Assumption-based Design of Hybrid Diagnosis Systems Analyzing Model-based and Data-driven Principles
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Published
Nov 5, 2024
Daniel Jung
Mattias Krysander
Abstract
Hybrid diagnosis systems combine model-based and data-driven methods to leverage their respective strengths and mitigate individual weaknesses in fault diagnosis. This paper proposes a unified framework for analyzing and designing hybrid diagnosis systems, focusing on the principles underlying the computation of diagnoses from observations. The framework emphasizes the importance of assumptions about fault modes and their manifestations in the system. The proposed architecture supports both fault decoupling and classification techniques, allowing for the flexible integration of model-based residuals and data-driven classifiers. Comparative analysis highlights how classical model-based and pure data-driven systems are special cases within the proposed hybrid framework. The proposed framework emphasizes that the key factor in categorizing fault diagnosis methods is not whether they are model-based or data-driven, but rather their ability to decuple faults which is crucial for rejecting diagnoses when fault training data is limited. Future research directions are suggested to further enhance hybrid fault diagnosis systems.
How to Cite
Jung, D., & Krysander, M. (2024). Assumption-based Design of Hybrid Diagnosis Systems: Analyzing Model-based and Data-driven Principles. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.4141
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Keywords
Fault diagnosis, Model-based diagnosis, Data-driven diagnosis
References
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Atoui, M., & Cohen, A. (2021). Coupling data-driven and model-based methods to improve fault diagnosis. Computers in Industry, 128, 103401. doi: 10.1016/j.compind.2021.103401
Atoui, M., Cohen, A., Verron, S., & Kobi, A. (2019). A single bayesian network classifier for monitoring with unknown classes. Engineering Applications of Artificial Intelligence, 85, 681–690. doi: 10.1016/j.engappai.2019.07.016
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Breiman, L. (2001). Random forests. Machine learning, 45, 5–32. doi: 10.1023/A:1010933404324 Camastra, F., & Staiano, A. (2016). Intrinsic dimension estimation: Advances and open problems. Information Sciences, 328, 26–41. doi: 10.1016/j.ins.2015.08.029
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Amin, A., & Hasan, K. (2019). A review of fault tolerant control systems: advancements and applications. Measurement, 143, 58–68. doi: 10.1016/j.measurement.2019.04.083
Atoui, M., & Cohen, A. (2021). Coupling data-driven and model-based methods to improve fault diagnosis. Computers in Industry, 128, 103401. doi: 10.1016/j.compind.2021.103401
Atoui, M., Cohen, A., Verron, S., & Kobi, A. (2019). A single bayesian network classifier for monitoring with unknown classes. Engineering Applications of Artificial Intelligence, 85, 681–690. doi: 10.1016/j.engappai.2019.07.016
Becraft, W. R., Lee, P. L., & Newell, R. B. (1991). Integration of neural networks and expert systems for process fault diagnosis. In Proceedings of the 12th international joint conference on artificial intelligence-volume 2 (pp. 832–837).
Breiman, L. (2001). Random forests. Machine learning, 45, 5–32. doi: 10.1023/A:1010933404324 Camastra, F., & Staiano, A. (2016). Intrinsic dimension estimation: Advances and open problems. Information Sciences, 328, 26–41. doi: 10.1016/j.ins.2015.08.029
Chen, H., Jiang, B., Ding, S., & Huang, B. (2020). Data-driven fault diagnosis for traction systems in high-speed trains: A survey, challenges, and perspectives. IEEE Transactions on Intelligent Transportation Systems, 23(3), 1700–1716. doi: 10.1109/TITS.2020.3029946
Commault, C., Dion, J., Sename, O., & Motyeian, R. (2002). Observer-based fault detection and isolation for structured systems. IEEE Transactions on Automatic Control, 47(12), 2074–2079.
