Set-membership condition monitoring framework for dual fuel engines

Condition monitoring and condition-based maintenance can reduce maintenance costs and improve operation safety of vehicles across the full spectrum of transportation domain, including automotive, rail, air and marine applications. In this paper, a set-membership condition monitoring framework is proposed to detect aging and health degradation for dual fuel engines based on simultaneous input and parameter estimation techniques. Dual fuel engines operate in two modes, a diesel mode or gas mode, and can burn either diesel fuel or natural gas fuel. To improve health parameter identifiability, we exploit transient data from mode transitions between the diesel mode and gas mode. Simulation results based on a nonlinear mean-value model of the engine are reported, demonstrating that the proposed set-membership estimation algorithms can provide tight overbound of the health parameters.