Study On Intelligent Monitoring And Intelligent Diagnosis Of Marine Diesel Engine

Fault diagnosis theory and technique applying to marine diesel engine and equipment in marine engine room are systemically investigated in this thesis. A marine diesel engine is the power supply of a ship, and its healthful running is the guarantee to the safe voyage of the ship. The operation states of a diesel engine can be effectively monitored through a distributed system on board. Some fault diagnosis based on engine dynamic models, gray relation method, neural network, and information fusion are studied in this thesis to make effective diagnosis of engine faults.Monitoring is the foundation of diesel engine fault diagnosis. The structure of the monitoring system of marine engine, and faults detecting methods are firstly discussed in this paper. The advantages of using fieldbus in marine engine room are described, and a structure model of distributed marine engine room monitoring and controlling system based on fieldbus is designed in this paper.A diagnostic method for diagnosing a diesel engine cylinder faults according the engine transient speed waveform and gas pressure torque waveform is presented in the thesis. There is plentiful information in the engine transient waveform, which represents the torsional vibration on engine shaft and individual cylinder's working ability. The gas pressure due to engine combustion is the source power to drive the engine running, and the causation of the fluctuation of engine transient speed. Through the analyzing of the transient engine speed waveform and gas pressure torque waveform, the performance of each engine cylinder can be evaluated, and faults can be diagnosed. In the 3rd chapter, a diesel engine dynamic model for diagnosis use is established. Based on this model and the measurement of engine transient speed, the engine gas pressure torque waveform can be calculated. From the transient speed waveform and gas pressure torque waveform some characteristic parameters can be abstracted. The diagnosis method using these parameters and the measurement way of transient speed are described in detail.The gray theory has a wide range application in the fields of pattern cognition and fault diagnosis. In this thesis, the feasibility of applying gray relation degree method to diesel engine fault diagnosis is discussed; the speciality of applying gray relation to fault diagnosis is also studied; and an improved gray relation algorithm for diesel engine diagnosis is presented. It is essential to select proper parameters that can best represent system features in the engine diagnosis process. For the B-mode relation, the principle is given on how to use B-mode in fault diagnosis. To better representing different fault extent, a parameter field, which corresponds to different fault grade, is used to construct the standard fault model. This can improve the accuracy of fault classification. There is limitation for the gray relation fault diagnosis method in some case which typical fault model is difficult to establish. For cylinder faults in a multi-cylinder engine, an improved gray relation degree method, grey excellent and inferior relation method, can be use to analyze and evaluate thecylinder working sates, that is, how close the working states with the best states and worst states. And further an evaluation and fault diagnosis can be made.Neural network is an effect tool for fault diagnosis. In this thesis, several fault parameters, sampled from engine transient speed waveform and gas pressure torque waveform, are used to build a feed forward neural network, and the back-propagation (BP) algorithm is used to perform a case study of engine fault diagnosis. To overcome the disadvantageous of the BP algorithm, slow convergence speed and easy to fall into local small extremum, self-adjustable learning rate and momentum method are used to improve the performance of BP algorithm. The selection of learning rate and momentum coefficient are studied in detail. Using mathematical algorithm optimizing BP training can increase the network convergence speed. Case studies are made to compare these mathematical algorithms, and a guideline is given for the selection of neural network structure in fault diagnosis use. And, the application of Recurrent Network and Radius Basis Network to fault diagnosis is also primarily studied.Due to the complexity of a diesel engine and facilities in engine room, one monitoring or diagnosing method is sometime difficult to get an accurate result. Thus raise the demand for multi-sensor information fusion in fault detection, and integration of diagnostic method. The multi-sensor information theory, function model and structure are studied, and study case on diesel engine fault diagnosis using Bayesian Method and D-S Evidential Theory are given. To share the diagnosis information, and to increase the accuracy and reliability of fault detecting and diagnosis, a distributed multi-intelligent agent diagnostic structure can be used. In this paper, study work is mainly focus on the theory and structure of multi-intelligent agent diagnostic system, and multi-sensor information fusion theory and methods. Further should be done on the application of information fusion and integration.