Key points are not available for this paper at this time.
Edge computing is an emerging paradigm that offloads the computations and analytics workloads onto the Internet of Things (IoT) edge devices to accelerate the computation efficiency, reduce the channel occupation of signal transmission, and reduce the storage and computation workloads on the cloud servers. These distinct merits make it a promising tool for IoT-based machine signal processing and fault diagnosis. This article reviews the edge computing methods in signal processing-based machine fault diagnosis from the aspects of concepts, state-of-the-art methods, case studies, and research prospects. In particular, the lightweight designed algorithms and application-specific hardware platforms of edge computing in the typical fault diagnosis procedures, including signal acquisition, signal preprocessing, feature extraction, and pattern recognition, are reviewed and discussed in detail. The review provides an insight into the edge computing framework, methods, and applications, so as to meet the requirements of IoT-based machine real-time signal processing, low-latency fault diagnosis, and high-efficient predictive maintenance.
Lu et al. (Thu,) studied this question.