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Keh-Kim Kee
University College of Technology Sarawak
Electrical & Electronics Engineering

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Universal cyber physical system, a prototype for predictive maintenance Keh-Kim Kee; Simon Lau Boung Yew; Yun Seng Lim; Yip Ping Ting; Ramli Rashidi
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3216

Abstract

Industrial 4.0 technology of cyber-physical system enables real-time monitoring, sensing and actuating of physical machinery for predictive maintenance that replaces the conventional labor-intensive approach. This paper presents the design and development of a universal, cost-effective and internet of thing (IoT)-based proof-of-concept prototype universal cyber-physical system (UniCPS) with a cloud platform with an open and modular-based design of three-tier system architecture. The prototype demonstrates promising precision and accuracy for predictive maintenance on a pilot use case with MAPE of 3.77%, and average RMSE of 0.50. Besides, real-time visualization and detection of anomaly were also demonstrated with a cloud-based solution. The maintenance alert sent out by the actuator serves to notify the authorized personnel immediately for corrective action. As an extension to this work, a wireless sensor network can be incorporated in future work to acquire various data from diverse locations to overcome the limitations of sensor data.
Impact of nonintrusive load monitoring on CO2 emissions in Malaysia Keh-Kim Kee; Yun Seng Lim; Jianhui Wong; Kein Huat Chua
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2979

Abstract

Nonintrusive load monitoring (NILM) based energy efficiency can conserve electricity by creating awareness with the behaviour change and shrinking CO2 emissions to the environment. However, the lack of effective models and strategies is problematic for policymakers to forecast quantitatively CO2 emissions. This paper aims to study the impact of NILM on CO2 emissions in Malaysia. Firstly, the predictive models were established based on Malaysia open data from 1996 to 2018. After that, scenario simulations were conducted to predict CO2 emissions and NILM impact on environmental degradation in 2019-2030. The results revealed that a 12% reduction in electricity consumption due to NILM could contribute to a 10.2% shrinkage of the total CO2 emissions. The result also statistically confirmed Malaysia to achieve a 45% reduction of CO2 intensity in 2030. With NILM, the carbon reduction can be further enhanced to 60.2%. The outcomes provide valuable references and supporting evidence for policymakers in planning effective carbon emission control policies and energy efficiency measures. The work can be extended by developing a decision support system and user interfaces access via the cloud.
Co-Authors Jianhui Wong Kein Huat Chua Ramli Rashidi Simon Lau Boung Yew Yip Ping Ting Yun Seng Lim Yun Seng Lim