The Ship Cabin Intelligent Monitoring System Based On The Fcs

Practically, the reliability, maintainability, real-time control ability and interoperability are required by the monitor and control system for marine engine room. However the monitor system based on the multi-microprocessors and the hierarchical structure is not so satisfied in such requirements, owing to its complex structure and devoted communication sub-stations. In order to solve this problem, a design method of intelligent monitor system based on field-bus control system (FCS) for marine engine room is presented in this dissertation. To simplify the structure of the monitor and control system and to enhance its reliability, a monitoring network composed of a management computer and the multiple intelligent nodes is constructed using field-bus technology. In this system, each intelligent node can be installed at any control or measurement field to realize the functions of both data-acquisition and communication. Because the FCS features much higher performances as compared with DCS, it can be used as the updated substitute of DCS-based monitor system for marine engine room. In this dissertation, the design of the main functional units as well as the configuration of the monitor system is described. To supply much easier maintenance and configuration to the monitor system, a design method of the system software based on database technology is presented in this dissertation. All process parameters in the monitor and control system are managed by the databases, with one being used specially to manage the process points including the number and name of each sensor, the range of each measurement, the alarm limit and the measuring channel etc. Besides, some universal display modules are designed for the monitor software. A new adaptive optimal predictor based on diagonal recurrent neural network (DRNN) is present in this dissertation. And a new TD-DBP algorithm combined temporal difference algorithm with dynamic back-propagation algorithm is proposed for DRNN on-line learning. Usually, some important parameters must be determined firstly in a predictor. For example, the order of the system must be determined at advance for an adaptive predictor based on the forward or feedback neural network. But it might be difficult or impossible in some cases. Now the new predictor does not require deciding on any system parameter. For this reason, it is able to realize system modeling, parameter modifying and trends forecasting on line via learning ftom the time series of a real dynamic process. This predictor has the advantage in simple 3 network topology and short learning time. A program of predictive analysis based on DRNN is integrated in the monitoring software. All the theoretical methods proposed in this dissertation have been applied to build up an experimental monitor system at the Lab Center of Automatic Marine Engine Room in Shanghai Maritime University. The satisfactory results have been obtained by the experiments. Such design of the monitor and control system can simplify its structure, and enhance its ability of information processing, and promote its reliability and flexibility. Besides, this type of monitor system for marine engine room can be easily combined with other marine automatic systems to realize the information network of the total ship.