Several Key Problem Researches On Monitoring Cracks Of Hydraulic Turbine Blades Based On Acoustic Emission Technique

Francis turbine is popularly used in power stations. The runner of the turbine bears the important task of transferring the water energy to mechanical energy, which makes cracks appeared easily. Initiation of blade cracks endangers the safety operation of power stations. And frequently shutting down to weld cracks brings a lot of economic loss for power stations. Although many studies have been done at the stage of design and manufacture, the problem of cracks can't be resolved completely. It will be an important measure of predicting the working life of runners if we can monitor the crack initiation and master the developing trend of cracks, especially the throughout cracks, which is an enormous problem relevant to highly efficient, stationary and safety operation.Acoustic emission (AE) is a phenomenon of generating transient elastic wave caused by the fast energy release of local source during the initiation and growth of cracks. There are many researches and applications using AE technique to collect the signals of cracks. This paper is a preliminary study of applying AE technique to detecting the large-scale turbine runner. The major investigative contents include: attenuation characteristics of AE signals across the runner, extraction of weak signals buried in big background noise, feature extraction of AE parameters and source location of cracks in blades and the fatigue AE characteristics of the blade material. The following achievements had been obtained:1. The attenuation characteristics of AE signals propagated across the runner were researched. The attenuation characteristics due to the propagation distance and the existence of a structure interface were studied according to the major affecting factors on AE signals across the runner. At the same time, the attenuation results were analyzed by the wavelet packet technique. Two commonly used AE parameters, energy and maximum amplitude, were used to describe the attenuation performance. From the tests, it is concluded that AE signals are detectable after propagating at a distance of 6 m. The propagation distance is the major factor of attenuation and the interface composed by the same type of material has effects on attenuation, which depends on the relative size of structures. It is a better way to mount sensors on a simple structure that has a possibly equivalent size with the structure incurred AE sources when the sources propagate across the interface. Furthermore, through the wavelet packet analysis, it is concluded that the maximum deviations of the attenuation slopes of the energy and amplitude between the feature packet and raw signal are small, which shows that the attenuation characteristics of the packet and the corresponding raw signal are substantial agreement. The feature packet can reflect the attenuation characteristics of signals propagated across the runner. In light of this, the pressure on data transmission and storage can be decreased by extracting feature packet coefficients.2. Extraction of AE signals mixed with the strong background noise was studied. The operating noise of turbine units is strong. In order to extract the wanted signal from the received noisy signal, there are two ways to denoise according to the different conditions: 1) When the total number of noise components and independent components is not more than that of the received mixed signals (i.e. the noise component is also regarded as an independent source), the blind source separation of the independent component analysis (ICA) was used to extract the wanted components. Then the free-noise signal was obtained by the (pseudo) inverse transformation of the wanted components. Through extraction for the pressing lead signal mixed with white Gaussian noise of different strengths and the operating noise of the turbine unit, it indicates that this method is not affected by the inputted signal-noise-ratio (SNR) and the frequency range of signals. The wanted signals can be extracted preferably.2) When the number of sources is more than the mixed signals, the denoising method of sparse coding shrinkage (SCS) based on ICA was used. The independent components were estimated by the maximum a posteriori (MAP) estimation and the probability density functions (PDFs) of the independent components were fitted by generalized Gaussian model. Then the nonlinear shrinkage functions were used to denoise. The results were compared with those obtained by the wavelet threshold value denoising method. It shows that the SCS method can extract the crack signal of the blade and the pressing lead signal buried in the operating noise of the turbine unit. Although the results of the SCS method are not better than those of the first method, it is more applicable. Furthermore, its denoising results are better than the wavelet method.3. The feature extraction of the AE parameters and source location of the runner cracks were studied. Hydraulic turbine runner has a complex structure and the crack regions are centered, so the intelligent location methods were used. In order to improve the accuracy of location, it is necessary to extract the feature parameters of the AE signal. The ICA, kernel principle component analysis (KPCA) and kernel ICA (KICA) were used to extract features. The result shows that the information of the first nine feature parameters with above 90% contribution rate is the largest and that the redundancy between the feature parameters is the least. The wavelet neural network (WNN) and support vector machine (SVM) were used to recognize the crack regions according to the extracted feature parameters, respectively. The simulation shows that recognition rate of SVM is better than that of WNN. As a result, in real world applications, doing feature extraction by KICA can decrease the dimension of input signals, which reduces the pressure on AE parameters transmission and storage and improves the accuracy of location as well. In light of these, it is a good method for source location in complex big-size structures to combine KICA with SVM.4. The fatigue AE characteristics of the runner blade material were studied. Some researches indicated that most of the regular cracks were fatigue cracks when blades were subjected to vibratory alternate stress. The fatigue crack growth rate of blade is the basic data to evaluate the degree of damage of the turbine runner. The crack signals of the blade material from three-point bending fatigue tests were received, and the fatigue crack growth rate and the corresponding AE characteristics were studied in the laboratory. Furthermore, the characteristics of the cracks were compared with those of background noise received from the locale of a hydraulic turbine unit. The results show that the AE parameters and fatigue cracks have a coincidence relation and that the parameters can represent the state of crack. The correlations of the crack growth rates, AE count rates and AE energy changed rates versus the stress intensity factor (SIF) range were obtained. The fatigue life can be predicted by the correlations. At the same time, the correlations of the crack growth rates versus AE count rates and AE energy changed rates were obtained. As a result, the crack growth rates of can be deduced by the AE parameters changed rates, which avoids the problem of measuring the SIF in real applications. In addition, the ranges of the AE parameters of fatigue crack signals and the background noise onsite are different.