STUDY OF THE APPLICATION OF THE SPIKING NEURAL NETWORK AND FINITE ELEMENT METHOD FOR DIAGNOSTICS OF ROBOT ASSEMBLIES

One of the key parameters of any modern mechanical system is its vibration and acoustic characteristics,which have a direct impact on the environment and humans during operation. In this connection, thetask of diagnosing the vibration characteristics of various complex mechanical objects, to which industrialrobotic complexes can be referred, remains relevant. Due to the difficulty in carrying out diagnostics andexperimental debugging of newly developed mechanisms, it is interesting to apply modern approaches tosolving the problem of diagnostics, in particular, with the use of neural networks and numerical methods.The purpose of this work was to investigate the possibility of joint application of spike neural network andfinite element method for estimation of vibration characteristics on the example of wave gearbox bearing.The paper describes in detail the algorithm of diagnostics, which includes the stages of development of boththe finite element model of the investigated mechanical system and the development of the neural networkarchitecture. At the same time, the generation of training and control datasets for the neural network is carriedout on a simplified finite element model having characteristics similar to the detailed one, which is ensuredby the coincidence of the first ten eigenforms of the assembly. The data sets were generated on thebasis of numerical calculations using an explicit scheme of integration in time of a simplified model of agearbox with several types of artificially introduced defects similar to those appearing during operation of areal bearing. To analyze the frequency characteristics, a spike neural network architecture was developedand further improved on a training set of single defects. As a result of the study it was determined that thedeveloped spike neural network provides classification of data on the control dataset with 85% accuracy,which allows us to conclude about the applicability of the proposed method of determining the vibration stateof mechanical systems with the joint use of neural networks and finite element method.