Inspection Operations Research Articles

The Method for Storing a Seabed Photo Map of the During Surveys Conducted by an Autonomous Underwater Vehicle

The paper introduces a novel method for creating a photographic map of the seabed using images captured by the on-board photo and video systems of autonomous underwater vehicles (AUVs) during various missions, while incorporating navigation parameters. Additionally, it presents a new approach for storing this photo map on the on-board device in a mosaic format (tiles), which significantly accelerates operational visual inspection by enabling the automatic search and recognition of underwater objects that may exceed the coverage area of a single photograph. This capability is achieved by organizing the photo map into layers with varying zoom levels. Semi-natural experiments were conducted with data from actual missions using the real underwater vehicle demonstrate the high efficiency of the proposed method and algorithm. Unlike existing methods that form photo maps after the underwater vehicle has taken pictures of the bottom using special high-performance computers, the developed method forms a photo map directly during the movement of the vehicle, using only the computing power of the on-board computer. In addition, in the event of accidents, when it is necessary to detect objects of interest on the seabed as quickly as possible, it is necessary to provide a quick visual inspection of the generated photo map. For this purpose, we have developed an algorithm for saving a photo map in the form of a mosaic, which is widely used in interactive geographic maps, such as Google Maps. This algorithm differs from existing methods in that it selectively saves data to the on-board storage device to reduce the number of read and write operations, thus ensuring the timely operation of the entire process of creating a photo map at a given frequency of photography. After the generated map has been stored as a mosaic and a high-speed connection with the vehicle has appeared, the operator can immediately view the entire generated map using a regular web browser.

Definition of particle visibility threshold in parenteral drug products - towards standardization of visual inspection operator qualification.

The detectability size threshold of visible particles (″visibility″ size) in the context of visual inspection of parenteral drug products has been an elusive target for several decades. The current common sense, also reflected in official guidelines, dictates that particles of different shapes and morphologies have different ″visibility″ size thresholds, that can range between hundreds and thousands of micrometers. This study demonstrates experimentally for the first time that it is possible to define a single, shape- and morphology- independent detectability size threshold, identical across particles of various types, provided that observation conditions and product attributes are kept constant. We propose that, based on the physiology of human visual perception, instead of single-dimension measures of particle size (e.g. diameter or length), such a single size-threshold requires the use of area-based size parameters (such as ″equivalent circular diameter″, or ECD. The experimental results reported here clearly demonstrate that the ″visibility″ thresholds for particles of various morphologies converge on a single ECD value. In addition, the data reported here show that product attributes, such as container configuration, fill volume etc. influence the threshold of visibility. Collectively, the findings reported in this paper provide substantial evidence and scientific rationale, as well as unanticipated prospects for standardization of visual inspection qualification practices, ultimately leading to improving pharmaceutical product quality.

An IoT-based GNSS platform for infrastructure monitoring

Abstract. In recent years, mining operators have adopted Information and Communications Technology (ICT) for monitoring and inspection operations but archaic/manual processes to collect and analyse data are still in use, thus making the protection of mining critical assets a costly and highly complex problem, reducing the capabilities of the monitoring system to trigger automatic alarms. Within the SEC4TD project, a low-cost IoT-based GNSS positioning device is developed, with the aim to provide accurate absolute positions of infrastructures. The solution includes the use of a two bands (L1, L2) GNSS receiver, long power autonomy and RTK correction on edge nodes. The paper describes the first prototype and the initial testing to evaluate its positional precision. A long term evaluation over 15 days is performed including a comparison with a high-end receiver. Lastly, field conditions are mimicked by reducing to 60 the number of epochs used in estimating the position each hour allowing for continuous monitoring of the infrastructure of interest.

Systematic review on benzene, toluene, ethylbenzene, and xylene (BTEX) emissions; health impact assessment; and detection techniques in oil and natural gas operations.

