Automation of the process of dynamic signals of tensometric systems using convolutional neural network - Illia Kolysnychenko; Victor Tkachov (Авіаційно-космічна техніка та технологія, 2022)
Purpose. Based on empirical data obtained from the system of weighing on single-platform railway scales in motion and numerical methods, create software to generate an array of input data to the wrapped neural network. Using the Python programming language, build a model of a wrapped neural network that will allow you to recognize the type of object (auto-coupling, cart) that has passed through the strain gauge platform. Generate test data of different quality, with noise overlay, and test the recognition quality of the obtained model on different test input data. Research methods. The Python programming language and keras library were used to build plot diagrams of objects through the weighing platform, create a model of a convolutional neural network and generate test plots with noise overlay. Results. Empirical data from the railway strain gauge system were approximated for all types of moving objects used by the enterprise using the algorithm of approximation to empirical data by the Heaviside function. Software for data generation based on the obtained approximation equations is developed. Using a noise generator, we managed to create a dataset for learning and testing a convolutional neural network with different levels of input signal quality. A convolutional neural network has been built and trained, the test of which shows a high level of recognition of test objects, even when simulating an incorrectly configured weighing platform. Scientific novelty. The novelty is a new method of processing dynamic signals for tensometric systems based on machine learning, which with minimal error can recognize the types of objects that have passed through the weighing system. This used algorithm for processing dynamic signals is universal and can be so in many dynamic systems. Practical meaning. The method presented in the article is a key to building an intelligent identification system for evaluating wagons in dynamics. This system allows you to increase the speed of weighing cars and reduce errors in the system as a whole.