a A0 + ?A. expE ~

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Acoustic emission detection using artificial neural network analysis and study of tick noise


And some of the problems with the process, we focused on the traditional BP algorithm appropriate improvements, an increase of momentum, and in the frequency of 40MHz, 386 host E, using the C language has developed a back-propagation learning rule of the source . Based adaptive filter, we use network forms of adaptive training. 3 simulated acoustic emission signals and noise sources for neural networks in order to facilitate the study of acoustic emission in the tick noise problem, first we carried out computer simulations of acoustic emission waves, a careful analysis of the characteristics of acoustic emission after the crossing, according to these characteristics, Without loss of generality to build a mathematical model of acoustic emission signals can be used as a transient signal, (£) a A0 + ΣA. expE ~ (£ a.)] · l1sinE ~ (£ a .)+]·- the i-th signal arrival time - the frequency of the i-th signal - signals the beginning of the i-th phase can be considered by a transient signal composed of (£), set (A, m, ti,, no) were as {3.0,ed hardy,0.6,5.0,2,ralph lauren outlet, / 2},franklin and marshall uk, then () of A to take 4,abercrombie düsseldorf.0, prepared using c language source code simulation of the sound source, the sampling points from 5.O ~ 10. 0, interval 0.1, 5O sampling points, the noise source we use O ~ 1 of the smell of the normal distribution of zero mean noise, prepared by the c language source code. Chad +] Tpe may j1 plant X,. + · △ (4) 4 where llylI - the length of the vector y 4O0 · simulation experiment and the results of acoustic emission source waveform samples need to book a further ∈ u.,: Li Jialin, etc.; acoustic emission detection using Noise cut off the nose of artificial neural network analysis and study pre-treatment, the so-called pre-processing is to the point samples are normalized, so that various values ​​are varied between 0 and 1, BP network processing to facilitate the use a separate experiment, the first the S (f) signal to the network input,hollister, output, expected output is still S (f). training the network is successful, then (f) + (f) the input signal to test the network,hollister hamburg, first set up the training sample set, the {A ,,,,,) for different values ​​of the sampling results as the training sample, the random noise value plus a collection of samples as a function of the test sample, after training the network to filter the test samples at this time to take three BP network layer network architecture, learning rate 0.9, momentum term of 0.7, the number of 3000 training results showed that this network has a good filtering effect, we will be S (),(}), S ()+(), S (f) to make the waveform, as shown in Figure 2 to 5.5 Looking 0r0r0r0. To make the artificial neural network software in the actual acoustic emission filter to play a role,ed hardy shop, it must be pre-filter unit, which is the network of Figure 2BP the input signal S (f) O2o4060I/IlB noise signal in Figure 3 (f) disclosure form, 'as a research object to the amount of filtering software. Specifically, the neural network input filter unit Jing and A / D converted value of the normal sampling interface,mulberry sverige, this is the actual rejection of this network should be a major problem, this needs to conduct in-depth research in this area. References 1 Yuan Zhenming, Ma Yu W, He Zeyun ed. Acoustic emission technique and its application Beijing; Machinery Industry Press. 1985.2WidrowB. GroverJR. McCoolJM. Aldaptivenoisecan-ceiling; prineiplesandapplieation. ProcIEEEVo1.63.1975 (12); 86-873 Hu Shouren ed. A virtual neural network technology used. Long copy,ralph lauren danmark; National Defense University Press. 199Z. Tibetan Song Yi, etc. 4. Emblem of weak signal detection methods and equipment. Beijing, National Defense Industry Press. 1994. Received Date :1999 -03-23 ​​volumes were required to mail-order plus 15 postage, we wish to purchase, please contact call letters, address: 99 Handan Road, Shanghai Institute of information materials issued at Shanghai,franklin and marshall sale, Zip: 200437, Contact: single country United Kingdom, Telephone: (021) 65420775x311. Shanghai Institute of information materials issued at 401.1 OOOm

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