The ONG industry emits VOC such as BTEX, which pose health risks to workers. This study analyzed peer-reviewed research articles to provide BTEX emission profiles from three primary ONG operations and their associated health risks. PRISMA (Preferred Reporting Items for Systematic Reviews) was used to choose relevant articles for this review paper. The analysis revealed that in ONG operation, upstream operations involving gas flaring (benzene: 0.115 ± 0.1 ppmv, toluene: 0.029 ± 0.001 ppmv, ethylbenzene: 0.002 ± 0.001 ppmv, xylene: 0.123 ± 0.001 ppmv) contributed to relatively lower concentration of BTEX emission. Meanwhile, midstream operation involving tanker loading (benzene: 5.391 ± 28.670 ppmv, toluene:10.376 ± 48.929 ppmv, ethylbenzene:1.583 ± 6.563 ppmv, xylene:2.067 ± 9.211 ppmv) contributed to significant BTEX emission. On the other hand, downstream operations involving refinery operation zone (benzene: 3.5 ± 1.69 ppmv, toluene: 4 ± 0.87 ppmv, ethylbenzene: 1.2 ± 0.24 ppmv, xylene: 6.6 ± 1.34 ppmv) and refueling station (benzene: 1.164 ± 0.408 ppmv, toluene: 2.394 ± 1.086 ppmv, ethylbenzene: 1.301 ± 0.779 ppmv, xylene: 1.736 ± 0.898 ppmv) exhibited higher BTEX emissions. The Lifetime Cancer Risk (LCRi) for benzene was greater than 10-6 near gasoline pump stations (1400 × 10-6) and during loading operations (160 × 10-6). Ethylbenzene also had a significant LCRi value of 1000 × 10-6 during loading operations. Other ONG activities like gas flaring, inspection operations, and gasoline station pumps have hazard ratio value of > 1. The study highlights BTEX emissions in all three ONG sectors, with significant contributions from midstream tanker loading and downstream refinery and refueling stations. E-nose techniques are promising for BTEX detection due to their real-time measurement capabilities and ease of use. Some Asian countries have reported benzene concentrations exceeding permissible limits during tanker loading and refueling operations. Overall, BTEX emissions are a cause for concern and should be addressed in ONG operations.

Assessing cross-docking performance using a novel network DEA model regarding sustainability and undesirable factors under uncertainty

Although evaluating warehouses' efficiency using different approaches has been gaining prestige in recent years, this study is the first attempt to assess the efficiency of cross-docking systems. For this purpose, a comprehensive structure of a cross-docking system is examined, where a broad range of components affecting its performance are considered, including inbound and outbound doors, various means of transportation, inspection, kitting, storage, retrieval, and staging operations. In addition, an extensive set of key performance indicators (KPIs) are introduced for each component regarding automation, digitization, resiliency, sustainability, and lean concerns. More importantly, a novel network data envelopment analysis model is proposed to evaluate the efficiency of the cross-docking system with respect to undesirable factors. Furthermore, a new hybrid uncertainty approach is offered to handle the uncertainties in introduced KPIs and quantify some qualitative judgments. Finally, a case study is examined to illustrate the effectiveness of the proposed model and uncertainty approach. The obtained results reveal that the simultaneous exploitation of pre-distribution and post-distribution policies can increase the efficiency of the examined system by 36%. Also, modifying the layout to eliminate redundant transportation operations can lead to an 18% increase in efficiency.

Advancements in Machine Learning for Pipeline Integrity Management: A Comprehensive Review of Predictive and Optimization Techniques

Oil and gas pipeline networks are crucial component of energy infrastructure. However, as the integral elements of the energy transportation system are evolving towards Industry 4.0 and Smart Manufacturing, pipelines network are progressively shifting towards intelligence and digitization. The fields of asset integrity management, operation inspection, corrosion prevention, leak and intrusion detection, and flow assurance are just a few of the areas where machine learning and artificial intelligence (AI) algorithms are becoming more and more important. The predictive and optimization capabilities of these Models/ Algorithms have been leveraged by various Pipeline Network Integrity Management systems to avert pipeline failure, reduce environmental damage, and effectively allocate resources. This study reviews and gives a summary of current advancements in intelligent techniques, encompassing heuristic computations, mathematical programming, and machine learning techniques used in a pipeline network integrity management to enhance various operational and maintenance aspects. This work makes two intellectual accomplishments. Firstly, it offers a technical review of literatures, systematically synthesizing different computational intelligence and machine learning approaches previously applied, along with their methodologies, contributions, drawbacks, and comparisons. Secondly, the study recommends some nature-inspired meta-heuristic algorithms, which encompasses Evolutionary and the Swarm Intelligence Algorithms to be applied for future directions and challenges. Finally, the study underscores the potential of computational intelligence and algorithms for learning methods in predicting and optimizing the efficiency of pipeline network operation and management, emphasizing the necessity for further development of models/algorithms and application to enhance more intricate interactions between pipeline infrastructure monitoring, maintenance, and management.

