Probabilistic Boolean Network Modeling as an aid for DFMEA in Manufacturing Systems
- Pedro J. Rivera Torres 1
- Eileen I. Serrano Mercado 2
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1
Universidade de Vigo
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2
Polytechnic University of Puerto Rico
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Year of publication: 2016
Pages: 1-11
Congress: 18th Scientific Conference of Engineering and Architecture
Type: Conference paper
Abstract
Modeling manufacturing processes assists the design of new systems, allowing predictions of futurebehaviors, identifying improvement areas and evaluating changes to existing systems. Probabilistic BooleanNetworks (PBN) have been used to study biological systems, since they combine uncertainty and rule-basedrepresentation. A novel approach is proposed to model the design of an automated manufacturing assemblyprocesses using Probabilistic Boolean Networks (PBNs) to generate quantitative data for occurrenceassessment in Design Failure Mode and Effects Analysis (DFMEA). FMEA is a widely used tool in RiskAssessment (RA) to ensure design outputs consistently deliver the intended level of performance.Effectiveness of RA depends upon the robustness of the data used. Temporal logic is applied to analyze statesuccessions in a transition system, while interactions and dynamics are captured over a set of Booleanvariables using PBNs. Designs are therefore enhanced through assessment of risks, using proposed tools inthe early phases of design of manufacturing systems. A Two-Sample T test demonstrates the proposed modelprovides values closer to expected values; consequently modeling observable phenomena (p-value > 0.05).Simulations are used to generate data required to conduct inferential statistical tests to determine the level ofcorrespondence between model prediction and real machine data.
Bibliographic References
- Akutsu, T., Kosub, S., Melkman, A., & Tamura, T. (2012). Finding a Periodic Attractor of a BooleanNetwork. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(5), 1410–1421.
- Alwi, S., & Fujimoto, Y. (2014). Safety Properties Comparison Between Gröbner Bases and BDD-basedModel Checking Method. Presented at the 13th International Conference on Control AutomationRobotics and Vision.
- Arnosti, D. N., & Ay, A. (2012). Boolean modeling of gene regulatory networks: Driesch redux. Proceedingsof the National Academy of Sciences, 109(45), 18239–18240.
- Babiceanu, R. F., & Chen, F. F. (2006). Development and applications of holonic manufacturing systems: Asurvey. Journal of Intelligent Manufacturing, 17(1), 111–131.
- Berntensis, N., & Ebeling, M. (2013). Detection of attractors of large Boolean networks via exhaustiveenumeration of appropriate subspaces of the state space. BMC Bioinformatics, 14(361).
- Booker, L., Goldberg, D., & Holland, J. H. (1989). Clasifier systems and genetic algorithms. ArtificialIntelligence, 40(1–3), 235–282.
- Cicirello, V., & Smith, S. (2001a). Improved routing wasps for distributed factory control. In Proceedings ofthe Workshop on Artificial Intelligence and Manufacturing. Presented at the Workshop on ArtificialIntelligence and Manufacturing.
- Cicirello, V., & Smith, S. (2001b). Wasp nests for self-configurable factories. In Proceedings of the 5thInternational Conference on Autonomous Agents.
- Corry, P., & Kozan, E. (2004). Ant Colony Optimisation for Machine Kayout Problems. ComputationalOptimization and Applications, 28(3), 287–310.
- De Smet, O., & Rossi, O. (2002). Validation of a controller for a flexible manufacturing line written inLadder Diagram via model-checking. In Procedings of the American Control Conference. Presented atthe American Control Conference.
- Dorigo, M. (1992). Optimization, Learning, and Natural Algorithms (Doctoral Thesis). Politecnico diMilano, Milan, Italy.
- Dorigo, M., & Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical Computer Science,344(2–3), 243–278.
- Dougherty, E. R., Kim, S., & Chen, Y. (2000). Coefficient of determination in nonlinear signal processing.Signal Processing, 80, 2219–2235.
- Dubrova, E., & Teslenko, M. (n.d.). A SAT-Based Algorithm for Finding Attractors in Synchronous BooleanNetworks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(5), 1393–1399.
- Gang, X., & Wu, Z. (2003). The application and verification of Banker’s Algorithm for deadlock avoidancein Flexible Manufacturing System usomg SPIN. In Proceedings of ICRA ’03. Presented at the IEEEInternational Conference on Robotics and Automation.
- Guo, W., Yang, G., Wu, W., He, L., & Sun, M. (2014). A Parallel Attractor Finding Algorithm Based onBoolean Satisfiability for Genetic Regulatory Networks. PLoS ONE.
- Hopfensitz, M., Müssel, C., & Maucher, M. (2012). Attractors in Boolean networks: a tutorial.Computational Statistics.
- Huang, Y., McMurran, R., Dhadyalla, G., & Jones, R. P. (2008). Prob- ability based vehicle fault diagnosis:Bayesian network method. Journal of Intelligent Manufacturing, 19(3), 301–311.
- ICH Harmonised Tripartite Guideline: Quality Risk Management Q9(ICH Q9). (2005, November 9).International Conference on Harmonisation of Technical Requirements for Registration ofPharmaceuticals for Human Use.