Dai, X., & Gao, Z. (2013). From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis. IEEE Transactions on Industrial Informatics, 9(4), 2226–2238. doi: 10.1109/TII.2013.2243743
De Kleer, J., & Williams, B. (1987). Diagnosing multiple faults. Artificial intelligence, 32(1), 97–130. doi: 10.1016/0004-3702(87)90063-4
Destro, F., Facco, P., Munoz, S., Bezzo, F., & Barolo, M. (2020). A hybrid framework for process monitoring: Enhancing data-driven methodologies with state and parameter estimation. Journal of Process Control, 92, 333–351. doi: 10.1016/j.jprocont.2020.06.002
Feiyi, R., & Jinsong, Y. (2015). Fault diagnosis methods for advanced diagnostics and prognostics testbed (adapt): A review. In 2015 12th ieee international conference on electronic measurement & instruments (icemi) (Vol. 1, pp. 175–180). doi: 10.1109/ICEMI.2015.7494248
Frisk, E., Jarmolowitz, F., Jung, D., & Krysander, M. (2022). Fault diagnosis using data, models, or both–an electrical motor use-case. IFAC-PapersOnLine, 55(6), 533– 538. doi: 10.1016/j.ifacol.2022.07.183
Gao, Z., Cecati, C., & Ding, S. (2015). A survey of fault diagnosis and fault-tolerant techniques—part i: Fault diagnosis with model-based and signal-based approaches. IEEE transactions on industrial electronics, 62(6), 3757–3767. doi: 10.1109/TIE.2015.2417501
Garcia-Alvarez, D., Bregon, A., Pulido, B., & Alonso- Gonzalez, C. (2023). Integrating pca and structural model decomposition to improve fault monitoring and diagnosis with varying operation points. Engineering Applications of Artificial Intelligence, 122, 106145. doi: 10.1016/j.engappai.2023.106145
Ghosh, K., Ng, Y., & Srinivasan, R. (2011). Evaluation of decision fusion strategies for effective collaboration among heterogeneous fault diagnostic methods. Computers & chemical engineering, 35(2), 342–355. doi: 10.1016/j.compchemeng.2010.05.004
Goupil, L., Chanthery, E., Trav´e-Massuy`es, L., & Delautier, S. (2022). A survey on diagnosis methods combining dynamic systems structural analysis and machine learning. In 33rd international workshop on principle of diagnosis–dx 2022.
Gustafsson, F. (2007). Statistical signal processing approaches to fault detection. Annual Reviews in Control, 31(1), 41–54. doi: 10.1016/j.arcontrol.2007.02.004
Jung, D., Khorasgani, H., Frisk, E., Krysander,M., & Biswas, G. (2015). Analysis of fault isolation assumptions when comparing model-based design approaches of diagnosis systems. IFAC-PapersOnLine, 48(21), 1289– 1296. doi: 10.1016/j.ifacol.2015.09.703
Jung, D., Krysander, M., & Mohammadi, A. (2023). Fault diagnosis using data-driven residuals for anomaly classification with incomplete training data. IFACPapersOnLine, 56(2), 2903–2908.
Jung, D., Ng, K., Frisk, E., & Krysander, M. (2018). Combining model-based diagnosis and data-driven anomaly classifiers for fault isolation. Control Engineering Practice, 80, 146–156. doi: 10.1016/j.conengprac.2018.08.013
Jung, D., & Sundstr¨om, C. (2017). A combined datadriven and model-based residual selection algorithm for fault detection and isolation. IEEE Transactions on Control Systems Technology, 27(2), 616–630. doi: 10.1109/TCST.2017.2773514
Khorasgani, H., Farahat, A., Ristovski, K., Gupta, C., & Biswas, G. (2018). A framework for unifying modelbased and data-driven fault diagnosis. In Annual conference of the phm society (Vol. 10).
