Dependability analysis of instrumented linear static systems based on their observability

Abstract

ABSTRACTIn many applications, the ability of a system to operate correctly is related to the capacity to estimate its state variables. The quality of the estimated variables is strongly conditioned by sensors state, number and location. Availability and reliability are highly sought in industry where systems should continue operating even in the presence of some failures. This makes the problem of designing instrumentation systems very important in system control, diagnosis and reliability. In this paper, we propose an approach to assess the dependability of an instrumented system from its observability perspective. This approach is based on two analysis and computation stages. In the first one, the concept of analytical redundancy degree (ARD) for state estimation is discussed. The ARD reflects the process’ capacity degree to tolerate some sensors’ faults without affecting its observability. In the second stage, the classical definition of system's reliability will be extended to the estimation of its state variables. Based on the ARD resulting from the first stage analysis, a stochastic activity networks-based modelling approach and Monte Carlo simulation will be used to assess the minimal reliability and availability of the system's state variables based on their observability. This study concerns material/energy transportation systems where variables are related to each other by static linear equations.