Sensor placement determination in system health monitoring process based on dual information risk and uncertainty criteria

Abstract

Validity of sensor data is a challenge in system monitoring due to stochastic nature of failure occurrence. The quantity and location of sensors affect the system health information, while sensor malfunction causes misleading results about the system condition. Occurred economic losses are irrecoverable expenses in respect to the monitoring system as well as the system failure. In this study, a dual index approach is proposed for the determination of sensor placement scenarios based on two criteria: (1) uncertainty of sensor information and (2) risk of sensor failure. With about variation of environmental factors conditions (e.g. temperature) and their failure threshold characterization, system failure model is developed and analyzed by a proposed efficient Monte Carlo simulation. Statistical variance of sensor information about estimating of system state as the quantitative uncertainty measure of choice in this research is estimated according to the information value that each possible sensor placement scenario provides through sensor information. In the next phase, risk index is determined based on sensor malfunction and corresponding quantifiable losses through both failure costs and maintenance expenditure. All feasible combinations of sensor failures are considered in the risk model. Finally, a combinatorial criterion is determined through information entropy calculation, which considers both indexes proposed above simultaneously. Sensor placement scenarios are comparatively prioritized based on this criterion. Accordingly, the technical directions are provided for suitability of the criteria for prioritizing sensor arrangement in various systems with different reliability-based characteristics. As a case study, determination of sensor placement is demonstrated on a typical steam turbine. According to the low variation of both information uncertainty and risk indexes, it is concluded that the combinatorial index is the proper criterion for sensor placement determination in health monitoring process of the steam turbine.

Keywords

False alarm information uncertainty missed alarm sensor placement determination risk-based determination system health monitoring

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References

Kammer

. Sensor placement for on-orbit modal identification and correlation of large space structures. J Guid Control Dynam 1991; 14(2): 251–259.

Yao

Sethares

Kammer

. Sensor placement for on-orbit modal identification via a genetic algorithm. AIAA J 1993; 31(10): 1922–1928.

Kammer

Tinker

. Optimal placement of triaxial accelerometers for modal vibration tests. Mech Syst Signal Pr 1993; 18(2): 29–41.

Rosich

Sarrate

Nejjari

. Optimal sensor placement for FDI using binary integer linear programming. In: 20th International Workshop on Principles of Diagnosis, DX09, 235–242.

Sarrate

Puig

Escobet

et al . Optimal sensor placement for model-based fault detection and isolation. In: Proceedings of the 46th IEEE conference on decision and control, New Orleans, LA, 21 January 2007. New York: IEEE.

Jackson

Mosleh

. Bayesian inference with overlapping data for systems with continuous life metrics. Reliab Eng Syst Safe 2012; 106: 217–231.

Jackson

Modarres

. Sensor-based Bayesian inference and placement: review and examples. Int J Perform Eng 2016; 12(1): 13–32.

Pourali

Mosleh

. A Bayesian approach to functional sensor placement optimization for system health monitoring. In: Proceedings of the IEEE conference on prognostics and health management, Denver, CO, 18–21 June 2012. New York: IEEE.

Bhushan

Narasimhan

Rengaswamy

. Sensor network reallocation and upgrade for efficient fault diagnosis. In: Proceedings of the fourth international conference on foundations of computer-aided process operations, Coral Springs, FL, 12–15 January 2003, 443–446.

10.

Bhushan

Rengaswamy

. Comprehensive design of a sensor network for chemical plants based on various diagnosability and reliability criteria. 2. Applications. Ind Eng Chem Res 2002; 41(7): 1840–1860.

11.

Guratzsch

Mahadevan

. Structural health monitoring sensor placement optimization under uncertainty. AIAA J 2010; 48(7): 1281–1289.

12.

Assaf

Dugan

. Diagnosis based on reliability analysis using monitors and sensors. Reliab Eng Syst Safe 2008; 93: 509–521.

13.

Longhi

AEB

Pessoa

De Almada Garcia

. Multiobjective optimization of strategies for operation and testing of low-demand safety instrumented systems using a genetic algorithm and fault trees. Reliab Eng Syst Safe 2015; 142: 525–538.

14.

Duan

Dong

et al . Optimal sensor placement for fault diagnosis based on diagnosis cost specifications. J Comput Inform Syst 2011; 7(9): 3253–3260.

15.

Farhad

Reza

. Imprecise Shannon’s entropy and multi attribute decision making. Entropy 2011; 12: 53–62.

16.

Salehpour-Oskouei

Pourgol-Mohammad

. Optimal sensor placement for efficient fault diagnosis in condition monitoring process; a case study on steam turbine monitoring. In: Kumar

Ahmadi

Verma

et al . (eds) Current trends in reliability, availability, maintainability and safety part of the series lecture notes in mechanical engineering. Lulea: Springer, 2015, pp.83–97.

17.

Salehpour-Oskouei

Pourgol-Mohammad

. Fault diagnosis improvement using dynamic fault model in optimal sensor placement: a case study of steam turbine. Qual Reliab Eng Int 2016; 33: 531–541.

18.

Modarres

Kaminskiy

Krivtsov

. Reliability engineering and risk analysis: a practical guide. New York: Marcel Dekker, Inc., 1999.

19.

Fraden

. Handbook of modern sensors: physics, design and applications. 4th ed.New York: Springer, 2010.

20.

Yang

Xiao

Shah

. Optimal sensor location design for reliable fault detection in presence of false alarms. Sensors 2009; 9: 8579–8592.

21.

Szidarovszky

Gersbon

Duckstein

. Techniques for multi-objective decision making in systems management. Amsterdam: Elsevier, 1986.

22.

Heinz

Murari

. Steam turbines: design, applications, and rerating. 2nd ed.New York: McGraw-Hill, 2009.

23.

Gunnarsson

. Maintenance of the steam turbines at Hellisheidi power plant, faculty of industrial engineering. Master’s Thesis, Mechanical Engineering and Computer Science, School of Engineering and Natural Science, University of Iceland, Reykjavik, 2013.

24.

Denson

Chandler

Crowell

et al . None-electronic parts reliability data. Rome, NY: Reliability Analysis Center, 1991.

25.

Salehpour-Oskouei

Pourgol-Mohammad

. Risk assessment of sensor failures in a condition monitoring process; degradation-based failure probability determination. Int J Syst Ass Eng Manag 2017; 8: 584–593.

26.

Salehpour-Oskouei

Pourgol-Mohammad

. Degradation evaluation on sensor network optimization in fault diagnosis process. In: Proceedings of the 23th international mechanical engineering congress and exposition, Houston, TX, 13–19 November 2015. New York: ASME.

27.

Poore

Walford

. Development of an operations and maintenance cost model to identify cost of energy savings for low wind speed turbines. Subcontract Report, National Renewable Energy Laboratory (NREL), U.S. Department of Energy, Washington, DC, 20 July–30 June 2008.