Research on optimization technology of ventilation system in an industrial X-ray inspection workshop

Objective: To investigate the reasonable airflow organization and exhaust system facilities during the operation of the inspection workshop, and solve the problem of the accumulation of harmful gases such as ozone and nitrogen oxides in the workshop. Methods: In May 2023, computational fluid dynamics (CFD) technology was used to numerically simulate the diffusion of ozone and nitrogen oxides generated by industrial radiographic inspection operations, and the comparative detection method was used to analyze the ozone and nitrogen oxides concentrations before and after the renovation of the ventilation system of the inspection workshop. Results: After the renovation of ventilation system, the average concentration of ozone in the inspection workshop decreased from 0.81 mg/m(3) to 0.03 mg/m(3), and the average concentration of nitrogen oxides decreased from 0.42 mg/m(3) to 0.01 mg/m(3), and the differences were statistically significant (t=20.51, 10.38, P<0.001) . Conclusion: The ventilation facilities of the inspection workshop are set up in the airflow organization mode of sending up and down the exhaust, and the ventilation pipes are scientifically designed through the calculation of ventilation hydraulic balance, which can effectively control the concentration of harmful gases in the inspection workshop.

Pemeriksaan Operasional Atas Aktivitas Pencatatan dan Pengendalian Persediaan Cafe Owl Ways di Pekanbaru

Operational inspections have an important role to ensure that operational activities have run effectively and efficiently. Operational inspections also not only play a role in overcoming or repairing but also play a role in preventing these risks from occurring. The purpose of this study is to find out whether the policies and procedures for recording and controlling inventory implemented by Owl Ways Pekanbaru café have run effectively and efficiently. The research method used in this study is a descriptive study method. The data collection technique uses literature study data and field studies consisting of interviews, observations and company documents. The data that has been collected will then be analyzed. The object of research in this study is an operational inspection of inventory recording and control activities in an effort to improve the effectiveness and efficiency of inventory management of Owl Ways café in Pekanbaru. After an operational inspection, weaknesses were found in the Owl Ways café, namely inventory planning and control, inventory document recording procedures and the purchase of raw materials for supplies that are still inadequate

The Supervising Role of the Apron Movement Control (AMC) Unit on Aviation Security and Safety at Pattimura Ambon International Airport

Security and safety are crucial factors in aviation activities, considering the sensitivity of air transportation that requires assurance of safety at all times. The success of the AMC unit's tasks and responsibilities is measured by their ability to optimize supervision, thus creating safe and secure conditions, in accordance with applicable regulations. This study aims to evaluate the role of the AMC unit in ensuring security and safety in the airside area of Pattimura Ambon International Airport and to identify obstacles in supervision in the area. The approach used in this study is descriptive qualitative. The data collected consists of primary and secondary data. Primary data is obtained through observation and interviews, while secondary data comes from literature studies and photographs related to the problem being studied. Data analysis techniques involve data reduction, data presentation, and drawing conclusions. To ensure the validity of the data, triangulation techniques are used. The research was conducted in March 2024. The results of the study indicate that the AMC unit at Pattimura Ambon International Airport has carried out supervision well in accordance with the standard operating procedures PM-IK (Quality Procedures and Work Instructions). Supervision covers four components of the work area: Airport Operation Control Center (AOCC), Data Entry, Field Inspection, and Aviobridge Operations. Supervision is carried out through inspections every two hours for FOD and fuel spills, supervision of aircraft movements and engine running up/idle, GSE vehicle speed, and worker compliance with the use of Personal Protective Equipment. The AMC unit strives to implement the 3S+1C principle (Safety, Security, Service, and Compliance). The obstacles faced include a shortage of personnel, which causes an increase in workload and less than optimal supervision, as well as violations by service users or workers in the airside area, which have the potential to cause accidents.

