This paper describes a study on damage identification using wavelet packet analysis and
neural networks. The identification procedure could be divided into three steps. First, structure
responses are decomposed into wavelet packet components. Then, the component energies are used
to define damage feature and to train neural network models. Finally, in combination with the feature
of the damaged structure response, the trained models are employed to determine the occurrence, the
location and the qualification of the damage. The emphasis of this study is put on multi-damage case.
Relevant issues are studied in detail especially the selection of training samples for multi-damage
identification oriented neural network training. A frame model is utilized in the simulation cases to
study the sampling techniques and the multi-damage identification. Uniform design is determined to
be the most suitable sampling technique through simulation results. Identifications of multi-damage
cases of the frame including different levels of damage at various locations are investigated. The
results show that damages are successfully identified in all cases.
Woodworking Tool Wear Condition Monitoring during Milling Based on Power Signals and a Particle Swarm Optimization-Back Propagation Neural Network
