., Aranaz, J.M., Valencia-MartÃn, J.L., Mira, J.J.: How much of root cause analysis translates into improved patient safety: a systematic review. Med. Princ. Pract. 29, 524–531 (2020)
e Oliveira, E., Miguéis, V.L., Borges, J.L.: Automatic root cause analysis in manufacturing: an overview & conceptualization. J. Intell. Manuf. 1–18 (2022)
Jayswal, A., Li, X., Zanwar, A., Lou, H.H., Huang, Y.: A sustainability root cause analysis methodology and its application. Comput. Chem. Eng. 35, 2786–2798 (2011)
Abdelrahman, O., Keikhosrokiani, P.: Assembly line anomaly detection and root cause analysis using machine learning. IEEE Access 8, 189661–189672 (2020). https://doi.org/10.1109/ACCESS.2020.3029826
Papageorgiou, K., et al.: A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing (2022)
Wu, H., Zhao, J.: Deep convolutional neural network model based chemical process fault diagnosis. Comput. Chem. Eng. 115, 185–197 (2018)
Lokrantz, A., Gustavsson, E., Jirstrand, M.: Root cause analysis of failures and quality deviations in manufacturing using machine learning. Proc. CIRP 72, 1057–1062 (2018). https://doi.org/10.1016/j.procir.2018.03.229
Huang, D.J., Li, H.: A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing. Mater. Des. 203, 109606 (2021). https://doi.org/10.1016/j.matdes.2021.109606
Steenwinckel, B., et al.: FLAGS: a methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning. Future Gener. Comput. Syst. 116, 30–48 (2021). https://doi.org/10.1016/j.future.2020.10.015
Kosko, B.: Fuzzy cognitive maps. Int. J. Man Mach. Stud. 24, 65–75 (1986)
Papageorgiou, E.I., Salmeron, J.L.: A review of fuzzy cognitive maps research during the last decade. IEEE Trans. Fuzzy Syst. 21, 66–79 (2013). https://doi.org/10.1109/TFUZZ.2012.2201727
Kosko, B.: Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Prentice-Hall Inc., USA (1992)
Armstrong, R.A.: Should Pearson’s correlation coefficient be avoided? Ophthalmic Physiol. Opt. 39, 316–327 (2019)
Papageorgiou, E.I., Salmeron, J.L.: Methods and algorithms for fuzzy cognitive map-based modeling. In: Papageorgiou, E. (eds.) Fuzzy Cognitive Maps for Applied Sciences and Engineering. ISRL, vol. 54, pp. 1–28. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39739-4_1
Stylios, C.D., Groumpos, P.P., et al.: Mathematical formulation of fuzzy cognitive maps. In: Proceedings of the 7th Mediterranean Conference on Control and Automation, pp. 2251–2261. Mediterranean Control Association Nicosia, Cyprus (1999)
Acknowledgement
This work has been supported by EU Project OPTIMAI (H2020-NMBP-TR-IND-2020-singlestage, Topic: DT-FOF-11-2020, GA 958264). The authors acknowledge this support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tziolas, T. et al. (2023). Root Cause Analysis with Fuzzy Cognitive Maps and Correlation Coefficient. In: Massanet, S., Montes, S., Ruiz-Aguilera, D., González-Hidalgo, M. (eds) Fuzzy Logic and Technology, and Aggregation Operators. EUSFLAT AGOP 2023 2023. Lecture Notes in Computer Science, vol 14069. Springer, Cham. https://doi.org/10.1007/978-3-031-39965-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-39965-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-39964-0
Online ISBN: 978-3-031-39965-7
eBook Packages: Computer ScienceComputer Science (R0)