With the continuous expansion of industrial systems, multisource and heterogeneous industrial data have increased rapidly, making the construction of a structured industrial knowledge system a core requirement in the industrial domain. Industrial knowledge graph (IKG) serves as a key approach for knowledge structuring and relation modeling and has become an indispensable foundation for industrial tasks. However, existing IKG construction methods still face core challenges such as data heterogeneity, complex semantic understanding, frequent knowledge changes, and limited automation. Inspired by the construction of IKG by industry experts, we propose CoMA-IKG, an large language model (LLM)-driven collaborative multiagent framework for automated construction of IKG. In the industrial data processing stage, an LLM-driven adaptive chunking agent is developed to achieve semantically complete and self-adjusting segmentation. In the triple extraction stage, a cluster of LLM-driven agents for progressive triple reasoning extraction and mechanism-aware logical discrimination is constructed to enable accurate industrial triple extraction under stepwise reasoning and industrial mechanism constraints. In the IKG evolution stage, an LLM-driven co-evolution agent is developed to generate evolution commands automatically based on the structural state of the IKG and real-time industrial data changes, enabling autonomous updating and continuous evolution of the IKG. Experimental results show that CoMA-IKG significantly outperforms existing automated knowledge graph construction methods in terms of relation mining, logical reasoning, and dynamic evolution of the IKG.

Grasping tasks are crucial in industrial manufacturing, where precise and efficient object grasping ensures smooth assembly processes and stable operations. Robotic arms, deployed in industrial environments, require timely and accurate computations to perform these tasks. This paper introduces BGRN, an RGB-D fusion-based binary grasp prediction network designed for lightweight grasp pose prediction in such settings. We propose a binary grasp pose prediction framework that significantly reduces memory usage by quantizing both weights and activations to 1 bit. Additionally, an interaction fusion module improves the integration of RGB and depth images, while an information recovery connection helps mitigate feature loss caused by binarization. Experimental results show that BGRN achieves competitive accuracy and notable reductions in memory usage and computational load compared to full-precision models.

Recently, foundation models (such as ChatGPT) have emerged with powerful learning, understanding, and generalization abilities, showcasing tremendous potential to revolutionarily promote modern industry. Despite significant advancements in various fields, existing general foundation models face challenges in industry when dealing with the data of specialized modalities, the tasks of varying-scenario with multiple processes, and the requirements of trustworthy output, which makes industrial foundation model (IFM) a necessity. This article proposes a system architecture of termed IFMsys, including model training, model adaptation, and model application. Specifically, in model training, a base model is constructed by pretraining on multimodal industrial data and fine-tuning with fundamental industrial mechanisms. In model adaptation, the base model is developed into a series of task-oriented and domain-specific IFMs through fine-tuning with representative tasks and domain knowledge. In model application, an industrial agent-centric collaboration system and a comprehensive application framework of IFM are proposed to enhance the industrial product lifecycle applications. In addition, a prototype system of the IFM, namely, MetaIndux, is delivered, with application examples presented in typical industrial tasks. Finally, future research directions and open issues of IFM are prospected. We hope this article will inspire the advancements in the theories, technologies, and applications in this emerging research field of IFM.

Industrial time-series prediction is crucial for Industrial Internet of Things. Due to the complexity and variation of modern industry, knowledge transfer for varying data has been an attractive research area. However, conventional methods may overlook the intradomain distribution and the mutual information, leading to incorrect semantic alignment and loss of prediction-relevant information. To address these issues, a contrastive learning-based domain adaptation method, contrastive temporal prediction adaptation, for industrial time-series cross-domain prediction is proposed. It leverages a contrastive domain generalization and a contrastive self-supervised alignment method to obtain stable representations and capture the relationship between the data distribution and labels, to bring samples with similar labels closer in the feature space. Besides, an instancewise adversarial discrimination is developed to leverage the data distribution to mitigates interference from irrelevant information. The performance of our method is verified through experiments on CMAPSS dataset. The results demonstrate that our method outperforms existing methods.

The Industrial Internet of Things (IIoT) amalgamates cutting-edge information technologies, including artificial intelligence, big data, and cloud computing, to establish a sophisticated platform for intelligent predictive maintenance of complex industrial equipment. While numerous predictive maintenance methodologies have been proposed, much of the existing research predominantly emphasizes predictive techniques, with limited attention devoted to developing a comprehensive predictive maintenance framework. To bridge this scholarly gap, this paper proposes a novel cloud-edge adaptive framework for equipment predictive maintenance in IIoT. Positioned across the cloud, edge, and equipment planes of the IIoT infrastructure, this framework adeptly addresses challenges such as highly generalized collaborative modeling, scenario-specific modeling, and continuous dynamic evolution of equipment predictive maintenance in the Industrial Internet. Consequently, this framework offers a methodical and holistic solution to predictive maintenance for industrial equipment.

