Autonomous Industrial Cyber-Physical Systems (ICPS) represent a future vision where industrial systems achieve full autonomy, integrating physical processes seamlessly with communication, computing and control technologies while holistically embedding intelligence. Cloud-Fog Automation is a new digitalized industrial automation reference architecture that has been recently proposed. This architecture is a fundamental paradigm shift from the traditional International Society of Automation (ISA)-95 model to accelerate the convergence and synergy of communication, computing, and control towards a fully autonomous ICPS. With the deployment of new wireless technologies to enable almost-deterministic ultra-reliable low-latency communications, a joint design of optimal control and computing has become increasingly important in modern ICPS. It is also imperative that system-wide cyber-physical security are critically enforced. Despite recent advancements in the field, there are still significant research gaps and open technical challenges. Therefore, a deliberate rethink in co-designing and synergizing communications, computing, and control (which we term “3C co-design”) is required. In this paper, we position Cloud-Fog Automation with 3C co-design as the new paradigm to realize the vision of autonomous ICPS. We articulate the state-of-the-art and future directions in the field, and specifically discuss how goal-oriented communication, virtualization-empowered computing, and Quality of Service (QoS)-aware control can drive Cloud-Fog Automation towards a fully autonomous ICPS, while accounting for system-wide cyber-physical security.

Virtual Programmable Logic Controllers (vPLCs), as a newborn technology, are becoming increasingly important in modern industrial automation due to their flexibility and scalability. There is lack of researches on data synchronization and redundancy mechanisms for vPLCs, limiting applications of vPLCs in critical industrial scenarios. This paper designs and implements a data synchronization and redundancy mechanism between vPLCs based on heartbeat detection to enhance the reliability of vPLC systems. The mechanism continuously monitors for failures and synchronizes data between vPLCs to ensure seamless control task takeover in the event of a failure. Experimental results demonstrate the mechanism's high effectiveness in fault detection and recovery, achieving a redundancy switchover time that meets industrial application requirements.

Time-sensitive networking (TSN) is an important research direction for the transformation and upgrading of industrial internet infrastructure. In future industrial sites, TSN and traditional industrial networks will coexist in the same network, and this integration will be inevitable. Ensuring reliable and deterministic transmission of data flows in the converged network of Profinet and TSN will be a key research topic. This paper presents a compatible way for the Cyclic Queuing and Forwarding (CQF) queuing model of TSN and the Isochronous Real-Time (IRT) communication of Profinet. Firstly, we propose a Delay Reservation Mechanism based on CQF (DRM-CQF). This mechanism achieves reliable and deterministic transmission by delaying the sending time of cross-domain data flows in the Profinet and reserving transmission opportunities for cross-domain data flows in TSN. Secondly, we construct a mathematical optimization model based on DRM-CQF to schedule data flows in the converged network to seek the optimal schedule. Experimental results show that DRM-CQF can ensure the reliable transmission of cross-domain data flows in the Profinet and TSN converged network, and the end-to-end average delay is reduced by 49% compared with other CQF scheduling methods.

With the rapid development of the industrial internet of things and automation control systems, supervisory control and data acquisition (SCADA) systems have been widely adopted in industrial manufacturing due to their flexibility and scalability. The cloud-fog automation (CFA) paradigm is emerging to address higher real-time and computing demands in complex industrial environments. To fully leverage the efficiency, flexibility, and scalability of Kubernetes, an open-source container orchestration platform Kubernetes in managing containerized applications, this article investigates methods for deploying SCADA systems on the Kubernetes platform. This approach aims to capitalize on Kubernetes' benefits, such as automated deployment, elastic scaling, and high availability, to optimize resource management and enhance system performance. To validate the proposed solution, we employs testing tools such as wrk and tc, along with monitoring tools like Prometheus and Grafana, to conduct a comprehensive evaluation of Kubernetes' advantages in various scenarios. We focus on three key aspects: reliability, scalability, and security. The results demonstrate that Kubernetes can significantly improve the scalability, fault recovery capabilities, and stability of SCADA systems.

