Abstract
The large-scale vibrating screen has been applied widely in coal industry and other industrial areas as a kind of important device. However, the lower crossbeam is the main carrying structure and is easily damaged, so it is regarded as a researched object in this thesis and it is tested under the load in the laboratory. Based on the test, acoustic emission wave signals can be gotten by modern acoustic emission testing technique. Then, the "wavelet packet - energy" from the characteristics of acquiring signals is used as neural network input vector. In Matlab6.5, neural network model identification is created and taking advantage of nonlinearity and the ability of learning and memory of Neural Network，this model is fixed by training structure of network with training samples. The work presented shows that acoustical emission signal processing and research on early fatigue fault diagnosis based on the wavelet and the neural network is viable.
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