Study On Method Of Random Stress Response Process For Ship Structure Health Monitoring

Ships are subjected to long-term complex environmental loads during the maritime navigation process,which will cause structural fatigue damage.Ship structural damage analysis and safety status assessment are essential to ensure the safety of ships and the safety of life and property.Traditional spectral analysis is often used to predict fatigue damage at key locations of structures.Because of the large differences in the types of loads and parameters of various classification societies,the results predicted by traditional spectral analysis have large differences under different specifications.At the same time,statistical methods cannot be used for fast,real-time assessment of the structural safety status.With the development of signal processing and sensor technology,structural health monitoring technology has been applied in the fields of ship and ocean engineering.For ship structures,the structural fatigue life is usually predicted by directly monitoring the stress response at the hot spot location.However,the hot spot location is affected by the structural form and construction process.It is thus difficult to monitor the stress state of the hot spot directly in many cases.There are some studies conducted to extrapolate the stress state of the hot spot by monitoring the stress response near the hot spot.Since ships works in complex marine environment,the monitoring stress response is susceptible to external environmental factors so that it cannot be directly used for structural safety assessment.Therefore,it is necessary to analyze the structural characteristics of the area near the critical position of the ship,to develop a more reasonable and accurate structural monitoring plan and to study the characteristics of monitoring load and random load analysis and processing methods.Hence,the real-time stress state of the ship's structural key position can be obtained accurately in time,which is significant for ship structural damage analysis and safety status assessment.Based on the structural health monitoring technology,this thesis studies the random load analysis and processing method,and analyzes and verifies the elliptic relationship between the different nodes of the ship structure under the action of regular wave.An identification method of working condition of ship navigation and a prediction method of structural boundary load are proposed.The layout scheme of ship local structure monitoring sensor is also designed.By determining the basic working conditions of the ship's navigation,the stress relationship ellipse between each monitoring point and the structural nodes under the basic working conditions is calculated.A basic working condition database of the ship navigation is established for working condition identification and load prediction.At the same time,the measured load of the ship structure monitoring point under actual working conditions is simulated by numerical simulation method.Using the random load analysis method,the identification method of working condition based on stress ellipse characteristic parameters and the prediction method of the hull local structure boundary load are proposed in this thesis.The condition identification and boundary load prediction of local model of a ship under actual working conditions is realized.The ship local structure monitoring method proposed in this thesis can meet the requirements of real-time acquisition of hot spot stress state in real ship monitoring.The random load analysis and processing method adopted achieved a good effect in reducing the monitoring error caused by the external environment.In addition,by comparing the finite element calculation results,the feasibility and accuracy of the condition identification method and boundary load prediction method based on the stress elliptic relationship between structural nodes under the rule wave are verified.The results of this study can provide a reference for real-time assessment of the structural safety status of ships based on health monitoring.