Fault isolability and sensor selection in underdetermined gas path fault diagnosis of gas turbine engines

Gas path fault diagnosis in high-performance gas turbines is often challenged by insufficient sensor availability, leading to underdetermined problems where fault isolation is easily confused with degradation in other components. In such cases, isolation results become unreliable when the number of sensors is smaller than the number of estimated health parameters, and multiple feasible solutions arise. To alleviate this issue, this paper introduces an isolability indicator for gas path faults under simultaneous unknown degradations, along with a sensor selection algorithm that maximizes isolability. The isolability of a component fault is defined as the probability of distinguishing it from faults in other components, and a sensor configuration performance metric is constructed from the isolabilities of all faults. Since calculating isolability through Monte Carlo (MC) simulations imposes a heavy computational burden, an approximation method based on clustering to reduce MC simulations is developed. A simplified indicator is designed to infer the change of isolability. And these simplified indicators can be classified by the geometric features of the fault symptoms sets. Building on this, an algorithm is proposed to determine the necessary MC simulations, further estimating the remaining isolability and reducing computational cost. The approach is validated on a high-bypass ratio turbofan engine model through digital simulations. Results demonstrate that the proposed indicator effectively characterizes component fault isolability, and that appropriate sensor selection enhances fault isolability in the presence of unknown degradation. Moreover, the algorithm successfully identifies optimal or near-optimal sensor configurations across different scenarios.