The Study Of The Characteristic Of Acoustic Emission And Sources Identification Method For Damage Of The Key Structural Parts Of Truck Crane

As a widely used special equipment, truck crane has a large number in our country anddevelops with a fast growing speed. Affected by long time alternating load and working inpoor environment, there easily formed fatigue cracks, corrosion and other damage defectson the key structure of truck crane and which result in catastrophic accident to the wholeequipment. So, it is necessary to detect, analyze and study the damage. Because acousticemission (AE) technology can detect the early crack producing and expanding in metallicmaterials, thus it is a very suitable method to detect local damage in the structural parts.Recently detecting local damage in key structural components of the crane is still in theearly stage, and there is not a clear understanding for the characteristics of AE source.There is no effective method and strategy for their damage and then providing a dangerwarning, therefore the recognizing method to the typical AE source at the crane’s workingenvironment is still to be studied. Supported by the national high technology research anddevelopment program of china the thesis mainly include the following aspects:(1) Through the tensile testing of base materials and welded specimen made by HG70and Weldox960steel commonly used by truck crane, the characters of AE signals wereacquired, the changing rule and the sensitivity to the damage of which was studied, and the.AE signals of crack damage can be extracted by time-difference filtering method. Theexperiment showed that such parameters as RMS voltage was sensitive to the yieldingdeformation of material, according to this a warning method for yielding deformation ispresented based on the local minimum of RMS. By this method the warning-stress-rate is75%for HG70steel, and about90%for Weldox960steel. While such parameters ascentroid frequency, peak frequency and amplitude were effective indicators to monitor thecrack initiation and extension.(2) More than one parts were affected by bending load during the service process oftruck crane. Therefore, AE technology was used to monitor the process of three-pointbending test under laboratory conditions. The AE characteristics of different damage stageswere studied. An early warning method for crack damage was presented which was basedon Ib minimum value. The experiment showed that the Ib minimum value can predict the outcome of welding crack at least ahead of15seconds, and AE features such as amplitude,rise time, average frequency and RMS voltage were sensitive to the plastic deformationdamage of parent material, while such AE characteristics parameters as energy, peakfrequency can be regarded as sensitive parameters to the crack on the welding specimens.(3) A method which is used for optimization design to the inspection points and localdamage detection by AE technology is presented. Firstly the critical parts of truck crane,such as jib, chassis frame and outrigger, were modeled and analyzed by finite elementanalysis. The stress concentration and damage zones were determined by using stresstesting to the structural parts on load, and the local damage source can be located by usingfour-sensors arrays surface positioning mode. The experiment showed that this method caneasily determine two focus areas which must be monitored by AE technology. Leadbreaking test and actual loading test have both validated that this method also can locate thedamage source on the critical structural parts, such as crane, accurately.(4) A typical recognition method for AE source in truck crane is presented, which isbased on mixed characteristic vector and least squares support vector machine (LS-SVM).Firstly the mixed characteristic vector was constructed with wavelet packet analysis,time-domain and frequency-domain analysis, which can be optimized based on theevaluating factor calculated by distance between classes. Finally the result was input to theconstructed LS-SVM identification model to analyze. The analysis result to the typical sixkinds of AE signals commonly found in truck crane’s working condition showed that theidentification rates all exceeded93%by this model. This AE identification method wassuperior to the one based on artificial neural network.The research work in this dissertation will contribute to the comprehensiveunderstanding of AE sources of key parts damage of truck crane, and will provide atheoretical and experimental foundation for drafting standard of AE examination andevaluation．It is of vital importance and utility value to promote the application of AEtechnology in the related fields.