METHODS OF LARGE-SCALE SIGNALS TRANSFORMATION FOR DIAGNOSIS IN NEURAL NETWORK MODELS
DOI:
https://doi.org/10.15588/1607-3274-2018-4-11Keywords:
signal processing, neural network, instance, feature, data dimensionality reduction.Abstract
Context. The problem of dimensionality reduction of diagnosis signals for their use in neural network models is considered. The object of the study was the process of transformation of diagnosis input signals for their subsequent use in the synthesis of predictive
models.
Objective. The goal of the work is the creation of the methods for the conversion of diagnosis signals as a result of the application of which new signals will be obtained, which in turn will be used in the construction of neural network predictive models and will significantly reduce the synthesis time of the model by reducing their dimension and the allocation of the necessary components that characterize the state of the individual elements of the object of diagnosis.
Method. The methods of reducing the dimension of the input signals of diagnosis and isolation of their components, which characterize the state of the individual elements of the object of diagnosis on the basis of expert knowledge about the process of diagnosis
are proposed. The developed methods are based on the methods of digital signal processing. Based on the expert knowledge of the object and the process of diagnosis, the necessary signal conversion procedures and their parameters are selected. In accordance with
the requirements for the desired accuracy and detail of the forecast, the optimal degree of averaging of the signal is selected, which directly affects the speed of constructing the predictive model. The proposed methods can be used in the transformation of diagnosis
signals of various diagnostic processes where there is a need to build neural network predictive models based on high-dimensional signals. The developed methods were investigated for the conversion of diagnostic signals obtained on a complex object of technical
diagnostics, namely, on the transmission of the helicopter. On the basis of the received signals, a neural network model was synthesized, the training of which requires much less computational resources, while the prediction accuracy remains optimal.
Results. The developed methods are implemented programmatically and investigated in solving the problem of predicting the future state of the helicopter transmission during the diagnosis process.
Conclusions. The experiments have confirmed the effectiveness of the developed methods and allow us to recommend them for use in practice in solving diagnostic problems. The conducted experiments have confirmed the proposed software operability and allow recommending it for use in practice for solving the problems of diagnosis and automatic classification on the features. The prospects for further research may include the search for the best parameters of the developed methods, optimization of their software implementations, as well as experimental study of the proposed methods on a large set of practical problems of diagnosing complex objects of different nature by their diagnostic signals.
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