Work

Edge–Cloud Collaboration for Machine Condition Monitoring: A Comprehensive Review of Mechanisms, Models, and Applications

Year: 2026

Type: article

Abstract: Machine condition monitoring increasingly depends on distributed sensing, edge intelligence, and cloud analytics, yet timely and trustworthy health assessment remains constrained by latency, bandwidth, privacy, and reliability requirements. Cloud-only architectures provide scalable computation and historical data integration but often fail to satisfy real-time industrial needs, whereas edge-only deployments are limited by restricted computing resources and fragmented local knowledge. Edge–cloud collaboration has, therefore, emerged as a practical architecture for distributing perception, inference, learning, and coordination across hierarchical industrial systems. This review examines 147 publications on edge–cloud collaboration for machine condition monitoring published between 2019 and February 2026. A four-dimensional taxonomy is developed to organize the literature into model-centric, data-centric, resource and task-centric, and architecture and trust-centric mechanisms, while 13 survey and review papers are considered separately for contextual comparison. On this basis, the review analyzes representative collaboration mechanisms and enabling technologies, with particular attention to federated learning, transfer learning, knowledge distillation, digital twins, and deep reinforcement learning, and surveys their deployment in manufacturing, energy, transportation, and infrastructure monitoring scenarios. The literature remains dominated by model-centric collaboration, while architecture and trust-centric studies increasingly provide the system foundations required for practical deployment. The review further identifies major open challenges, including robust generalization under changing operating conditions, efficient data transmission, real-time resource coordination, interoperability, and trustworthy large-scale deployment, and outlines future directions in foundation-model-based edge–cloud collaboration, continual learning, dual digital twins, trustworthy collaboration, and privacy-preserving industrial ecosystems.

Source: Machines

DOI: 10.3390/machines14050476

Authors: Liyuan Yu, Jitao Fang, Qiuyan Wang, Fajia Li, Haining Liu

Institutions: Shandong Sport University, University of Jinan

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896