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With the development of big data technology, traditional monitoring methods for the scope of marine pollution can no longer meet the current needs of accuracy and timeliness. In light of the outstanding topic, this study proposed to use big data technology to monitor the scope of marine pollution. The intelligent digital remote sensing technology was used for multi-dimensional monitoring of ocean water quality and completed the calculation of data collected by water quality sensors through the improved big data comparative analysis method. Finally, the scope of pollution monitoring was realized. The results verified that the proposed monitoring method could achieve high-precision and time-sensitive monitoring of the range of marine pollutants, and could identify the basic information of pollutants.

In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of “offshore wind power engineering and biological and environment”, the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms.

The intelligent crack detection method is an important guarantee for the realization of intelligent operation and maintenance, and it is of great significance to traffic safety. In recent years, the recognition of road pavement cracks based on computer vision has attracted increasing attention. With the technological breakthroughs of general deep learning algorithms in recent years, detection algorithms based on deep learning and convolutional neural networks have achieved better results in the field of crack recognition. In this paper, deep learning is investigated to intelligently detect road cracks, and Faster R-CNN and Mask R-CNN are compared and analyzed. The results show that the joint training strategy is very effective, and we are able to ensure that both Faster R-CNN and Mask R-CNN complete the crack detection task when trained with only 130+ images and can outperform YOLOv3. However, the joint training strategy causes a degradation in the effectiveness of the bounding box detected by Mask R-CNN.

This paper proposes an Intelligent Monitoring System (IMS) for Photovoltaic (PV) systems using affordable and cost-efficient hardware and also lightweight software that is capable of being easily implemented in different locations and having the capability to be installed in different types of PV power plants. IMS uses the Internet of Things (IoT) platform for handling data as well as Interoperability and Communication among the devices and components in the IMS. Moreover, IMS includes a personal cloud server for computing and storing the acquired data of PV systems. The IMS also consists of a web monitor system via some open-source and lightweight software that displays the information to multiple users. The IMS uses deep ensemble models for fault detection and power prediction in PV systems. A remarkable ability of the IMS is the prediction of the output power of the PV system to increase energy yield and identify malfunctions in PV plants. To this end, a long short-term memory (LSTM) ensemble neural network is developed to predict the output power of PV systems under different environmental conditions. On the other hand, the IMS uses machine learning-based models to detect numerous faults in PV systems. The fault diagnostic of IMS is based on the following stages. Firstly, major features are elicited through an analysis of Current–Voltage (I–V) characteristic curve under different faulty and normal events. Second, an ensemble learning model including Naive Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) is used for detecting and classifying fault events. To enhance the performance in the process of fault detection, a feature selection algorithm is also applied. A PV system has been designed and implemented for testing and validating the IMS under real conditions. IMS is an interoperable, scalable, and replicable solution for holistic monitoring of PV plant from data acquisition, storing, pre-and post-processing to malfunction and failure diagnosis, performance and energy yield assessment, and output power prediction.

Tool wear is inevitable in cutting process. If tool wear failure is not detected in time, it will lead to abnormal cutting process and affect the machining efficiency and quality seriously. The intelligent monitoring of tool wear can make the machining system perceive the real-time status of tools in advance and make early warning and decision-making, which is an effective way to ensure the efficient operation of machining and manufacturing system. By reviewing the research status of intelligent monitoring of tool wear, the key technical principles and methods of multisource-correlation signal selection, feature extraction and pattern recognition are classified. On the basis, the current application status of tool wear monitoring is discussed. In view of its shortcomings, this paper puts forward the prospect of the future, in order to provide a theoretical basis and reference for the development of tool wear intelligent monitoring technology and intelligent manufacturing industry.

Aero-engines, the core of air travel, rely on advanced high strength-toughness alloys (THSAs) such as titanium alloys, nickel-based superalloys, intermetallics, and ultra-high strength steel. The precision of cutting techniques is crucial for the manufacture of key components, including blades, discs, shafts, and gears. However, machining THSAs pose significant challenges, including high cutting forces and temperatures, which lead to rapid tool wear, reduced efficiency, and compromised surface integrity. This review thoroughly explores the current landscape and future directions of cutting techniques for THSAs in aero-engines. It examines the principles, mechanisms, and benefits of energy-assisted cutting technologies like laser-assisted machining and cryogenic cooling. The review assesses various tool preparation methods, their effects on tool performance, and strategies for precise shape and surface integrity control. It also outlines intelligent monitoring technologies for machining process status, covering aspects such as tool wear, surface roughness, and chatter, contributing to intelligent manufacturing. Additionally, it highlights emerging trends and potential future developments, including multi-energy assisted cutting mechanisms, advanced cutting tools, and collaborative control of structure shape and surface integrity, alongside intelligent monitoring software and hardware. This review serves as a reference for achieving efficient and high-quality manufacturing of THSAs in aero-engines. The energy field assisted mechanical processing technology methods and development status are introduced. The development of tool preparation technology for high-strength and toughness materials is elaborated. The development of collaborative technologies of structure shape and surface integrity is summarized. The development of intelligent monitoring technology is summarized. The development and sustainability of advanced cutting technologies for high strength-toughness alloys in aero-engines are prospected.