Оцінка можливості виявлення тріщин під час контролю ЛЮМ1-ОВ на лопатках турбін після нанесення комплексних жаростійких покриттів

Currently, gas turbine engines (GTD) are being improved and made more powerful by increasing the temperature of the gases in front of the turbine and increasing the air compression ratio in the compressor. In the aviation industry, nickel heat-resistant alloys such as ЖС6У-ВІ, ЖС26-ВІ, ЖС32-ВІ, etc. are used to manufacture GTD blades. Despite their high properties in terms of corrosion resistance and heat resistance, the service life of GTD blades and their protection against oxidation and corrosion damage is ensured by applying coatings using various technologies both to the flowing surface of the blade blades (heat-resistant and heat-protective) and to the inner surfaces of the cooled channels (diffusion coatings). Failure of turbine blades is one of the most severe types of damage to turbine blades, so at different stages of serial production, including after applying heat-resistant coatings, LUM1-OV inspection of blades is performed to detect cracks that can form during processing and lead to their failure during engine operation. The paper evaluates the possibility of detecting cracks on GTD turbine blades with complex heat-resistant coatings GCP+SDP-2. The study was carried out on samples of working blades in the form of an experiment. First, fatigue tests of the samples were carried out on a test bench until cracks appeared. Then, a complex heat-resistant coating of GCP (CrAl gas circulation coating) + SDP-2 (heat-resistant alloy of the Ni-Cr-Al-Y system) was applied to the blade profile in stages according to the serial technology. After each coating was applied, the blades were inspected by the LUM1-OV method to determine the possibility of crack detection. The results showed that immediately after the application of the complex heat-resistant coating, cracks on the blades were not detected, because they spread only in the GCP layer and partially pass into the SDP-2 layer without reaching the surface. The results obtained can be the basis for removing the luminescent inspection operation from the technological process, immediately after applying complex heat-resistant coatings. Since this does not allow detecting cracks in the blade material and can lead to unnecessary costs for additional technological operations.

Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review

Positioning systems are integral to Unmanned Underwater Vehicle (UUV) operation, enabling precise navigation and control in complex underwater environments. This paper comprehensively reviews the key technologies employed for UUV positioning, including acoustic systems, inertial navigation, Doppler velocity logs, and GPS when near the surface. These systems are essential for seabed mapping, marine infrastructure inspection, and search and rescue operations. The review highlights recent technological advancements and examines the integration of these systems to enhance accuracy and operational efficiency. It also addresses ongoing challenges, such as communication constraints, environmental variability, and discrepancies between theoretical models and field applications. Future trends in positioning system development are discussed, with a focus on improving reliability and performance in diverse underwater conditions to support the expanding capabilities of UUVs across scientific, commercial, and rescue missions.

Seismometer Orientation Measurements of Broadband Seismic Stations in the China Digital Seismograph Network

ABSTRACT The China Digital Seismograph Network, one of the largest national seismic networks, has been operating for over four decades which provides valuable seismic data for various scientific studies. Our investigation gathered a comprehensive dataset comprising 5,456,816 three-component waveforms from 3187 seismic events that took place over nine years (2014–2022). We assessed sensor orientations at 1056 broadband stations using the P-wave polarization method. Together with our calculation results, operation and maintenance log of regional networks, on-site checking, and manual inspection, we identified and addressed issues related to temporal changes of orientation, polarity reversal, and channel mislabeling. We found that ∼65.8% of seismometers (694) were well aligned with the absolute misorientation angle ≤3°, 20.8% of seismometers (220) were fairly well aligned with the absolute misorientation angle lying between 3° and 10°, 3.6% (38) of seismometers were misaligned exceeding 10°, and 9.8% of seismometers (104) showed a temporal variation in alignment. The fairly high consistency between our numerical results and gyrocompass measurements confirms the reliability of our investigation. We further compared the results of P-wave receiver functions analysis before and after sensor orientation correction. The findings indicate that sensor misorientation angles may lead to inaccurate and unstable seismological results. Therefore, conducting a systematic assessment, diagnosis, and correction for sensor orientation would be beneficial for advancing seismological analysis by promoting consistency, efficiency, accuracy, collaboration, reproducibility, and adaptability in data processing and interpretation.

