Damage process testing of wood under loads based on acoustic emission and neural network

作者：Sun Jianping^*; Wang Fenghu; Hu Yingcheng

来源：Chinese Journal of Scientific Instrument, 2011, 32(2): 342-347.

• Acoustic emission signal
• Cumulative energy
• Damage process
• Generalization capacity
• Local maximum
• Local minimums
• Maximum error
• Neural network model
• Non destructive testing
• Time serial
• Verification samples

Acoustic emission(AE)signals of wood under loads were tested and analyzed to realize nondestructive testing and monitoring. In this study, time serial and neural network (NN) are employed to simulate and predict the cumulative energy of AE parameter and corresponding loads. A neural network model is used to predict the time serial of AE cumulative energy. The maximum error of 44 verification samples is 5.6%, which indicates the good generalization capacity of the trained model. And it appears that the AE amplitude of the samples with relatively large error corresponds to its local maximum, however the ring count and AE rate correspond to the local minimum. In the prediction of the time serial of loads, the maximum error between the neural network model output and object output for 53 verification samples is less than 0.1%, which indicates the perfect generalization capacity of the trained model. The NN models with the structure of 5 × 5 × 1 and 6 × 5 × 1 can predict the time serial of AE cumulative energy and loads accurately.

• 出版日期2011
• 单位东北林业大学