Intelligent data-driven condition monitoring of power electronics systems using smart edge–cloud framework

Abstract

The ongoing revolution in industrial production- Industry 4.0, is driven by transformative technologies such as the Industrial Internet of Things (IIoT), Artificial Intelligence (AI), single-board computers, and 5G communication. As the trend towards IIoT continues, an increasing number of industrial drive systems and their fleets are being connected to the cloud. This enables the manufacturers to perform condition monitoring (CM) and streamlined maintenance activities. At the heart of these drive systems are Power Electronics Systems (PESs), which operate at high switching frequencies (10 kHz–1 MHz) to efficiently transfer electrical power and deliver it to a load in a controlled manner. However, due to their functionalities and the presence of semiconductor switches, PESs are susceptible to failure, necessitating effective condition monitoring (CM) for fault detection and improved lifetime. Link to this issue, to enable CM based on high-frequency data, an industrial site with multiple electric drives is required to record data up to 15TB/week. Therefore, there is a demand from industrial partners to establish intelligent communication between a fleet of physical systems and the cloud to reduce transmission, storage, and bandwidth costs, as well as to enable real-time fault detection and learning from fleet operations. This paper proposes an intelligent edge–cloud computing methodology to address the challenge of high-frequency data monitoring for PESs, focusing on novelty detection and selective data transmission to reduce transmission costs. The methodology involves developing a novel edge–cloud framework that incorporates a neural network-based novelty detector for selective data transmission from physical systems to the cloud. The proposed methodology is evaluated through hardware tests, demonstrating a significant reduction in data transmission (94%) and potential cost savings of up to €5.9k/year for a single remote system. 95.6% detection accuracy of the PQ phase is obtained during experimental tests over 590 samples. Thus, this paper contributes to the vision of the smart grid and IIoT by analyzing the Power Quality (PQ) monitoring problem of a three-phase grid and showcasing the capability of the proposed framework in terms of novelty detection and data transmission cost reduction. To conclude, the proposed intelligent edge–cloud computing methodology offers a promising solution for effective condition monitoring of PESs, with potential cost savings and improved fault detection capabilities. By leveraging advanced technologies and intelligent data-driven approaches, this framework advances the goals of Industry 4.0 and paves the way for efficient and reliable industrial operations in the digital age.