Study On Visualization System For Tank Floor Inspection Based On Magnetic Flux Leakage

Storage tanks are special equipments which play a very important role in storingliquid ingredients and intermediate products for oil stocks, port and petrochemicalenterprises. Serious environmental pollution will be triggered once leaking, which maybring great loss and damage to the national economy and people’s lives safety.Therefore, to strengthen the quality monitoring of storage tank, improve the detectionefficiency and the reliability of test results has become a basic condition to ensure thesafe operation of the storage tank. Although some of the more mature system has beendeveloped around the tank bottom detection at home and abroad, the existing defectquantitative accuracy is not high and software operating inconvenience is insufficientand testing efficiency needs to improve from the actual application situation.Firstly,24road hall sensor array composed of tank bottom plate magnetic fluxleakage probe are adopted to acquire signals, using the hybrid algorithm of combiningmedian filtering with least square for signal preprocessing to filter interference signalcaused by signal drift and random noise, which lay the foundation for the analysis oftesting data processing and defect data quantitative interpretation.Secondly, the framework of tank bottom data visualization software and functionmodule is introduced. Then each function module of the software which realizeparameter setting, data acquisition, data storage, data preprocessing, data playback,defect scope identification, quantification and data visualization and other functions.Synchronizing tasks and exchanging data are realized between multiple VI or same VIbut different threads with the event structure and queue structure.Finally, the method to generate defect distribution maps automatically for floorinspection is studied based on the magnetic flux leakage(MFL) technique. Firstly, theinitial scanning position of the first channel sensor in MFL detector is chosen asreference point, and the single scanning region can be defined by sensor channelnumbers and sampling interval; then the vertex coordinates of rectangle block diagonalare calculated by the sensor channels and sampling points of defect signals involved, ablock can be drawn to characterize the defect and the single defect distribution map isgenerated. And after the reference point coordinates are normalized under a unifiedcoordinate system, blocks characterized the same defect, which are drawn in twoadjacent scanning, are merged into a new one by combing operation. Finnally, the mapto show defects distributed on the whole tank floor are joined by single defectdistribution maps. The experimental results indicate that the proposed methods couldgenerate the defect distribution map of tank floor automatically, provide defect reportsfor tank floor inspection intuitively, and improve the efficiency, practicality andreliability of the tank floor inspection system.