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, the task of diagnosing the vibration characteristics of various complex mechanical objects, to which industrial robotic complexes can be referred, remains relevant. Due to the difficulty in carrying out diagnostics and experimental debugging of newly developed mechanisms, it is interesting to apply modern approaches to solving 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 and finite 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 both the finite element model of the investigated mechanical system and the development of the neural network architecture. At the same time, the generation of training and control datasets for the neural network is carried out on a simplified finite element model having characteristics similar to the detailed one, which is ensured by the coincidence of the first ten eigenforms of the assembly. The data sets were generated on the basis of numerical calculations using an explicit scheme of integration in time of a simplified model of a gearbox with several types of artificially introduced defects similar to those appearing during operation of a real bearing. To analyze the frequency characteristics, a spike neural network architecture was developed and further improved on a training set of single defects. As a result of the study it was determined that the developed 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 state of mechanical systems with the joint use of neural networks and finite element method.
