Diagnosing Electrical Appliance Health for Predictive Maintenance using mmWave Radar

Abstract

ABSTRACT Electrical appliances are ubiquitous in modern households and industries, playing a crucial role in daily operations. However, the reliability and longevity of these appliances can be compromised by wear and tear, electrical faults, or other issues that may arise over time. To address this challenge, we propose a novel approach for diagnosing the health of electrical appliances using millimeter-wave (mmWave) radar technology. In this research, we leverage the unique capabilities of mmWave radar sensors to remotely monitor the condition of electrical appliances without the need for physical contact. By analyzing the reflections of mmWave signals from the surfaces of appliances, we can extract valuable information about their structural integrity, operational status, and potential faults. Key aspects of our approach include extracting data from mmWave sensor and processing them for appliance health diagnosis, which enable the detection of anomalies such as loose connections, insulation degradation, and mechanical wear. Furthermore, we explore the use of machine learning techniques to enhance the accuracy and reliability of appliance health assessment based on radar data. Through experimental validation and real-world deployment scenarios, we demonstrate the effectiveness and feasibility of our proposed method for predictive maintenance of electrical appliances. As per the analysis done throughout this research when a healthy electrical device is near the sensor, the Doppler index values, which represent frequency values are shifted to the right and when a faulty device is near by the values are more shifted to the left. By proactively identifying impending issues and scheduling maintenance tasks accordingly, our approach can help prevent costly downtime, improve energy efficiency, and prolong the lifespan of electrical appliances. Overall, this research contributes to advancing the field of predictive maintenance by harnessing the capabilities of mmWave radar technology for non-invasive, remote diagnosis of electrical appliance health, with potential applications in both residential and industrial settings.