Bayesian Reliability Prognosis for Systems with Heterogeneous Information
A Bayesian methodology for prognosis of system reliability with heterogeneous reliability information is presented. Available information may be in the form of physics-based or experiment-based mathematical models, historical reliability data, or expert opinion. Such information typically describes the failure rates of individual components of the system and does not provide information on dependencies between them. The Bayesian methodology presented in this paper addresses this concern by learning the conditional probabilities in the Bayes network as observations about the system are made. First, the component and system faults are defined and the failure event tree is established. Bayesian priors for probabilities of both individual failure events and the conditional probabilities between them are established using various types of experimental data, expert opinion, or simulation data. Both the priors and conditional probabilities are updated as new data is collected, leading to an updated prognosis of system reliability. The methodology is demonstrated on an automobile startup system.
How to Cite
reliability, Bayesian, Conditional Probability, Dependence, prognosis, Bayesian updating
Andersen, B., & Fagerhaug, T. (2006). Root Cause Analysis: Simplified Tools and Techniques, Second Edition. ASQ Quality Press.
Bearfield, G., & Marsh, W. (2005). Generalising Event Trees Using Bayesian Networks with a Case Study of Train Derailment.
R. Winther, B. A. Gran, & G. Dahll (Eds.), Computer Safety, Reliability, and Security, Lecture Notes in Computer Science (Vol. 3688, pp. 52-66). Springer Berlin / Heidelberg. Retrieved from http://dx.doi.org/10.1007/11563228_5
Das, B. (2004, November). Generating Conditional Probabilities for Bayesian Networks: Easing the Knowledge Acquisition Problem. ArXiv Computer Science e-prints. Retrieved from http://adsabs.harvard.edu/abs/2004cs.11034D
Heckerman, D. (1995). A Tutorial on Learning With Bayesian Networks (Technical No. MSR-TR95-06). Microsoft Research. Retrieved from http://research.microsoft.com/apps/pubs/defau lt.aspx?id=69588
Høyland, A., & Rausand, M. (1994). System reliability theory: models and statistical methods. John Wiley & Sons, Inc.
Kumar, D. (2000). Reliability maintenance and logistic support: a life cycle approach. Kluwer Academic Publishers.
Lucas, P. (2002). Restricted Bayesian Network Structure Learning. In Advances in Bayesian Networks, Studies in Fuzziness and Soft Computing (pp. 217–232). Springer-Verlag.
Marsh, W., & Bearfield, G. (2007). Representing Parameterised Fault Trees Using Bayesian Networks.
F. Saglietti & N. Oster (Eds.), Computer Safety, Reliability, and Security, Lecture Notes in Computer Science (Vol.4680, pp. 120-133). Springer Berlin / Heidelberg. Retrieved from http://dx.doi.org/10.1007/978-3-540-751014_13
Martin, J., & VanLehn, K. (1995). A Bayesian Approach to Cognitive Assessment.
P. D. Nichols, S. F. Chipman, & R. L. Brennan (Eds.), Cognitively diagnostic assessment. Hillsdale, NJ: Lawrence Erlbaum Associates.
McCurry, J. (2010, February 17). Toyota to fit brakeoverride system in all future models. Guardian.co.uk. Retrieved from http://www.guardian.co.uk/business/2010/feb/ 17/toyota-brake-override-system-corolla
Mengshoel, O. J., Darwiche, A., & Uckun, S. (2008).Sensor Validation using Bayesian Networks.
Min, J., Jang, S., & Cho, S. (2009). Mining and Visualizing Mobile Social Network Based on Bayesian Probabilistic Model.
In D. Zhang, M. Portmann, A. Tan, & J. Indulska (Eds.), Ubiquitous Intelligence and Computing, Lecture Notes in Computer Science (Vol. 5585, pp. 111-120). Springer Berlin / Heidelberg. Retrieved from http://dx.doi.org/10.1007/978-3-642-028304_10
Patrick, R., Orchard, M. E., Zhang, B., Koelemay, M.D., Kacprzynski, G. J., Ferri, A. A., & Vachtsevanos, G. J. (2007). An integrated approach to helicopter planetary gear fault diagnosis and failure prognosis. In Autotestcon, 2007 IEEE (pp. 547 -552). doi:10.1109/AUTEST.2007.4374266
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann.
Rosenthal, M. (2010). Diagnose and Repair why Car doesn't Start or Stalls. Retrieved from http://www.ifitjams.com/starting.htm Sallis, E. (2002). Total quality management in education. Routledge.
Schlaifer, R. (1978). Analysis of decisions under uncertainty. R. E. Krieger Pub. Co.
Sierra, B., Inza, I., & Larrañaga, P. (2000). Medical Bayes Networks. In R. Brause & E. Hanisch (Eds.), Medical Data Analysis, Lecture Notes in Computer Science (Vol. 1933, pp. 1-49). Springer Berlin / Heidelberg. Retrieved from http://dx.doi.org/10.1007/3-540-39949-6_2
Stapelberg, R. F. (2009). Handbook of Reliability, Availability, Maintainability and Safety in Engineering Design. Spring-Verlag London Limited.
Toyota Consumer Safety Advisory: Potential Floor Mat Interference with Accelerator Pedal. (2009, September 29). Retrieved from http://pressroom.toyota.com/pr/tms/toyota/toy ota-consumer-safety-advisory-111943.aspx
Whoriskey, P. (2010, February 4). 2007 federal probe of Toyota complaints resolved nothing. The Washington Post. Retrieved from http://www.washingtonpost.com/wpdyn/content/article/2010/02/03/AR201002030 4056.html?hpid=topnews&sid=ST201002020 4001
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.