Rainfall-triggered landslide are a typical geological hazard in the southeastern coastal area of China. The disaster process of rainfall-triggered landslide is investigated by field monitoring, model tests, three-dimensional reconstruction and discrete element numerical simulation. Intelligent monitoring and warning technology for rainfall-triggered landslide on the basis of multisource information is proposed. The results show that confining pressure has an effect on the disaster process of rainfall-triggered landslide. The failure scale and trigger time under confining pressure are smaller than those without confining pressure, which is more suitable for determining the failure characteristics of rainfall-triggered landslide. All-weather monitoring of landslides is carried out by collinear triocular visual equipment. Feature matching, point reprojection and point cloud generation are carried out by automatically extracting image depth information. A three-dimensional model of the prototype slope is constructed to identify its danger zone. A three-dimensional numerical model of a slope is established via the discrete element method on the basis of three-dimensional reconstruction technology. By analysing the variation characteristics of the displacement and soil pressure of a slope under rainfall, a two-variable early-warning technology of displacement–soil pressure is proposed, which integrates multiple early-warning technologies. The instability mechanism of landslides under extreme rainfall is investigated. Moreover, a multisource intelligent monitoring and warning system for rainfall-triggered landslide is developed by integrating information from model tests, three-dimensional reconstructions, numerical simulations, and field monitoring. The visualization and intelligent prediction of rainfall landslide disaster situations are realized. This work can provide a reference for monitoring and warning of rainfall-triggered landslide.

Rapid developments in artificial intelligence and image processing have presented many new opportunities for defect detection in manufacturing processes. In this work, an intelligent image processing system has been developed to monitor inter-layer deposition quality during a wire arc additive manufacturing (WAAM) process. This system reveals the feasibility and future potential of using computer vision knowledge in WAAM. Information produced from this system is to be used in conjunction with other quality monitoring systems to verify the quality of fabricated components. It is tailored to identify the presence of defects relating to lack of fusion and voids immediately after the deposition of a given layer. The image processing system is built upon the YOLOv3 architecture and through moderate changes on anchor settings and achieves 53% precision on surface anomaly detection and 100% accuracy in identifying the fabricated components’ location, providing a prerequisite for high-precision assessment of welding quality. The work presented in this paper presents an inter-layer vision-based defect monitoring system in WAAM and serves to highlight the feasibility of developing such intelligent computer vision systems for monitoring the WAAM process for defects.

HighlightsThe as-designed triboelectric nanogenerator (TENG) generates an excellent electric output, which is an order of magnitude higher than that of TENGs made from commercial dielectric materials.The as-designed TENG-based sensor can detect worn debris in oils at least down to 0.01 wt% and water contamination down to 100 ppm, which are much better than other online monitoring methods (particle > 0.1 wt%; water > 1000 ppm).A high-selective monitoring system is successfully developed for distinguishing water contamination from the multi-mixed contaminants in lubricating oils.Triboelectric nanogenerators (TENGs) have potential to achieve energy harvesting and condition monitoring of oils, the “lifeblood” of industry. However, oil absorption on the solid surfaces is a great challenge for oil–solid TENG (O-TENG). Here, oleophobic/superamphiphobic O-TENGs are achieved via engineering of solid surface wetting properties. The designed O-TENG can generate an excellent electricity (with a charge density of 9.1 µC m−2 and a power density of 1.23 mW m−2), which is an order of magnitude higher than other O-TENGs made from polytetrafluoroethylene and polyimide. It also has a significant durability (30,000 cycles) and can power a digital thermometer for self-powered sensor applications. Further, a superhigh-sensitivity O-TENG monitoring system is successfully developed for real-time detecting particle/water contaminants in oils. The O-TENG can detect particle contaminants at least down to 0.01 wt% and water contaminants down to 100 ppm, which are much better than previous online monitoring methods (particle > 0.1 wt%; water > 1000 ppm). More interesting, the developed O-TENG can also distinguish water from other contaminants, which means the developed O-TENG has a highly water-selective performance. This work provides an ideal strategy for enhancing the output and durability of TENGs for oil–solid contact and opens new intelligent pathways for oil–solid energy harvesting and oil condition monitoring.

Coral reefs are facing a catastrophic decline due to climate-induced bleaching, threatening critical marine biodiversity. Automated, large-scale monitoring is essential; however, modern object detectors are hindered by two fundamental limitations in complex underwater scenes: a spatial reasoning deficit in their decoupled heads, which inhibits robust multi-scale feature integration, and a feature robustness deficit, which renders deterministic networks vulnerable to stochastic visual variations. To address these limitations, we propose Coral-YOLO, a novel framework for detection and forecasting. We introduce the Holistic Attention Block Head (HAB-Head), which enables deep cross-scale reasoning through explicit feature interaction, and MCAttention, a randomized training mechanism that enables the network to learn scale-invariant features with inherent robustness. Evaluated on our newly curated, multi-year CR-Mix dataset, Coral-YOLO achieves a state-of-the-art 50.3% AP (average precision at IoU threshold 0.5:0.95, following COCO metrics), representing a +1.8 percentage point improvement over the YOLOv12-m baseline, with particularly pronounced gains on small objects (+2.6 percentage points in APS). Crucially, its integrated temporal forecasting module achieves 82.7% accuracy in predicting future coral health, substantially outperforming conventional methods. Coral-YOLO sets a new performance benchmark and enables proactive reef conservation. It provides a powerful tool to identify at-risk corals long before severe bleaching becomes visually apparent.