- Jamhour, A., & García, C. (2012). Automation of industrial serial processes based on finite state machines.Presented at the 20th International Congress of Chemical and Process Engineering, Prague, CzechRepublic.
- Kaplan, S., & Garrick, B. J. (1981). On the quantitative definition of risk. In Risk Analysis (Vol. 1).
- Kauffman, S. A. (1969a). Metabolic stability and epigenesis in randomly constructed genetic nets. Journal ofTheoretical Biology, 22, 437–467.
- Kauffman, S. A. (1969b). Homeostasis and differentiation in random genetic control networks. Nature,(224), 177–178.
- Kumar, A., & Dhingra, A. K. (2012). Optimization of scheduling problems: A genetic algorithm survey.International Journal of Applied Science and Engineering Research, 1(1), 11–25.
- Kwiatkowska, M. Z., Norman, G., & Parker, D. (2011). PRISM 4.0: Verification of Probabilistic Real-TimeSystems. In Lecture Notes in Computer Science (Vol. 6806, pp. 585–591). Springer-Verlag.
- Li, P., Zhang, C., Perkins, E. J., Gong, P., & Deng, Y. (2007). Comparison of probabilistic Boolean networkand dynamic Bayesian network approaches for inferring gene regulatory networks. BMCBioinformatics, 8(13).
- Mazzolini, M., Brusaferri, A., & Carpanzano, E. (2010). Model-Checking based Verification Approach forAdvanced Industrial Automation Solutions. Presented at the IEEE Conference on EmergingTechnologies and Factory Automation.
- Moore, K., & Gupta, S. M. (1996). Petri net models of flexible and auto- mated manufacturing systems: Asurvey. International Journal of Production Research, 34(11), 3001–3035.
- Mosallam, A., Medjaher, K., & Zerhouni, N. (2014). Data-driven prog- nostic method based on Bayesianapproaches for direct remaining useful life prediction. Journal of Intelligent Manufacturing.
- Nouiri, M., Bekrar, A., Jemai, A., Niar, S., & Ammari, A. C. (2015). An effective and distributed particleswarm optimization algorithm for flexible job-shop scheduling problem. Journal of IntelligentManufacturing.
- Pal, R., Ivanov, I., Datta, A., Bittner, M. L., & Dougherty, E. R. (2006). Synthesizing Boolean networks witha given attractor structure. Genomic Signal Processing and Statistics, 2006. GENSIPS '06. IEEEInternational Workshop on, 73–74. doi:10.1109/GENSIPS.2006.353162
- Park, H.-S., & Tran, N.-H. (2010). An intelligent manufacturing system with biological principles.International Journal of CAD/CAM, 10(1), 39–50.
- Potential Failure Mode and Effects Analysis (FMEA), 3rd Edition, (2001). Automotive Industry ActionGroup.
- Qiu, Y., Tamura, T., Ching, W.-K., & Akutsu, T. (2014). On control of singleton attractors in multipleBoolean networks: integer programming-based method. BMC Systems Biology, 8(S7).
- Rausand, M., & Høyland, A. (2004). Systems Reliability Theory: Models, Statistical Methods, andApplications (2nd ed.). Hoboken, New Jersey: John Wiley and Sons.
- Samanta, B., & Nataraj, C. (2009). Application of particle swarm optimization and proximal support vectormachines for fault detection. Swarm Intelligence, 3(4), 303–325.
- Shmulevich, I., & Dougherty, E. R. (2010). Probabilistic Boolean Networks: Modeling and Control of GeneRegulatory Networks. Philadelphia, PA, USA: SIAM.
- Stamantis, D. H. (2003). FMEA: A General Overview. In Failure Mode and Effects Analysis: FMEA fromtheory to execution (Second., pp. 21–81). Milwaukee, Wisconsin, USA: ASQ Quality Press.
- Takatsuka, K., & Tomita, S. (2010). Modelling of Discrete Manufacturing Systems having multiple jobs forVerification by Model-Checking (pp. 1136–1141). Presented at the 8th IEEE International Conferenceon Industrial Informatics.
- Tchangani, A. P. (2004). Decision-making with uncertain data: Bayesian linear programming approach.Journal of Intelligent Manufacturing, 15(1), 17–27.
- Voronov, A., & Akesson, K. (2009). Verification of process operations using model checking. InProceedings of CASE 2009. Presented at the IEEE Conference on Automation Science andEngineering.
- Wang, X., Wang, H., & Qi, C. (2014). Multi-agent reinforcement learning based maintenance policy for aresource constrained flow line system. Journal of Intelligent Manufacturing.
- Wang, Y., & Wu, Z. (2003). Deadlock Avoidance Control Synthesis in Manufacturing Systems Using ModelChecking. In Proceedings of the American Control Conference. Presented at the American ControlConference.
- Zheng, D., Yang, G., Li, X., Wang, Z., Liu, F., & He, L. (2013). An Efficient Algorithm for ComputingAttractors of Sychronous and Asynchronous Boolean Networks. PLoS ONE.