Advanced Materials Research Vols. 889-890

Abstract: The extraction and correct recognition of mud pulse signal in Wireless Measurement While Drilling was a key technology in petroleum drilling process. It determined whether the well course in the petroleum drilling process was right or not. This article has carried out the numerical modeling of mud pulse signal and illustrated its signal feature. In terms of the problems of extraction and recognition of PLM encoded mud pulse signal, it has researched on the noise removing using the Wavelet multi-scale feature recognition and related de-noising algorithms. The location of PLM encoded mud pulse signal was discerned precisely by using the combinational algorithm of local feature and waveform character recognition and laid a foundation for the extraction and accurate recognition of mud pulse signal. Finally, the results of live tests indicate that this arithmetic is simple, useful and conform to the requirements of engineering application.

Abstract: In order to reduce noise disturbance of alternating current field measurement (ACFM) signals in crack defects detection, a new noise cancellation method called empirical mode decomposition (EMD) was proposed. A butt welded plate specimen was inspected and measured by a hand-held ACFM probe and a TSC AMIGO instrument. The original ACFM signal was first decomposed into different intrinsic mode functions (IMFs) and a residue, and some IMFs containing useful information were subsequently reconstructed based on correlation coefficients. The experimental results show that the reconstructed ACFM signals B_x and B_z, and butterfly plot can characterize crack defects more accurately, demonstrating the feasibility and effectiveness of the proposed method.

Abstract: In this paper the application of wavelet in data detection of dynamic testing is chiefly researched , i.e. , by applying the method of wavelet denoising to eliminate the non-stationary random noise which produced in data detection of dynamic testing , by analyzing dynamic testing data to define the optimal wavelet as well as the optimal decomposition scale. Based on actual requirements of dynamic testing system, to reconstruct the data accurately by using FIR filter with biorthogonal wavelet, the method has a favorable effect.

Abstract: This work present the application of Empirical Mode Decomposition (EMD) to analyze the air borne acoustic signatures commenced during welding process. In order to achieve goals, bead-on plate welding was done onto the carbon steel specimen using Metal Inert Gas Welding. At the same moment, the microphone with operating frequency of 31.5 Hz to 8 kHz used to collect air borne acoustic signatures. In analysis part, Empirical Mode Decomposition has been applied to the acoustic signals and the selected Intrinsic Mode Function (IMF) was presented in frequency-distance plot using spectrogram. Based on the analysis results, there were 3 significant IMF has been found. Those were IMF mode 3, mode 5 and mode 8 which lie within the frequency of 1500 Hz to 4500 Hz, 200 Hz to 800 Hz, and 40 Hz to 80 Hz respectively. The frequency-distance plot from spectrogram of IMF mode 3 showed a significant pattern which can be related with the discontinuity of welding. The discontinuity appears wherever low amplitude power detected in the frequency-distance plot of IMF mode 3. Moreover, the frequency-distance plot of IMF mode 5 and mode 8 can be significantly related with the spatter and weld pool oscillation condition. High power amplitude in frequency-distance plot of IMF mode 8 can indicates the offset of weld pool oscillation frequency and cause the existence of higher amount of spatter which resulting the high power amplitude in frequency-distance plot of IMF mode 5. In summary, it can be conclude that the application of EMD in the analysis of air borne acoustic signatures allow the detection of several phenomena in welding process which might lead to defect once in a time. This was found to be significant in the process of developing the online welding quality monitoring.

Abstract: Magnetic anomaly detection is a passive method for detection of a ferromagnetic target, and its performance is often limited by external noise with a power spectral density of 1/f ^a, (0<a<2). In consideration of this kind of noise is non-stationary, self-similarity and long-range correlation, an effective noise reduction method based on the wavelet transform is proposed in this paper. The proposed method is only take one parameter into account, while the hard thresholding and soft thresholding methods utilize the relationship of the variance of the noisy signal. The simulation results show that the performance of our proposed method is superior to that of other methods.

Abstract: In order to more effectively remove noise in partial discharge signals, it is proposed a new threshold selection method in this paper. This method firstly takes the signals before the partial discharge starting to happen as only contain noise signal, and then applies a wavelet transform to the only contain noise signal. Secondly record every detail part and the maximum value of wavelet coefficients of last layer approximation part, and take this value as its layer threshold. And then applies a wavelet transform to the partial discharge signals which contains noises. Next is to process wavelet coefficient of each layer using the selected threshold. Finally, the already handled wavelet coefficients is used to reconstruction the signals. The whole process of threshold choosing is automatic without human intervention. Simulation experiment show that compared with the traditional threshold selection method, this method can be better to remove the noise of the partial discharge signals, and it has a strong practical value.

Abstract: The method of wheel-weight for mine's automatic light rail weight-bridge is put forward. According to some important factors which cause measure error using wheel-weight method, for example, zero drift, speed and gradient, the error compensation algorithms are studied. The software flow charts of these algorithms are given, which have been put into effect in the ARM microprocessor based embedded system. It has been found that these error compensation methods are efficient to improve the accuracy of the measurement system.

Abstract: Though the Support Vector Regression Machines (SVRM) is considered to be an effective method for time series prediction, its performance is greatly influenced by its parameters. In order to improve the rationality of parameter setting, the influences of the parameters (the number of support vectors N_S and the prediction length N_E) and signal characteristics on the SVRM performance were discussed. The results proved that the existence of confliction between prediction accuracy and prediction efficiency, and SVRM may inappropriate to long-term prediction, and N_S should be greater than a threshold which depends on the signal characteristics for an accurate prediction result. The research results may provide a theoretical basis for the improvments of SVRM algorithm.

Abstract: This paper studies the application of wavelet analysis in the fault diagnosis of mechanical system which describes the principle of wavelet analysis and its application in fault diagnosis of gear mechanism. It can identify and eliminate the failure by analyzing the vibration signal obtained from the fault simulation experiment of gear transmission system. Wavelet analysis in MATLAB can extract some important fault characteristics that the other methods cannot extract them. Application of wavelet analysis in the fault diagnosis of gear transmission system is effective by pretreating the characteristic information that extracted from gear transmission system.

Abstract: Vibration signal analysis has been widely used in the fault detection and condition monitoring of rotation machinery. But the practical signals are easily polluted by noises in their transmission process. The raw signals should be processed to reduce noise and improve the quality before further analyzing. In this paper an improved wavelet threshold denosing method for vibration signal processing is proposed. Firstly, a new threshold is developed based on the VisuShrink threshold. The effect of noise standard deviation and wavelet coefficient is retained, and the correlation of wavelet decomposition scale is considered. Then, a new threshold function is defined. The new algorithm is able to overcome the discontinuity in hard threshold denoising method and reduce the distortion caused by permanent bias of wavelet coefficient in soft threshold denoising method. At last five kinds of threshold principles and three kinds of threshold functions are compared in processing the same signal, which is simulated as the mechanical vibration signal added white noises. The results show that the improved threshold is superior to the traditional threshold principles and the new threshold function is more effective than soft and hard threshold function in improving SNR and decreasing RMSE.