| Hydraulic support is a main equipment of the comprehensive mechanization mining, and it is of great significance for modern coal mine production. Hydraulic cylinder is a key actuator which completes various motions and bears roof pressure. Its quality directly affects the reliability of hydraulic support, and then affects safety and normal production of coal mine.Aiming at the problem of girth weld quality test for hydraulic cylinder, in order to conquer the disadvantages of manual ultrasonic A-scan testing and ensure weld quality, taking digital ultrasonic flaw detector as base, computer as core, virtual instrument programming language of Lab Windows/CVI as software platform and existing automatic welding equipment as clamping workpiece platform, an ultrasonic automatic testing system has been successfully developed by means of technologies of computer, ultrasonic NDT and NDE, integration of mechanics and electrics. This system has implemented numeralization, automatization and imaging of girth weld testing for hydraulic cylinder, and enhanced efficiency and reliability of ultrasonic testing.The principle and method of ultrasonic scan imaging for transverse wave angle probe are studied, the definitions of ultrasonic B-scan and C-scan imaging for angle probe have been given, the mathematical models of ultrasonic B-scan and C-scan imaging for artificial defect have been established, the key technique problems of software implementation of ultrasonic scan imaging have been achieved, and the functions of ultrasonic B-scan and C-scan imaging have been realized.According to the characteristics of ultrasonic echo-signals of weld flaws, the principle and algorithm of wavelet on threshold de-noising are further studied. On the basis of soft and hard threshold fuctions, a new threshold function is structured, and a lifting wavelet transform (LWT) de-noising algorithm based on the improved threshold function has been put forward. Experimental results show that this algorithm has overcome the disadvantages of soft and hard threshold de-noising methods, and obtained a better de-noising performance and a higher signal-noise ratio (SNR). Moreover, this algorithm has a good application prospect in real-time signal de-noising.Taking the common types of weld flaws as object, the method of energy feature extraction based on wavelet packet transform (WPT) are studied. The feature values of actual measured ultrasonic echo-signals are extracted by this method and evaluated by the sort separability criterion based on distance. The experimental result shows that this method is comparatively quite effective in the feature extraction of ultrasonic echo-signals for welding flaws.In order to overcome the disadvantages of artificial neural network, such as needing the massive trainings amples, the difficulty of determining network structure, and so on, through contrastively analyzing the common multi-class classification methods of support vector machine (SVM), a binary tree multi-class classification algorithm based onν-SVM (ν-SVM-BTMC) has been proposed. Experimental results show that the classification accuracy of this algorithm is high, and it is superior to other multi-class classification methods of SVM in the aspects of training and testing time.Considering the equivalence between RBF neural network (RBFNN) and SVM in structure, a new RBFNN recognition algorithm to welding flaws based onν-SVM-BTMC is proposed. From the experimental results, it is shown that the new recognition algorithm has a higher classification accuracy than RBFNN and the binary tree multi-class classification algorithm based onν-SVM, moreover, its training and testing speed are fastest. |
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