Submit a Manuscript to the Journal

Journal of the Chinese Institute of Engineers

For a Special Issue on

Real-time Anomaly Detection and Predictive Maintenance for Intelligent Electrical Appliances

Manuscript deadline
30 April 2024

Cover image - Journal of the Chinese Institute of Engineers

Special Issue Editor(s)

Prof. Cheng-Chien Kuo, National Taiwan University of Science and Technology, Taiwan
[email protected]

Dr. Jiaqi Ruan, The Hong Kong Polytechnic University, Hong Kong
[email protected]

Dr. Abdelmalek Atia, University of El Oued, Algeria
[email protected]

Dr. Waheed Mobolaji Ashagidigbi, Federal University of Technology Akure, Nigeria
[email protected]

Submit an ArticleVisit JournalArticles

Real-time Anomaly Detection and Predictive Maintenance for Intelligent Electrical Appliances

Real-time anomaly detection and predictive maintenance are essential for ensuring intelligent electrical appliances' optimal performance and reliability. Anomaly detection involves identifying any unusual patterns or deviations from the normal behavior of an appliance. This could include spikes in energy usage, unusual noises, or other performance issues.

On the other hand, predictive maintenance involves using data analytics and machine learning techniques to predict when an appliance is likely to fail or require maintenance. By analyzing historical data and real-time performance metrics, predictive maintenance algorithms can identify patterns and trends that indicate an appliance is at risk of malfunctioning.

Intelligent electrical appliances can benefit greatly from these techniques. By monitoring performance in real-time and identifying potential issues before they become serious, appliances can be serviced proactively, minimizing downtime and reducing the risk of catastrophic failures. Additionally, predictive maintenance can help extend the appliances' lifespan by identifying areas where maintenance or repairs can be performed to prevent further damage. Various technologies can be used to implement real-time anomaly detection and predictive maintenance, including sensors, data analytics software, and machine learning algorithms. These technologies can be integrated into the appliance or a monitoring system that tracks real-time performance metrics. Intelligent electrical appliances, such as air conditioners, washing machines, and refrigerators, have been applied to households for many years. With the development of the Internet of Things (IoT), more intelligent electrical appliances are emerging. These IoT intelligent electrical appliances provide users convenience and cause problems such as breakdowns. Therefore, real-time anomaly detection and predictive maintenance for these IoT intelligent electrical appliances are economically and socially crucial. The development of intelligent electrical appliances has changed people's lives. However, they are prone to special hazards when user errors happen, such as overheating and voltage imbalance, because of their complex internal structure, which can lead to severe consequences. Hence, there is a need for efficient anomaly detection and predictive maintenance methods for these appliances.

Overall, real-time anomaly detection and predictive maintenance are critical components of intelligent electrical appliances, helping to ensure optimal performance and minimize downtime and maintenance costs. This special issue explores the latest advancements in real-time anomaly detection and predictive maintenance techniques for intelligent electrical appliances. This issue will focus on techniques that can improve appliances' reliability, performance, and lifespan while minimizing downtime and maintenance costs. Additionally, it will highlight applications and case studies that demonstrate the effectiveness of these techniques in various domains.

Topics of interest include but are not limited to:

  • Real-time anomaly detection techniques for intelligent electrical appliances
  • Predictive maintenance techniques for intelligent electrical appliances
  • Real-time anomaly detection and predictive maintenance techniques for intelligent electrical appliances
  • Techniques to improve intelligent electrical appliances' reliability, performance, and lifespan while minimizing downtime and maintenance costs.
  • Real-time anomaly detection and predictive maintenance applications
  • Intelligent energy management systems for intelligent electrical appliances
  • Applications of anomaly detection and predictive maintenance techniques in the electrical appliance domain.
  • Current challenges in applying machine learning approaches to real-time anomaly detection and predictive maintenance schemes.
  • New problems arise when using these techniques in the real world.
  • Sensor technologies for real-time monitoring of electrical appliances
  • Data analytics and machine learning techniques for anomaly detection and predictive maintenance
  • Integration of anomaly detection and predictive maintenance algorithms into intelligent electrical appliances
  • Security and privacy issues related to real-time monitoring and maintenance of appliances

Submission Instructions

  • The manuscript submitted should be single-column, double-spaced, 30 pages including figures and illustrations for a full paper. The font size should be 11 points.
  • Please select the special issue title "Communication technologies for security implementations in all network layers" when submitting your paper to Submission Portal.
  • Please sign a “Page Charge Form” and “Copyright Agreement”, and upload these forms by submission Portal. You have to sign on the “Page Charge Form” and “Copyright Agreement” by yourself. Please don't just key in your name.

Instructions for AuthorsSubmit an Article