Edge computing exploits node devices situated in close proximity to terminals to deliver distributed computing services directly to users, with FPGAs serving as the predominant platform. With the amplification in volume and intricacy of deep learning models, effectively deploying these models on FPGA devices introduces substantial challenges. To counter this predicament, this paper introduces an FPGA-based hardware acceleration framework for reconfigurable edge devices tailored for Convolutional Neural Networks (CNNs). Initially, a method for dynamically quantizing network models is devised to markedly curtail model memory consumption. Subsequently, a meticulously engineered hardware acceleration unit is formulated to attain augmented computing parallelism via meticulous temporal redesign. Finally, model deployment on FPGA devices for inference verification is showcased utilizing fully connected and convolutional neural networks as exemplars. On the MNIST dataset, the FPGA inference unit attains remarkable accuracy and computational efficiency in comparison to CPUs and GPUs

Time-series prediction plays a crucial role in the Industrial Internet of Things (IIoT) to enable intelligent process control, analysis, and management, such as complex equipment maintenance, product quality management, and dynamic process monitoring. Traditional methods face challenges in obtaining latent insights due to the growing complexity of IIoT. Recently, the latest development of deep learning provides innovative solutions for IIoT time-series prediction. In this survey, we analyze the existing deep learning-based time-series prediction methods and present the main challenges of time-series prediction in IIoT. Furthermore, we propose a framework of state-of-the-art solutions to overcome the challenges of time-series prediction in IIoT and summarize its application in practical scenarios, such as predictive maintenance, product quality prediction, and supply chain management. Finally, we conclude with comments on possible future directions for the development of time-series prediction to enable extensible knowledge mining for complex tasks in IIoT.

For prognostics and health management of industrial systems, machine remaining useful life (RUL) prediction is an essential task. While deep learning-based methods have achieved great successes in RUL prediction tasks, large-scale neural networks are still difficult to deploy on edge devices owing to the constraints of memory capacity and computing power. In this article, we propose a lightweight and adaptive knowledge distillation (KD) framework to alleviate this problem. First, multiple teacher models are compressed into a student model through KD to improve the industrial prediction accuracy. Second, a dynamic exiting method is studied to enable an adaptive inference on the distilled student model. Finally, we develop a reparameterization scheme to further lessen the student network. Experiments on two turbofan engine degradation datasets and a bearing degradation dataset demonstrate that our method significantly outperforms the state-of-the-art KD methods and enables the distilled model with an adaptive inference ability.

Industrial Internet of Things data incorporate the fundamental elements of industrial processes, providing novel paradigms of predictive maintenance for complex industrial equipment. Remaining useful life prediction is critical in the predictive maintenance task of product lifecycle management, which has attracted increasing research attention. However, most existing prediction methods cannot effectively extract complex multiscale temporal patterns and cannot meet the real-time requirements of industrial sites. To address these issues, we propose a$T^{2}$-Tensor-aided multiscale transformer for accurate and effective prediction in this article. We defined the$T^{2}$-tensor to represent the multiscale temporal pattern by reconstructing the time series. Besides, a high-order transformer for multiscale feature extraction is proposed. Particularly, the multiscale characteristics can be captured through intertoken and intratoken. In addition, a transformer parameter lightweighting method with tensor ring decomposition is developed. Experiments demonstrate the accuracy and efficiency of the proposed method.

In cloud manufacturing systems, fault diagnosis is essential for ensuring stable manufacturing processes. The most crucial performance indicators of fault diagnosis models are generalization and accuracy. An urgent problem is the lack and imbalance of fault data. To address this issue, in this article, most of existing approaches demand the label of faults asa prioriknowledge and require extensive target fault data. These approaches may also ignore the heterogeneity of various equipment. We propose a cloud-edge collaborative method for adaptive fault diagnosis with label sampling space enlarging, named label-split multiple-inputs convolutional neural network, in cloud manufacturing. First, a multiattribute cooperative representation-based fault label sampling space enlarging approach is proposed to extend the variety of diagnosable faults. Besides, a multi-input multi-output data augmentation method with label-coupling weighted sampling is developed. In addition, a cloud-edge collaborative adaptation approach for fault diagnosis for scene-specific equipment in cloud manufacturing system is proposed. Experiments demonstrate the effectiveness and accuracy of our method.