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

Author:

Uppal Mudita1,Gupta Deepali1ORCID,Goyal Nitin2,Imoize Agbotiname Lucky34ORCID,Kumar Arun5,Ojo Stephen6ORCID,Pani Subhendu Kumar7,Kim Yongsung8ORCID,Choi Jaeun9ORCID

Affiliation:

1. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

2. Department of Computer Science and Engineering, School of Engineering and Technology, Central University of Haryana, Mahendragarh, Haryana 123031, India

3. Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, Nigeria

4. Department of Electrical Engineering and Information Technology, Institute of Digital Communication, Ruhr University, Bochum 44801, Germany

5. Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh 201310, India

6. Department of Electrical and Computer Engineering, College of Engineering, Anderson University, Anderson, SC 29621, USA

7. Krupajal Engineering College, BPUT, Rourkela, Odisha 751002, India

8. Department of Technology Education, Chungnam National University, Daejeon 34134, Republic of Korea

9. College of Business, Kwangwoon University, Seoul 01897, Republic of Korea

Abstract

The Internet of Things (IoT) is a platform that manages daily life tasks to establish an interaction between things and humans. One of its applications, the smart office 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 office automation system are an unfriendly user interface, lack of IoT technology, high cost, or restricted range of wireless transmission. Therefore, this paper presents the design and fabrication of an IoT-based office automation system with a user-friendly smartphone interface. Also, real-time data monitoring is conducted for the predictive maintenance of sensor nodes. This model uses an Arduino Mega 2560 Rev3 microcontroller connected to different appliances and sensors. The data collected from different 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. The performance of both models, i.e., k-nearest neighbors and naive Bayes, was evaluated using different 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 efficiency while saving resources.

Funder

Ministry of Science, ICT and Future Planning

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

Reference34 articles.

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