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      저자 : Reza Chini (검색결과 2 건)
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      • A Neural Network Fault Diagnosis Method Applied for Faults in Intake System of a Spark Ignition Engine Using Normalized Process Variables

        Reza Chini,Mohammadhassan Behroozi,Amir Hossein Shamekhi,Ehsan Samadani 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10

        One essential part of automated diagnosis systems for SI engines is due to elements of air path system. The faults occur in this subsystem can result in deviation of air-fuel ratio, which causes increased emissions due to incomplete combustion, misfire and especially loss of power and drivability problems. In this article, a model-based diagnosis system for air-path of an SI engine is constructed. Thus, an adiabatic nonlinear four-state dynamic model of an SI engine is utilized for fault simulations. In the next step, a diagnosis system is designed in the framework of Multilayer Perceptron (MLP) Artificial Neural Network (ANN) classifier. Simulation results show that the constructed diagnosis system for six fault modes considering all three kinds of common faults is applied effectively. In this paper, the Manifold Air Temperature (MAT) sensor, Fuel Injector (FAG) and Throttle Actuator (THAG) faults which comparatively have been evaluated less than other elements in previous relative neural network based works, are also taken into account. As another remarkable aspect of this work, all classes of faults are diagnosed in their full possible over reading (positive) and under reading (negative) ranges.

      • A GA-based Comparative Study of DI Diesel Engine Emission and Performance Using a Neural Network Model

        Ehsan Samadani,Mohammadhassan Behroozi,Amirhossein Shamekhi,Reza Chini 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10

        In diesel engines, applying design techniques such as computer simulations has become a necessity in view of the fact that these methods can result in small amounts of NOx and SOOT and a reasonable fuel economy. To achieve such a target, multi-objective optimization methodology is a good choice In this paper, this technique is implemented on a closed cycle two-zone combustion model of a DI diesel engine. The combustion model is developed by Matlab programming and validated by a single cylinder Ricardo data obtained from the engine. The main outputs of this model are NOx, SOOT and engine performance. The optimization goal is to minimize NOx and SOOT at the same time while maximizing engine performance. Injection timing, injection duration and AFR (Air-fuel ratio) are selected from engine inputs as design variables. A neural network model of the engine is developed based on model data as an alternative for the complicated and time-consuming combustion model in a wide range of engine operation. Design variables are optimized using GA (Genetic Algorithm). Here, three common algorithms for multi-objective optimization, MOGA, NSGA-II, and SPEA2+ are applied and the results are compared.

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