Study On Magnetic Leakage Detection And Path Planning Of A Crawling Robot For Oil Storage Tank Wall

With the rapid development of major domestic oil fields,the strategic reserve of crude oil has become the national strategic development goal,Therefore,the preservation of tank wall quality is particularly important,which requires the tank wall corrosion and defects do a good job Monitoring work.The quality and safety of oil storage tank wall is the prerequisite to ensure the safe and stable operation of petrochemical storage.The quality of storage tank wall anti-corrosion is an important index that directly affects the life and safety of the tank.Therefore,in the actual production and development not only to study the stability of the tank wall corrosion structure,the tightness of the welding process,but also to the tank wall defects and the overall quality of anti-corrosion strict control.With the development of automation and information technology,climbing robots begin to move toward automation and intelligence.Due to the constraints of the size and production environment of large storage tanks,there is an urgent need to improve the automation and intelligence of the detection of wall-climbing robots.In particular,research on detection efficiency,accuracy of detection and positioning,path planning,automatic control and the like can not meet the needs of practical applications.In view of the actual situation of oil tank wall inspection,we developed a wall leakage magnetic detection tanker robot to solve the problem of detection of tank wall's comprehensiveness,accuracy and efficiency.Based on the detection of wall-climbing robots based on oil storage tank wall,a smart magnetic-leakage testing wall-climbing robot with autonomous path planning is developed.The main contents are as follows:(1)Aiming at the working characteristics of the current mainstream climbing wall detection robot,a main body structure of a climbing wall robot with higher integration and more energy saving is designed.The detection system mainly includes two parts: hardware magnetic flux leakage detection device and software data processing and analysis.In the aspect of structure,the requirements of on-site inspection of oil storage tank wall are strictly considered,and a comprehensive analysis on the working environment,static force and dynamic force,movement state and load capacity is carried out.According to the demand analysis,the corresponding solutions are designed to achieve the effect of safe and stable operation of climbing robots.(2)A magnetic flux leakage testing method based on ultrasonic thickness measurement and compound excitation is proposed.In view of the gradual change of wall thickness on the site,we use ultrasonic thickness measurement technology to monitor the wall thickness of the tank in real time.And then according to the different thickness of the tank wall to adjust the size of the current excitation,and always maintain the tank wall surface magnetic flux density within a reasonable range,so as to ensure the accuracy and effectiveness of magnetic flux leakage testing.(3)Data processing,due to the detection process will inevitably be affected by the test equipment and the environment,etc.,the final collected magnetic flux leakage data will carry noise.In order to effectively solve the interference problem and accurately reproduce the MFL signal,this paper presents a method of MFL detection data processing based on the variational mode decomposition.Utilizing the characteristics of high SNR of VMD and the effectiveness of processing low frequency signals,the original signals collected are decomposed and the required MFLs are extracted and reconstructed according to the preset scales.Finally,the effectiveness of this method is verified by a laboratory platform.(4)This paper puts forward a realization path planning task climbing wall robot based on improved genetic algorithm.The main contents include: build the environment map model,localization and path optimization.First,simply introduce the method of robot map building and localization technology,the introduction of the working principle and the advantages and disadvantages of genetic algorithm,and finally put forward an improved genetic algorithm and simulation tests were carried out.In order to make up the standard genetic algorithm applied in path planning to create a feasible path for the defects,the fitness function and adaptive mutation probability theory,avoid falling into local optimal algorithm premature convergence in the early stage,improve the algorithm The efficiency of optimizing;The fireworks operator is used to improve the genetic algorithm to improve population diversity and enhance the global search ability of the algorithm.This paper uses MATLAB to simulate and analyze the standard genetic algorithm and the improved genetic algorithm.The simulation results show that the improved genetic algorithm can effectively improve the balance between exploration and development ability.It can effectively solve the path planning problem of wall-climbing robot.(5)The tank wall magnetic flux leakage detection system for the overall performance of field testing,field testing is mainly carried out in three steps.Firstly,the performance of climbing robot is tested,including three aspects: load capacity,mobility and obstacle clearance.Secondly,we test the autonomous planning and design of the climbing wall robot system and the magnetic flux leakage testing function on site.Finally,the tank wall leakage magnetic detection autonomous climbing robot to do the whole field test system.After testing,the standards of wall climbing robots all meet the design requirements.Then,the collected magnetic flux leakage data signals are analyzed and processed to deduce the type of defect in the tank wall and perform positioning.Contrasting with the defects of the tank wall on site,the accuracy of defect detection and localization was verified.