A Service-Based Cloud-Edge Fusion Approach for Abnormality Detection of Power Generation Equipment

Abnormality detection of power generation equipment is of great significance in enhancing equipment reliability. In the era of Industry 4.0, with the rapid development and extensive application of IoT and sensor technology, large-scale sensing devices are deployed in power plants, resulting in a vast amount of sensor streaming data. The emergence of edge computing enables the streaming data processing and computation on edge devices. It reduces the latency of streaming data processing and improves throughput. However, those edge nodes are heterogeneous, decentralized, and capacity-constrained edge nodes. Moreover, the conventional cloud-edge model exhibits some characteristics such as tight coupling of data and computing, hard to deal with temporally varying continuity, and lack of flexibility. It poses significant challenges to detect normality and provide anomaly warning of power generation equipment. In order to deal with those challenges, we propose a service-oriented approach for fusing cloud-edge capabilities. Firstly, we encapsulated the streaming data and its processing into suitable granular service, serving as fundamental units for basic streaming data processing. These services can be deployed independently and flexibly scheduled in cloud-edge environment to facilitate the development of IoT application systems by developers. The services help to decouple the streaming data and its computing. Secondly, we proposed an event-driven mechanism to enable dynamic collaboration among services, allowing proactive response to events and adaptive adjustment of logic for streaming data processing to cope with the dynamics of IoT and time-varying logic of streaming data processing. This enhances service deployment flexibility, reduces latency in streaming data processing and improved the efficiency. Finally, based on the actual scenario of the fire power plants, we validated the feasibility and effectiveness in detecting equipment abnormalities by using our proposed cloud-edge fusion approach. We compared our proposed approach with three typical streaming data processing architectures including Cloud, iFogSim and PureEdgeSim from processing latency and system throughput. It reduced the latency remarkably with an average reduction of about 78%, 22%, and 16%. It improved system throughput of about 57%, 17%, and 14%.