ACCELEROMETER CALIBRATION METHOD FOR INDUSTRIAL EQUIPMENT VIBRATION DIAGNOSTICS
DOI:
https://doi.org/10.32782/IT/2022-3-6Keywords:
Internet of things; digital platform; vibration diagnostics; calibration; accelerometer; industrial equipmentAbstract
The subject of study in the article is the method of calibrating accelerometers as part of a digital platform for vibration diagnostics of industrial equipment. The goal is to increase the informativeness of the processes of vibration diagnostics of industrial equipment by developing and implementing IoT-oriented solutions based on smart sensors and actuators per the IEEE 1451.0-2007 standard. Tasks: justify the feasibility of using platform-oriented technologies for vibration diagnostics of industrial equipment and choose a cloud service for the implementation of the platform; develop software and hardware solutions of the IoT platform for vibration diagnostics of industrial equipment; calibrate the vibration diagnostics system and check the measurement accuracy. The methods used are the microservice approach, multilevel architecture, and assessing equipment state based on vibration data. We obtained the following results. The architecture of the IoT system for vibration diagnostics of industrial equipment developed and presented in the article is three-level. The level of autonomous sensors provides readings of vibration acceleration indicators and transmits data to the Hub level, which is implemented based on a BeagleBone single- board microcomputer through the BLE digital wireless data transmission channel. BeagleBone computing power provides work with artificial intelligence algorithms. At the third level of the server platform, the tasks of diagnosing and predicting the condition of the equipment are solved, for which the Dictionary Learning algorithm implemented in the Python programming language is applied. Verifying the accelerometer calibration method for vibration diagnostics of industrial equipment was performed using a unique stand. Conclusions. Correct operation of the entire system is confirmed by the coincidence of expected and measured results. In the next step, we plan the development of additional microservices that will provide the possibility of using time series analysis methods and modern artificial intelligence technologies for complex diagnostics and forecasting of the equipment state.
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