Risk-Informed Graded Approach for Reliability and Performance Assessment of Sensor and Instrumentation Systems Within Advanced Condition Monitoring Technologies

Advanced condition monitoring (ACM) technologies, such as digital twins, are innovative strategies
designed to provide real-time health insights, including the remaining useful life of components.
The primary goal of ACM is to predict and alert operators to potential functional failures before they
occur. ACM systems achieve this by integrating predictive models with various sensor instrumentation,
analog-to-digital converters, data warehouses, and data preprocessors. These
sensor and instrumentation systems (SIS) are essential for forming a comprehensive understanding
of component conditions and ensuring the predictive success of ACM programs. Introducing new technologies like
ACM, and ACM accompanying SIS, involves varying degrees of risk that can
impact plant reliability. Therefore, risk mitigation should be commensurate with the performance
and reliability of the developed technology, following a risk-informed, graded approach (RIGA). Establishing an
RIGA process requires a clear understanding of the hazards and reliability of all subsystems, including their
interdependencies and potential impacts on the overall system. Given
the critical role of SIS in ACM, this work reviews hazard identification and reliability quantification methods for
SIS. It also considers the implications of these methods when developing an RIGA process for ACM.