Research On Condition Evalution And Localization Technonlgy Using Acoustic Emission

Acoustic emission(AE)signal processing is the key research area in acoustic emission technology,which is also the important step in nondestructive testing and evaluation application.Using AE signal to detect structural damage degree,damage type and damage location is the core topics in AE detection technology.According to the physical nature of different sources,AE signal should be divided into two types:typical and quadratic.It is of great theoretical and practical value to study the generation mechanism,propagation characteristics,waveform characteristics and processing and analysis methods in different kinds of AE sources.In the thesis,we discuss the generation mechanism and processing methods involving these two kinds AE sources from the perspective of engineering application.One is the quadratic AE source in rub-impact fault of rotating machinery to be processed for recovery,localization and recognition.Another is the typical AE source in coal and rock mass fracture to be studied for feature parameters extraction and bursting condition prediction.The main contents of this paper include as follows:Based on theoretical analysis and experimental tests of AE propagation characteristics in metal free plate,a new preprocessing method to filter high order modal waves and compensate frequency dispersion is put forward.Simulation results demonstrate that it is an effective method to recover all frequency information of AE source by extracting dominant components A0 and S0 in the modal cluster.Besides,every modal wave is also be extracted by propagation forward calculation.Then,the processed AE signal served as the important evidence is further uesed for fault analysis.For the wideband,multimodal and dispersion characteristics of AE signals,two new near-field multiple AE source localization algorithms using frequency focusing theory are proposed for the first time.The first near-field coherent Signal-Subspace Method(N-CSM)is proposed,which is demonstrated that results are extremely depended on the focusing frequency and the initial position.According to this point,the second algorithm(N-AFCSM)improves frequency focusing method in auto way.Experimental results show that these two methods both have well ability to distinguish coherent signals.The latter method has higher precision and lower computational complexity for AE sources localization,which provides an effective evidence for the early fault detection.For AE signal energy distributed sparsely in the space,the sparse decomposition theory is creatively introduced to locate near-field AE sources.Considering multiply snapshots of AE signal in time domain,a convex optimization model using received AE sub-band array signal is constructed for AE energy sparse coefficient in the entire space.This method is demonstrated that the localization accuracy of the ulterior AE source is still unsatisfactory with low computational efficiency.Then,the improved algorithm using multiply AE snapshots in frequency domain is proposed based on frequency sub-band decomposition method and the coarse-fine grid optimal search strategy.The localization results present that the improved method has higher accuracy,lower computational complexity,preferable practicability and better decorrelation ability,which is an effective way to detect rub-impact AE sources for practical engineering application.Based on the deep learning frame,a new recognition algorithm is proposed for rotor rub-impact fault recognition by using frequency compensated AE signal spectrum features and deep Convolutional Neural Network(CNN).The proposed AE signal spectrum features describe AE waveform variation from several three perspectives including:time,frequency and energy.Compared with traditional AE waveform features,it provides complete AE information source in rub-impact fault and avoids information lossing by artificial selection.Combined with CNN model,the experimental results show that the proposed method achieves an approving performance in rotor rub-impact fault recognition.For the other kind AE source the thesis applies AE signal combined with fracture mechanics to predict coal rock mass bursting condition.Firstly,the designed algorithm improves the search strategy of GP correlation dimension for the optimal scaleless region localization in auto way.Results achieve better sensibility to the evolution of rock mass from stable to burst and preferable reliability in noise environment.On this basis,a new coal rock burst prediction method using multi-resolution AE waveform feature fusion is proposed The recognition results present that it has excellent performance to recognize the coal rock current conditions and is sensitive to coal rock crisis condition.Its good performance provides a new way to identify and analyze the stress conditions of coal rock mass.There is no precedent in similar recognition models for coal rock burst prediction using AE technology.