As industrial equipment becomes increasingly complex and intelligent, fault diagnosis (FD) technology has emerged as a critical means to ensure system reliability and safety, spurring the development of various real-time online monitoring techniques. Traditional fault diagnosis methods primarily rely on single data sources and specific algorithms, which makes it challenging to effectively integrate the multimodal data captured by diverse sensors and often overlooks the vital role of human expertise in the diagnostic process. By leveraging a universal fault diagnosis framework that combines Large Language Models (LLMs) with multimodal data, existing methods can be seamlessly integrated. LLMs possess powerful capabilities in natural language understanding, knowledge integration, and reasoning, enabling them to analyze text, images, signals, and other types of multimodal information to facilitate zero-shot and few-shot knowledge reasoning and fault diagnosis. This paper systematically reviews the development of LLM- and multimodal data-based fault diagnosis technologies, outlines key techniques such as data-driven processing, feature extraction, and feature fusion within LLM frameworks, and analyzes the technological advancements fostered by these methods. It also summarizes the advantages and limitations of this approach in fault diagnosis and health state assessment, and offers an outlook on the future trends and challenges of applying LLMs in multimodal fault diagnosis. The aim is to provide technical guidance for researchers and engineers, thereby accelerating the innovation and application of intelligent fault diagnosis technologies.

Bearing system is critical components in rotating machinery, whose health directly impacts operational safety. To address faults arising from bearing wear—and to overcome the limitations of conventional deep-learning methods that struggle to exploit both time- and frequency-domain information simultaneously—we propose FD-MVLLM, a novel reprogramming framework that combines a large language model (LLM) with multimodal vibration data for fault diagnosis. First, raw vibration signals are preprocessed to produce three distinct modalities: the original time series and two time–frequency representations. Convolutional layer then extract features from the time–frequency images. Innovatively, we reprogram both the raw time series and the image features using carefully designed text prototypes, yielding patch embeddings. To fully leverage the LLM's reasoning capabilities and boost diagnostic accuracy, we also integrate key time-domain and frequency-domain evaluation metrics into the prompt context, producing prompt embeddings. These patch and prompt embeddings are fed into an LLM fine-tuned via low-rank adaptation (LoRA); a final linear output layer translates the LLM's output into precise fault diagnoses. We validate FD-MVLLM on simulated rolling bearing fault data—generated using Hertz contact theory and Runge-Kutta numerical integration—as well as on several public benchmark datasets. Experimental results demonstrate that FD-MVLLM substantially outperforms fault diagnosis methods based on single-modal vibration data and LLM., highlighting its promise as a new paradigm for multimodal data-driven fault diagnosis. This work is open-sourced at https://github.com/youngpy996/FD-MVLLM.

The advancement of large models has initiated a transformation in the field of time-series forecasting. Both the repurposing of existing large models and the development of large models tailored for time-series analysis have exhibited impressive performance. In industrial applications, challenges, such as limited data availability and constrained computational resources, render the first approach viable. However, it is important to note that this approach is still in its infancy and lacks both a thorough technical analysis and a unified effective framework. Meanwhile, as large models become a mainstream artificial intelligence paradigm, it is urgent to discuss typical industrial scenarios, such as how automated systems can transition from intelligent to collaborative operation and maintenance. In light of this premise, this article endeavors to advance a generalized technical framework for large model-driven time-series forecasting, under which existing methods can be subsumed. Then, within this overarching technical paradigm, the technical advancements facilitated by diverse methods will be systematically elucidated and analyzed, along with a comparative evaluation conducted across seven benchmark datasets. Concluding this analysis, the implementation pathway for the industrial automation system is delineated that integrates operator action commands to forecast post-action trends to assess action correctness in advance. Finally, the challenges and future directions of large model-based time-series forecasting are outlined.