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Damage Identification Using Wavelet Packet Transform and Neural Network Ensembles

Autor(en):


Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: International Journal of Structural Stability and Dynamics, , n. 12, v. 18
Seite(n): 1850148
DOI: 10.1142/s0219455418501481
Abstrakt:

This study presents a damage identification method that combines wavelet packet transforms (WPTs) with neural network ensembles (NNEs). The WPT is used to extract damage features, which are taken as the input vectors in the NNEs used for damage identification. An experiment was performed on a helicopter rotor blades structure to verify the proposed method. First, the vibration responses collected by different sensors are decomposed using the WPT. Second, the relative band energy of each decomposed frequency band is calculated and fused as the damage feature vectors. Third, two types of the NNEs are designed. One is based on the backward propagation neural networks (BPNNs) for detecting the damage locations and severities and the other one is based on the probabilistic neural network (PNN) to detect the damage types. Finally, the trained NNEs are employed in damage identification. From the identification outcomes, it is concluded that damage information can be extracted effectively by the WPT and the identification accuracy of the NNEs is better than that of individual neural networks (INNs).

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.1142/s0219455418501481.
  • Über diese
    Datenseite
  • Reference-ID
    10352117
  • Veröffentlicht am:
    10.08.2019
  • Geändert am:
    10.08.2019
 
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