Research On The Mechanized Non-destructive Testing Methods And Apparatus For Water-wall Tube

In the strained circumstance of the current electricity supply, as a big door of power supply, the thermal power plant annually provides about 63% electricity that the whole nation used every year. However, because of the poor manufacturing quantity and the unscientific management, the safety and cost-effective of the thermal power plant is influenced seriously by the tube explosion and leakage. In order to decrease and prevent the incidents happening,and to improve the boiler tube inspection quality,it is very important for us to search for an quantitative inspection method and develop an effective apparatus for testing boiler tube. In the light of the status that there exists faults in water-wall tube and there are no effective inspection and evaluation means, in this dissertation, a novel fusion testing method using the main magnetic flux (MMF) technique and ultrasonic method is studied, and an automatic testing device is developed. The main research contents, results and new ideas are as follows: First, according to the spot inspection environment of the erecting water-wall, a water-wall inspection apparatus, which includes the adhesion and crawling mechanism and safety,the magnetic transducer,a fixer for ultrasonic probe, remote data acquisition, the control system and the signal processing software, is designed. Comparing with other inspection methods for water-wall tube, this system not only have the fast examination speed and high sensitivity, but also can realize automatic examination and quantitative evaluation. And it can increase the examination efficiency and the reliability, guarantee the boiler for the thermal power plant running safely and cost-effectively, and have considerable economic benefit and the extensive market applied foreground. Second, because of the different characteristics of the local defect, system noise, the inside screw thread and the wall loss corrosion signal, a signal pre-processing method based on wavelet analysis is discussed. The signal processing method based on wavelet transform module maximum value, the wavelet decomposition and reconstruction denoise and an adaptive wavelet noise canceller is used to cancel the various noises. And wall loss signal is picked up from the original. Third, in order to meet the need of fusion inspection and avoid the wrong evaluation, a new method combined the MMF technique with ultrasonic method is proposed, and the magnetic technique is used to do full inspection and locate the flaws, and the ultrasonic is employed to implement further quantitative inspection accurately. After comparing the precision of the polynomial, exponential and logarithmic function, the first function is selected to fit the relations between the thickness and the peak value of MMF signal. Thus, the data measured by ultrasonic probe is used to calibrate the model parameters, and the defect profile can be evaluated quickly. The experimental result demonstrates that the model used in this system has better accuracy than the statistics relation model obviously, and it is suitable for defect evaluation real-time. Moreover, it is unnecessary to have much more experimental data for the curve fitting technology, so it has the better practicability than the other methods. Finally, after analyzing of the characteristics of inversion method existed today, the wavelet multi-resolution analysis method is employed to solve the nonlinear inversion problem for MFL, and the theoretical framework about multi-resolution solving for MFL inversion problem is discussed. In addition, the iterative model of multi-resolution inversion, wavelet neural network (WNN) forward and inversion model are set up. And the choice of wavelet basis function, parameters initialization, the number of hidden layer nodes and parameters regulating are discussed, the learning algorithm for wavelet network is also studied. The inversion result shows that the WNN has certain reliability to inverse the MFL signal, and it is very important to choose the wavelet basis function and resolutions for WNN. Thus, some methods to improve the inversion precision are discussed in this dissertation. In addition, the difference between the outside defect and the inside defect is studied, and the flaw recognition and classification method is discussed based on wavelet analysis.