IoT-платформа для вібраційної діагностики промислового обладнання

• Published in: Radìoelektronnì ì komp'ûternì sistemi (Online) no. 3; pp. 125 - 139
• Main Authors: Туркін, Ігор Борисович, Лезновскій, В'ячеслав Андрійович
• Format: Journal Article
• Language: English
• Published: 05.10.2021
• ISSN: 1814-4225; 2663-2012; 2663-2012
• DOI: 10.32620/reks.2021.3.10

The subject of study in the article is a digital platform for vibration diagnostics of industrial equipment. The aim is to increase the informativeness of vibration diagnostics processes of industrial equipment by developing and implementing IoT-oriented solutions based on the concept of intelligent sensors and actuators according to the IEEE standard 1451.0-2007. Tasks: to substantiate the feasibility of using platform-oriented technologies for vibration diagnostics of industrial equipment and choose a cloud service for the implementation of the platform, to develop software and hardware solutions for IoT-platform for vibration diagnostics of industrial equipment; calibrate the vibration diagnostic system and check the accuracy of the measurement. The methods used are microservice approach, multilevel architecture, methods for assessing the condition of equipment by vibration indicators. The following results were obtained. The Microsoft Azure IoT platform, which provides the infrastructure for creating and managing cloud applications, was chosen as the cloud computing platform for the industrial equipment vibration diagnostic system. Azure Internet of Things Suite is a Microsoft Azure IoT service designed to integrate and organize data flows, analyze, and present data in a format that helps people make informed decisions. The architecture of the IoT-system of vibration diagnostics of industrial equipment developed and presented in the article is three-level. The level of autonomous sensors provides reading of vibration acceleration indicators and through the digital wireless data transmission channel BLE transmits data to the Hub level, which is implemented based on a single-board microcomputer BeagleBone. The computing power of BeagleBone provides work with artificial intelligence algorithms. At the third level of the server platform, the tasks of diagnosing and predicting the state of the equipment are solved, for which the Dictionary Learning algorithm implemented in the Python programming language is used. Conclusions. Tests of the IoT system for vibration diagnostics of industrial equipment were performed using a special stand, which allows the calibration of sensors and verification of the accuracy of the measuring system. The correctness of the entire system is confirmed by the coincidence of expected and measured results. The direction of development of the IoT-system for vibration diagnostics of industrial equipment is the development of additional microservices, which will add the possibility of using modern artificial intelligence technologies for complex diagnostics and forecasting of equipment status.