A Real-Time Data Monitoring Framework for Predictive Maintenance Based on the Internet of Things

Abstract

Te Internet of Tings (IoT) is a platform that manages daily life tasks to establish an interaction between things and humans. One of its applications, the smart ofce that uses the Internet to monitor electrical appliances and sensor data using an automation system, is presented in this study. Some of the limitations of the existing ofce automation system are an unfriendly user interface, lack of IoT technology, high cost, or restricted range of wireless transmission. Terefore, this paper presents the design and fabrication of an IoT based ofce automation system with a user-friendly smartphone interface. Also, real-time data monitoring is conducted for the predictive maintenance of sensor nodes. Tis model uses an Arduino Mega 2560 Rev3 microcontroller connected to diferent appliances and sensors. Te data collected from diferent sensors and appliances are sent to the cloud and accessible to the user on their smartphone despite their location. A sensor fault prediction model based on a machine learning algorithm is proposed in this paper, where the k-nearest neighbors model achieved better performance with 99.63% accuracy, 99.59% F1-score, and 99.67% recall. Te performance of both models, i.e., k-nearest neighbors and naive Bayes, was evaluated using diferent performance metrics such as precision, recall, F1-score, and accuracy. It is a reliable, continuous, and stable automation system that provides safety and convenience to smart office employees and improves their work efciency while saving resources.