Abstract
The paper shows the efficiency of the multi-class recognition of technical states the welded joint tank by using the neural network classifier that based on Probabilistic Neural Network.Implementation of modern multichannel monitoring systems for determination the technical state of spatial objects requires researching of changes of a stress-strain state construction elements which function under the operation pressure and possible impact its structural integrity. Such researching are needed for prevention of cracks or damage determining and future prediction of object’s technical state.In the paper the tasks of a multi-class recognition of a state of multi-site damage the welded joint tank are determined. In the research was created the model of the object with probable location places cracks. One of them is a vertical crack and two are horizontal. Directions of their propagation are given. Cracks have the same value. The first researching included when cracks turn up one by one. The next one related with multi-focal defects when cracks were arised and they evolve in parallel and independently. The data of strain in the structural of the welded joint tank where sensors were attached is given.The research of the possibility of the error-free recognition was conducted by the developed classifier based on the stress-strain state of the geometric model of the tank structural elements with multi-site damage, where sensors are located. The development of the classifier was done by using Probabilistic Neural Network, which provides the best results of a multi-class recognition for determination technical state of spatial object with multidimensional vectors of diagnostic features. As a result, the probability of recognition from the network influence parameter, which shows the effectiveness of the neural network classifier for localization of single damage and localization of multiple cracks, was established
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