Abstract
In the present work, fault detection in industrial automation processes is investigated. A fault detection method for observable process variables is extended for application cases, where the observations of process variables are noisy. The principle of this method consists in building a probability distribution model and evaluating the likelihood of observations under that model. The probability distribution model is based on a hybrid automaton which takes into account several system modes, i.e. phases with continuous system behaviour. Transitions between the modes are attributed to discrete control events such as on/off signals. The discrete event system composed of system modes and transitions is modeled as a finite state machine. Continuous process behaviour in the particular system modes is modeled with stochastic state space models, which incorporate neural networks. Fault detection is accomplished by evaluation of the underlying probability distribution model with a particle filter. In doing so both the hybrid system model and a linear observation model for noisy observations are taken into account. Experimental results show superior fault detection performance compared to the baseline method for observable process variables. The runtime of the proposed fault detection method has been significantly reduced by parallel implementation on a GPU.
Keywords:
- fault detection
- hybrid systems
- filtering
Download chapter PDF
References
Christiansen, L., Fay, A., Opgenoorth, B., Neidig, J.: Improved diagnosis by combining structural and process knowledge. In: Emerging Technologies Factory Automation (ETFA), 2011 IEEE 16th Conference on. pp. 1–8 (2011)
Duda, R., Hart, P., Stork, D.: Pattern Classification (2nd Edition). Wiley-Interscience (2000)
Frey, C.: Diagnosis and monitoring of complex industrial processes based on selforganizing maps and watershed transformations. In: Computational Intelligence for Measurement Systems and Applications, 2008. CIMSA 2008. 2008 IEEE International Conference on. pp. 87–92 (2008)
Gao, Z., Cecati, C., Ding, S.X.: A Survey of Fault Diagnosis and Fault Tolerant Techniques. IEEE Transactions on Industrial Electronics 62(6) (2015)
Guo, J., Ji, D., Du, S., Zeng, S., Sun, B.: Fault diagnosis of hybrid systems using particle filter based hybrid estimation algorithm. Chem. Eng. Trans. 33(1), 145–150 (2013)
Harris, M.: Optimizing Parallel Reduction in CUDA. NVIDIA Developer Technology
Levy, P., Arogeti, S., Wang, D.: An integrated approach to model tracking and diagnosis of hybrid systems. IEEE Transactions on Industrial Electronics 61(4), 1024–1040 (2014)
Liu, W., Hwang, I.: Robust estimation and fault detection and isolation algorithms for stochastic linear hybrid systems with unknown fault input. Control Theory Applications, IET 5(12), 1353–1368 (2011)
Maier, A.: Online passive learning of timed automata for cyber-physical production systems. In: The 12th IEEE International Conference on Industrial Informatics (INDIN 2014). Porto Alegre, Brazil (2014)
Murray, L.M., Lee, A., Jacob, P.E.: Parallel Resampling in the Particle Filter. Accepted at Journal of Computational and Graphical Statistics (2015)
Narasimhan, S., Biswas, G.: Model-based diagnosis of hybrid systems. IEEE Transactions on systems, manufacturing and Cybernetics 37, 348–361 (2007)
Niggemann, O., Lohweg, V.: On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda. In: In:Twenty-Ninth Conference on Artificial Intelligence (AAAI-15) (2015)
Ristic, B., Arulampalam, S., Gordon, N.: Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House (2004)
Shui, A., Chen, W., Zhang, P., Hu, S., Huang, X.: Review of fault diagnosis in control systems. In: Control and Decision Conference, 2009. CCDC ‘09. Chinese. pp. 5324–5329 (2009)
Vodencarevic, A., Kleine Buning, H., Niggemann, O., Maier, A.: Identifying behavior models for process plants. In: Emerging Technologies & Factory Automation (ETFA), 2011 IEEE 16th Conference on. pp. 1–8 (2011)
Wang, M., Dearden, R.: Detecting and Learning Unknown Fault States in Hybrid Diagnosis. In: Proceedings of the 20th International Workshop on Principles of Diagnosis, DX09. pp. 19–26. Stockholm, Sweden (2009)
Windmann, S., Jiao, S., Niggemann, O., Borcherding, H.: A Stochastic Method for the Detection of Anomalous Energy Consumption in Hybrid Industrial Systems. In: INDIN (2013)
Windmann, S., Niggemann, O.: Automatic model separation and application to diagnosis in industrial automation systems. In: IEEE International Conference on Industrial Technology (ICIT 2015) (2015)
Windmann, S., Niggemann, O.: Automatic model separation and application to diagnosis in industrial automation systems. In: IEEE International Conference on Industrial Technology (ICIT 2015) (2015)
Windmann, S., Niggemann, O.: Efficient Fault Detection for Industrial Automation Processes with Observable Process Variables. In: IEEE International Conference on Industrial Informatics (INDIN 2015) (2015)
Windmann, S., Niggemann, O.: A GPU-Based Method for Robust and Efficient Fault Detection in Industrial Automation Processes. In: IEEE International Conference on Industrial Informatics (INDIN 2016) (2016)
Yu, M., Wang, D., Luo, M.: Model-based prognosis for hybrid systems with modedependent degradation behaviors. IEEE Transactions on Industrial Electronics 61(1), 546–554 (2014)
Zhao, F., Koutsoukos, X.D., Haussecker, H.W., Reich, J., Cheung, P.: Monitoring and fault diagnosis of hybrid systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B 35(6), 1225–1240 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
<p>This chapter is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, a link is provided to the Creative Commons license and any changes made are indicated.</p> <p>The images or other third party material in this chapter are included in the work's Creative Commons license, unless indicated otherwise in the credit line; if such material is not included in the work's Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce the material.</p>
Copyright information
© 2018 The Author(s)
About this chapter
Cite this chapter
Windmann, S., Niggemann, O. (2018). A Sampling-Based Method for Robust and Efficient Fault Detection in Industrial Automation Processes. In: Niggemann, O., Schüller, P. (eds) IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency. Technologien für die intelligente Automation, vol 8. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-57805-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-662-57805-6_5
Published:
Publisher Name: Springer Vieweg, Berlin, Heidelberg
Print ISBN: 978-3-662-57804-9
Online ISBN: 978-3-662-57805-6
eBook Packages: EngineeringEngineering (R0)