Research Of Monitoring And Fault Diagnosis System For Ship’s Engine Room Based On Labview

With the rapid development of shipping and shipbuilding industry, and the increasingcomplexity of the structure of the ship’s equipment, we must put forward higherrequirements for the automation of the ship, in order to ensure the stability and reliability ofthe cabin surveillance system. The cabin surveillance system can real-time collection anddisplay the operating conditions of auxiliary equipment in the main cabin, so the staff onboard can observe the changes in the operating parameters of the various parts of the cabin.This article describes the development status of the cabin monitoring system and faultdiagnosis, achieved the overall design of the cabin monitoring and fault diagnosis systemwith the virtual instrument technology combined with a modular design. This system cancomplete the function of data acquisition and display, real-time alarm, data management,historical parameter query on the carbin, combined with extraction of characteristicparameters of signal acquisition time domain analysis, and wavelet analysis method ofsignalanalysis and processing, and to lay the foundation for subsequent fault diagnosis.Finally used the new grey prediction model to predict the fault and achieved good effects, itcan effectively guide the ship staff regularly on engine component repair and maintenancework.Now more and more people choose to use neural networks to achieve artificialintelligence methods about fault diagnosis, but it requires the use of a large number of faultsamples of neural network to train the networks before diagnosis, data samples are easy toobtain in the normal operating state, while the fault samples are difficult to obtain. Thefuzzy nearness of the pattern recognition theory can be a good solution to this problem.Inthe early fault diagnosis of no failure data, technical personnel check the cause of the faultand fault characteristics corresponding to the value of the diagnostic fault samples over timewhen the fault alarm happens, then we can use the fuzzy nearness model to diagnos.Usingthe fuzzy nearness degree can have contrast the diagnosis of fault data samples in real timeto the diagnostic standard of fault symptom data, finally get the fault reason, and better forequipment fault diagnosis. In order to improve the validity and accuracy on marine engineroom equipment early fault diagnosis, using fuzzy nearness method to solve this problem,and the use of engine fuel system of the experimental data indicates that the traditionalfuzzy nearness method in fault diagnosis is insufficient, and has improved the traditionalfuzzy nearness formula. According to the principle of fuzzy degree of nearness, constructs a fuzzy nearness function model. And used the experimental data to verify the model, theexperimental results show that, using the improved fuzzy nearness method is superior to theconventional fuzzy nearness method, it can effectively accomplish the fuel system faultdiagnosis, which can satisfy the actual demands in ship fault diagnosis.