A health monitoring system for engine dynamometer shafts is proposed. The solution is based on the real-time identification of the physical characteristics of the coupling shaft i.e. stiffness and damping coefficients, by using a physical oriented model and linear recursive identification. The efficacy of the suggested condition monitoring system is demonstrated on a prototype transient engine testing facility equipped with a coupling shaft capable of varying its physical properties. Simulation studies reveal that coupling shaft faults can be detected and isolated using the proposed condition monitoring system. Besides, the performance of various recursive identification algorithms is addressed. The results of this work recommend that the health status of engine dynamometer shafts can be monitored using a simple lumped-parameter shaft model and a linear recursive identification algorithm which makes the concept practically viable.
The increasing complexity in the development and manufacturing process of internal combustion engines leads to a higher demand for more effective testing and monitoring methods. Cold engine testing becomes progressively the main End-of-Line test which is used nowadays from automotive engine manufacturers with the purpose of determining the integrity of engine assembly. The present work is focused on the development of a detailed physics-based, lumped-parameter, dynamic model of a single cylinder internal combustion engine coupled with an alternating current transient dynamometer for cold engine testing applications. The overall transient engine test cell model is described based on a two-inertia system model consisting of the engine, the dynamometer and the coupling shaft. The internal combustion engine is modelled based on First Law of Thermodynamics and Second Newton’s Law for rotational bodies. The transient dynamometer is actually an alternating current three-phase induction motor which is modelled according to direct-quadrature axis approach, and its drive unit which is responsible for controlling the speed of the motor using indirect field orientation scheme. The engine and dynamometer are connected through a coupling shaft which is modelled as a compliant member with damping. The model is validated against experimental measurements such as engine cylinder pressure, engine excitation torque and alternating currents of the induction motor. All of the experimental measurements were recorded from an identical single cylinder transient engine test cell using a highly advanced instrumentation system. The described model serves as an ideal platform for developing innovative model-based fault detection and diagnosis techniques for cold engine testing applications. In conclusion, this is presented successfully for two simulated fault cases, a process fault and a sensor fault, proving the functionality and usefulness of the model.
A methodology for nonlinear recursive parameter estimation with parameter estimability analysis for physical and semiphysical engine models is presented. Orthogonal estimability analysis based on parameter sensitivity is employed with the purpose of evaluating a rank of estimable parameters given multiple sets of observation data that were acquired from a transient engine testing facility. The qualitative information gained from the estimability analysis is then used for estimating the estimable parameters by using two well-known nonlinear adaptive estimation algorithms known as extended Kalman filter (EKF) and unscented Kalman filter (UKF). The findings of this work contribute on understanding the real-world challenges which are involved in the effective implementation of system identification techniques suitable for online nonlinear estimation of parameters with physical interpretation.
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