Sensor Set Decomposition for Enhanced Distributed Sensor Fault Isolability of Marine Propulsion Systems

Abstract

This paper proposes a greedy stochastic optimization algorithm for the sensor set decomposition used in the sensor fault monitoring of marine propulsion systems, based on fault isolability criteria. These criteria are expressed mathematically in terms of the number of unique columns in the theoretical fault signature matrices (FSMs) used during the sensor fault isolation process. Due to the large scale and complexity of marine propulsion plants, the diagnostic layer follows a distributed architecture with a combinatorial logic used for fault isolation in two cyber levels; the local and global decision logic. As a result, the FSMs of both levels are formulated as an integrated optimization problem. Each solution regarding the sensor set decomposition is then used to generate the respective distributed monitoring architecture, using semantic (qualitative) knowledge for the propulsion plant. Thus, the need for an analytical model of the plant is removed. Moreover, based on the design of the distributed monitoring architecture, the respective theoretical FSMs (quantitative) are Automatically generated and used for the evaluation of the objective function. Finally, simulation results are used to illustrate the application of the greedy stochastic optimization algorithm and its efficiency.