DATA ANALYSIS OF ENVIRONMENTAL CONDITIONS INFLUENCING THE WORK OF LABORATORY EQUIPMENT AND APPLICATION OF MACHINE LEARNING MODELS FOR CLASSIFICATION OF POOR CONDITIONS

Abstract

Environmental conditions can have a crucial impact on the functioning of laboratory equipment. Electric components are sensitive to the influence of certain environmental factors such as temperature, humidity, vibrations, etc. Environmental factors should, therefore, be monitored to avoid their negative influence on the system and potential faults and failures they could cause. Unlike the traditional approaches which required the presence of special staff to monitor environmental factors and react if they are poor, the rise of the Internet of Things enhanced the application of intelligent solutions where human factor is not necessary. In this paper, research on data analysis, preprocessing and intelligent classification of environmental conditions has been conducted. The data was collected by sensors connected to Raspberry Pi. The applied monitoring system setup enabled long-distance monitoring of laboratory conditions through the internet and full applicability of fundamental IoT concepts. Since data preparation is an important step in the process of designing machine learning models, the collected data was analyzed and preprocessed in Python. Intelligent classification of environmental conditions was performed using machine learning models k-Nearest Neighbors and Random Forest. Grid search was used for model selection, and the performances of k-Nearest Neighbors and Random Forest machine learning models were compared. Experimental results show that these machine learning models can be successfully used for intelligent classification of environmental conditions.