A Bayesian approach to sensor placement optimization and system reliability monitoring

Sensors are being increasingly used to monitor the functional state of complex systems. Sensors are used to make observation of physical quantities. The measured quantities are expected to provide information about the state of the system, its subsystems, components, and internal and external physical parameters. A complex system normally requires many sensors to extract required information from the sensed environment. In most cases, the problem of optimal sensor placement is difficult, because it requires optimization under uncertainty. This research developed new algorithms for sensor placement optimization under uncertainty and utilized them in a new system reliability monitoring approach. The overall methodology is designed to answer important questions such as how to infer the reliability of a system based on a limited number of sensor information points at certain subsystems (upward propagation); how to infer the reliability of a subsystem based on knowledge of the reliability of a main system (downward propagation); how to infer the reliability of a subsystem based on knowledge of the reliability of other subsystems (distributed propagation); and what are the optimum locations of sensors to provide the best estimate of system reliability.

[1]  Christopher Stephen Jackson.  Bayesian inference with overlapping data: Methodology and application to system reliability estimation and sensor placement optimization , 2011 .

[2]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[3]  J. Berger.  Statistical Decision Theory and Bayesian Analysis , 1988 .

[4]  L. Travé-Massuyès,et al.  Sensor Placement Optimisation Using Genetic Algorithms , 2004 .

[5]  Ali Mosleh,et al.  Hybrid causal methodology and software platform for probabilistic risk assessment and safety monitoring of socio-technical systems , 2010, Reliab. Eng. Syst. Saf..

[6]  Michael G. Pecht,et al.  Parameter selection for health monitoring of electronic products , 2010, Microelectron. Reliab..

[7]  Andreas Krause,et al.  Efficient Sensor Placement Optimization for Securing Large Water Distribution Networks , 2008 .

[8]  J.V. Nickerson,et al.  Computational Environmental Models Aid Sensor Placement Optimization , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.

[9]  Chris Jackson,et al.  Downwards propagating: Bayesian analysis of complex on-demand systems , 2010, 2010 Proceedings - Annual Reliability and Maintainability Symposium (RAMS).

[10]  K. M. Groth,et al.  Methodology and software platform for multi-layer causal modeling , 2008 .

[11]  Michael Pecht,et al.  Sensor System Selection for Prognostics and Health Monitoring , 2008 .

[12]  William E. Hart,et al.  Discrete sensor placement problems in distribution networks , 2005, Math. Comput. Model..

[13]  Richard E. Neapolitan,et al.  Learning Bayesian networks , 2007, KDD '07.

[14]  S. Sitharama Iyengar,et al.  Sensor placement for grid coverage under imprecise detections , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[15]  R. F. Guratzsch,et al.  SENSOR PLACEMENT OPTIMIZATION UNDER UNCERTAINTY FOR STRUCTURAL HEALTH MONITORING SYSTEMS OF HOT AEROSPACE STRUCTURES , 2007 .

[16]  Katrina M. Groth,et al.  A DATA-INFORMED MODEL OF PERFORMANCE SHAPING FACTORS FOR USE IN HUMAN RELIABILITY ANALYSIS , 2009 .

[17]  C. Guestrin,et al.  Near-optimal sensor placements: maximizing information while minimizing communication cost , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[18]  Steen Kristensen,et al.  Sensor Planning With Bayesian Decision Analysis , 1995 .

[19]  Hugh F. Durrant-Whyte,et al.  A Bayesian Approach to Optimal Sensor Placement , 1990, Int. J. Robotics Res..

[20]  John W. Fisher,et al.  Detection and Localization of Material Releases With Sparse Sensor Configurations , 2007, IEEE Trans. Signal Process..

[21]  Adnan Darwiche,et al.  Modeling and Reasoning with Bayesian Networks , 2009 .

[22]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[23]  Cynthia A. Phillips,et al.  SPOT: A Sensor Placement Optimization Toolkit for Drinking Water Contaminant Warning System Design. , 2007 .

[24]  C Jacksonn,et al.  Bayesian inference with overlapping data: methodology for reliability estimation of multi-state on-demand systems , 2012 .

[25]  Alyson G. Wilson,et al.  A fully Bayesian approach for combining multilevel failure information in fault tree quantification and optimal follow-on resource allocation , 2004, Reliab. Eng. Syst. Saf..

[26]  T. Régert,et al.  Pressure loss reduction of a Borda–Carnot sudden expansion applying a passive flow control method , 2012 .

[27]  R. Stolkin,et al.  Probability of Detection and Optimal Sensor Placement for Threshold Based Detection Systems , 2009, IEEE Sensors Journal.

[28]  Anindya Ghoshal,et al.  An Artificial Neural Receptor System for Structural Health Monitoring , 2005 .

[29]  Wright-Patterson Afb,et al.  Sensor Placement Optimization for SHM Systems Under Uncertainty , 2005 .

[30]  Perry Y. Li,et al.  Bayesian Belief Network Modeling and Diagnosis of Xerographic Systems , 2000, Dynamic Systems and Control: Volume 1.

[31]  M. S. Hamada,et al.  Using simultaneous higher-level and partial lower-level data in reliability assessments , 2008, Reliab. Eng. Syst. Saf..