Abstract
When using acoustic emission(AE) to locate the rub-impact source of rotating machinery, it is difficult to achieve exact source location for the effects of strong noise and waveform distortion. A neural network algorithm was presented to locate the AE source. In general BP wavelet neural network(WNN), it is a local search algorithm which falls into local minimum easily, so the probability of successful search is low. As an improved way, the particle swarm optimizer (PSO) algorithm was proposed to train the parameters of the WNN, then WNN based on PSO was used to locate the AE source. The localization experiment data of rub-impact AE signals was sampled from rotating test stand. The results show that the PSO algorithm is effective and the localization is accurate with proper structure of the network and the input parameters.
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Deng, A., Zhao, L., Wei, X. (2009). The Application of Wavelet Neural Network Optimized by Particle Swarm in Localization of Acoustic Emission Source. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_82
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DOI: https://doi.org/10.1007/978-3-642-10684-2_82
Publisher Name: Springer, Berlin, Heidelberg
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