Industrial fault diagnosis is a core technical support for ensuring the safe and efficient operation of modern industrial measurement systems. However, the key challenges of complex spatiotemporal correlations and sparse local fault features in sensor network data severely constrain the measurement accuracy of traditional diagnostic methods. To address these challenges, this paper proposes a novel Local Feature Augmented Graph-Transformer (LFA-GTF) framework, which integrates global topology modeling and local signal enhancement techniques. Firstly, the framework constructs a GraphTransformer (GTF) architecture, which accurately captures global spatiotemporal dependencies in industrial data by integrating a multi-head self-attention mechanism. Secondly, a Local Feature Augmentation (LFA) module is designed to mitigate feature sparsity by adaptively enhancing discriminative local patterns. Comprehensive experiments on the revised Tennessee Eastman (TE) dataset demonstrate that the LFA-GTF framework achieves a fault classification accuracy of 96.06%, outperforming baseline graph neural networks (GNNs) such as GCN, GAT, and GraphSage by 14.3–41.7%. Ablation studies verify that the LFA module improves classification performance by 5.0–13.7% across fault types with sparse features, and it also exhibits good generalization ability on Three-Phase Flow (TPF) processes. This framework provides a powerful solution for fault diagnosis of industrial measurement systems under unbalanced data conditions.

Process industries, including chemical, petrochemical, energy, and wastewater treatment systems, operate under high-dimensional, nonlinear, dynamic, and safety-critical conditions. Data-driven process monitoring and fault detection and diagnosis (FDD) have progressed from multivariate statistical monitoring to machine learning and deep learning. Yet many deployed or prototype systems still behave mainly as fault classifiers: they detect deviations, but offer limited causal explanation, weak integration of plant knowledge, and insufficient support for safe operator action. Recent advances in large language models, retrieval-augmented generation, digital twins, explainable artificial intelligence, and multi-agent systems make it timely to revisit FDD as an agentic decision-support workflow. This focused review examines how agentic AI can support process-industry monitoring and diagnosis by integrating process data, engineering knowledge, model outputs, and safety constraints. We synthesize established FDD foundations, deep-learning-based FDD, process-safety context, bridging technologies, and emerging LLM- and agent-based studies. The review argues that the near-term value of industrial agents lies not in unrestricted autonomous plant control, but in safety-aware, explainable, and human-in-the-loop decision support. We propose a process-industry-oriented taxonomy of agents, summarize enabling technologies and representative application settings, and identify evaluation criteria, benchmark requirements, limitations, and deployment conditions for trustworthy industrial agents.

Image datasets with imbalanced sampling, masking, missing and noise brought challenges to the development of an intelligent agricultural monitoring system. To tackle these issues, this paper proposes a cloud-based, multi-model integrated intelligent monitoring vision platform for agricultural greenhouse crops (named the CloudCropFuture platform), complete with algorithmic APIs, facilitating streamlined data-driven decision-making. For the CloudCropFuture platform, we first propose an image augmentation technology that employs an improved diffusion model to rectify deficiencies in image data, thereby enhancing the accuracy of agricultural image analysis. Experimental results demonstrate that on datasets enhanced by this method, the average precision of multiple YOLO models is improved by 5.6%. Then, a multi-level growth monitoring platform is introduced, integrating enhanced YOLOv11-based image models for more accurate and efficient crop observation. Furthermore, an intelligent model base comprising multiple integrated detection methods is established for assessing agricultural pests, maturity, and quality, leveraging the enhanced performance of vision models. CloudCropFuture offers a holistic solution for intelligent monitoring in agricultural greenhouses throughout the entire crop growth cycle. Through model verification and application across various greenhouse crops, this work has demonstrated the ability of the intelligent platform to provide reliable and stable monitoring performance. This research paves the way for the future development of agricultural technologies that can adapt to the dynamic and challenging conditions of modern farming practices.

Safety control based on barrier functions has gradually become one of the emerging and more important directions in the field of safety. Scholars are attempting to apply barrier functions to integer-order dynamical systems, such as general nonlinear systems, hybrid systems, linear systems, etc. Moreover, the introduction of barrier functions has even expanded the research approaches on safe reinforcement learning. However, there is very little research on the safety control problem of fractional-order dynamical systems. Based on our previous work, this article further explores, in depth, the problem of the transfer and adaptability of barrier functions for integer-order systems in fractional-order systems, and it also proposes the Caputo reciprocal barrier function and Caputo zeroing barrier function. And we established two theorems, which proved that we can also achieve uniform asymptotic stability or exponential stability with guaranteed safety. In the end, we created a new description for the definition of input-to-state safety under Caputo’s fractional-order systems, and we used this description and the above two Caputo barrier functions to construct two criteria of the Caputo input-to-state safety. Thus, we, finally, established the embryonic form of the theoretical framework of safety control based on barrier functions for fractional-order systems.