Multi-criteria sensor placement determination in prognostics and health management using combined fault diagnosis, fault detection, and risk indexes

Mohammad Pourgol-Mohammad and Farzin Salehpour-Oskouei

Journal of Risk and Reliability, 2023, vol. 237, issue 6, 1234-1247

Abstract: In framework of a prognostics and health management (PHM), a stochastic method is proposed for optimal sensor locations determination using three independent criteria indexes, reflecting the efficiency of sensor network: (i) the uncertainty of sensor information, the ability of fault diagnosis of a sensor network, (ii) the reliability of sensors, reflecting the fault detectability in sensor networks, (iii) risk of sensor failure including the consequence of sensor failure scenarios. The dynamic system failure model is developed and analyzed to consider the variation of environmental factors and their related failure threshold characterization along with statistical variance estimation as the information value that each possible sensor placement scenario provides through sensor information. A dynamic failure model is developed to incorporate the interaction of sensors and their corresponding components. It is demonstrated here that considering each criterion independently will not comprehensively determine the optimal placement scenario. An augmented index is formulated through Shannon Entropy theory, ranking all sensor placement scenarios based on proposed combined index. Optimization of sensor placement is demonstrated on a typical steam turbine. As shown in detail, the ranking based on each criterion provides different inconsistent rank for the location of the sensors. The combined index is found practically proper and recommended for rank and selection process.

Keywords: Fault diagnosis; information uncertainty; optimal sensor placement; risk-based optimization; sensor reliability (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:237:y:2023:i:6:p:1234-1247

DOI: 10.1177/1748006X221114735

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