Lee, G., Han, C., & Yoon, E. (2004). Multiple-fault diagnosis of the tennessee eastman process based on system decomposition and dynamic pls. Industrial & engineering chemistry research, 43(25), 8037–8048. doi: 10.1021/ie049624u
Lee, G., Tosukhowong, T., Lee, J., & Han, C. (2006). Fault diagnosis using the hybrid method of signed digraph and partial least squares with time delay: The pulp mill process. Industrial & engineering chemistry research, 45(26), 9061–9074. doi: 10.1021/ie060793j
Liu, Z., & Zhang, L. (2020). A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings. Measurement, 149, 107002. doi: 10.1016/j.measurement.2019.107002
Lundgren, A., & Jung, D. (2022). Data-driven fault diagnosis analysis and open-set classification of time-series data. Control Engineering Practice, 121, 105006. doi: 10.1016/j.conengprac.2021.105006
Luo, J., Namburu, M., Pattipati, K., Qiao, L., & Chigusa, S. (2009). Integrated model-based and datadriven diagnosis of automotive antilock braking systems. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 40(2), 321– 336. doi: 10.1109/TSMCA.2009.2034481
Melo, A., Cmara, M., Clavijo, N., & Pinto, J. (2022). Open benchmarks for assessment of process monitoring and fault diagnosis techniques: a review and critical analysis. Computers & Chemical Engineering, 107964. doi: 10.1016/j.compchemeng.2022.107964
Mirnaghi, M., & Haghighat, F. (2020). Fault detection and diagnosis of large-scale hvac systems in buildings using data-driven methods: A comprehensive review. Energy and Buildings, 229, 110492. doi: 10.1016/j.enbuild.2020.110492
Mohammadi, A., Krysander, M., & Jung, D. (2022). Analysis of grey-box neural network-based residuals for consistency-based fault diagnosis. IFAC-PapersOnLine, 55(6), 1–6. doi: 10.1016/j.ifacol.2022.07.097
Mosterman, P., & Biswas, G. (1999). Diagnosis of continuous valued systems in transient operating regions. IEEE Transactions on Systems, Man, and Cybernetics- Part A: Systems and Humans, 29(6), 554–565. doi: 10.1109/3468.798059
Mylaraswamy, D., & Venkatasubramanian, V. (1997). A hybrid framework for large scale process fault diagnosis. Computers & chemical engineering, 21, S935–S940. doi: 10.1016/S0098-1354(97)87622-3
Odgaard, P., & Stoustrup, J. (2012). Results of a wind turbine fdi competition. IFAC Proceedings Volumes, 45(20), 102–107. doi: 10.3182/20120829-3-MX-2028.00015
Pernestl, A., Nyberg, M., & Warnquist, H. (2012). Modeling and inference for troubleshooting with interventions applied to a heavy truck auxiliary braking system. Engineering applications of artificial intelligence, 25(4), 705–719. doi: 10.1016/j.engappai.2011.02.018
Purbowaskito, W., Lan, C., & Fuh, K. (2024). The potentiality of integrating model-based residuals and machine learning classifiers: An induction motor fault diagnosis case. IEEE Transactions on Industrial Informatics, 20(2), 2822-2832. doi: 10.1109/TII.2023.3299111
Qin, S. J. (2012). Survey on data-driven industrial process monitoring and diagnosis. Annual reviews in control, 36(2), 220–234. doi: 10.1016/j.arcontrol.2012.09.004
Ruijters, E., & Stoelinga, M. (2015). Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools. Computer science review, 15, 29–62. doi: 10.1016/j.cosrev.2015.03.001
Sanchez, H., Escobet, T., Puig, V., & Odgaard, P. (2015). Fault diagnosis of an advanced wind turbine benchmark using interval-based arrs and observers. IEEE Transactions on Industrial Electronics, 62(6), 3783– 3793. doi: 10.1109/TIE.2015.2399401
Sankavaram, C., Kodali, A., Pattipati, K., & Singh, S. (2015). Incremental classifiers for data-driven fault diagnosis applied to automotive systems. IEEE access, 3, 407– 419. doi: 10.1109/ACCESS.2015.2422833
Scheirer,W., Jain, L., & Boult, T. (2014). Probability models for open set recognition. IEEE transactions on pattern analysis and machine intelligence, 36(11), 2317–2324. doi: 10.1109/TPAMI.2014.2321392
Senjen, R., De Beler, M., Leckie, C., & Rowles, C. (1993). Hybrid expert systems for monitoring and fault diagnosis. In Proceedings of 9th ieee conference on artificial intelligence for applications (pp. 235–241). doi: 10.1109/CAIA.1993.366605
Spreafico, C., Russo, D., & Rizzi, C. (2017). A stateof- the-art review of fmea/fmeca including patents. Computer Science Review, 25, 19–28. doi: 10.1016/j.cosrev.2017.05.002
Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., . . . Nenadic, G. (2019). Machine learning methods for wind turbine condition monitoring: A review. Renewable energy, 133, 620–635. doi: 10.1016/j.renene.2018.10.047
Svard, C., Nyberg, M., Frisk, E., & Krysander, M. (2013). Automotive engine fdi by application of an automated model-based and data-driven design methodology. Control Engineering Practice, 21(4), 455–472. doi: 10.1016/j.conengprac.2012.12.006
Theissler, A., Perez-Velazquez, J., Kettelgerdes, M., & Elger, G. (2021). Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability engineering & system safety, 215, 107864. doi: 10.1016/j.ress.2021.107864
Tidriri, K., Chatti, N., Verron, S., & Tiplica, T. (2016). Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges. Annual Reviews in Control, 42, 63–81. doi: 10.1016/j.arcontrol.2016.09.008
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