A SCALABLE NEURAL-NETWORK MODULAR-ARRAY ARCHITECTURE FOR REAL-TIME MULTI-PARAMETER DAMAGE DETECTION IN PLATE STRUCTURES USING SINGLE SENSOR OUTPUT
Abstract
A scalable modular neural network array architecture has been proposed for real time damage detection in plate like structures for structural health monitoring applications. Damages in a plate like structure are simulated using finite element method of numeric system simulation. Various damage states are numerically simulated by varying Young's modulus of the material at various locations of the structure. Transient vibratory loads are applied at one end of the beam and picked at the other end by means of point sensors. The vibration signals thus obtained are then filtered and subjected to wavelet transform (WT) based multi resolution analysis (MRA) to extract features and identify them. The redundant features are removed and only the principal features are retained using principal component analysis (PCA). A large database of principal features (the feature base) corresponding to different damage scenarios is created. This feature base is used to train individual multi layer perceptron (MLP) networks to identify different parameters of the damage such as location and extent (Young's modulus). Individually trained MLP units are then organized and connected in parallel so that different damage parameters can be identified almost simultaneously, on being fed with new signal feature vectors. For a given case, damage classification success rate has been found to be encouraging. The main feature of this implementation is that it is scalable. That is, any number of trained MLP units capable of identifying a certain parameter of damages can be integrated into the architecture and theoretically it will take almost the same time to identify various damage parameters irrespective of their numbers.
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