A lightweight defect detection algorithm for escalator steps

In this paper, we propose an efficient target detection algorithm, ASF-Sim-YOLO, to address issues encountered in escalator step defect detection, such as an excessive number of parameters in the detection network model, poor adaptability, and difficulties in real-time processing of video streams. Firstly, to address the characteristics of escalator step defects, we designed the ASF-Sim-P2 structure to improve the detection accuracy of small targets, such as step defects. Additionally, we incorporated the SimAM (Similarity-based Attention Mechanism) by combining SimAM with SPPF (Spatial Pyramid Pooling-Fast) to enhance the model’s ability to capture key information by assigning importance weights to each pixel. Furthermore, to address the challenge posed by the small size of step defects, we replaced the traditional CIoU (Complete-Intersection-over-Union) loss function with NWD (Normalized Wasserstein Distance), which alleviated the problem of defect missing. Finally, to meet the deployment requirements of mobile devices, we performed channel pruning on the model. The experimental results showed that the improved ASF-Sim-YOLO model achieved an average accuracy (mAP50) of 96.8% on the test data set, which was a 22.1% improvement in accuracy compared to the baseline model. Meanwhile, the computational complexity (in GFLOPS) of the model was reduced to a quarter of that of the baseline model, while the frame rate (FPS) was improved to 575.1. Compared with YOLOv3-tiny, YOLOv5s, YOLOv8s, Faster-RCNN, TOOD, RTMDET and other deep learning-based target recognition algorithms, ASF-Sim-YOLO has better detection accuracy and real-time processing capability. These results demonstrate that ASF-Sim-YOLO effectively balances lightweight design and performance improvement, making it highly suitable for real-time detection of step defects, which can meet the demands of escalator inspection operations.

Automated vision-based concrete crack measurement system

Maintaining and ensuring the safety of ageing infrastructures is becoming of uttermost importance. This is particularly critical in Europe, where several infrastructures like tunnels and bridges are approaching their end-of-life. Such a condition justifies the need for a prompt recognition of the deterioration status of these structures as well as the identification of strategies for effective inspection and monitoring operations. This paper focuses on cracks, and proposes a vision-based measurement system aimed at precisely and accurately detecting cracks on concrete surfaces and measuring their geometric features. The system mainly leverages RGB-D cameras to make the pixel to real world measurement units conversion possible (by assessing the camera to target relative distance and pose), and exploits an image processing pipeline consisting of the following steps: (a) an artificial intelligence-based semantic segmentation model for identifying cracks on the target structure; (b) a ridge detection algorithm for identifying the centre line of the cracks; (c) a geometric feature extraction algorithm to extract key metrics of the crack, like its width and length. Variance analysis of the crack mean width measurement shows a repeatability standard deviation of 0.01 mm and a reproducibility standard deviation of 0.02 mm, with a Type A expanded uncertainty of 0.004 mm. The system performance is discussed in the paper referring both to lab testing activities, targeted to the assessment of its metrological characteristics, and to a real field application, i.e. concrete inspection in a tunnel. This latter application is of particular interests as it is targeted to demonstrate the robustness of the approach in harsh environments (i.e., strong presence of dust, total absence of natural light) and on surfaces whose roughness generates important light gradients and shadows (e.g., sprayed concrete).

An iterative up-sampling convolutional neural network for glass curtain crack detection using unmanned aerial vehicles

It is inefficient, subjective, and potentially life-threatening for both inspectors and pedestrians to manually inspect glass curtain cracks of high-rise buildings. Due to the development of image instance segmentation techniques for deep learning and the operation of compact unmanned aerial vehicles (UAVs), automatic, accurate, and safe glass curtain crack inspection can be achieved. A novel approach proposes to segment glass curtain cracks efficiently and accurately, combining a convolutional neural network (CNN), the region of interest alignment (RoIAlign), and an iterative up-sampling method. The approach proposed achieves excellent performance with an accuracy = 99.89 %, precision = 91.1 %, recall = 72.1 %, F1-score = 79.7 %, and mIoU = 78.7 %. Comparative studies have been conducted to examine the performance of the proposed approach relative to Solo, Swin, Mask RCNN, and ConvNext V2. The results show that the proposed network outperforms the networks used for comparison and that it can more accurately detect and segment a variety of crack types in complex reflective environments of glass curtains.

Scene understanding method utilizing global visual and spatial interaction features for safety production

Risk identification in power operations is crucial for both personal safety and power production. Existing risk identification methods mainly use target detection models to identify the common risks but the scene specificity of risk occurrence. For example, not wearing a safety harness, not wearing insulated gloves, etc. Since most methods for detecting safety gears make sense only under specific scene. But the power electric work is a complex object involving many elements such as personnel, equipment and safety tools. Therefore, this paper proposes a scene understanding method that integrates visual features and spatial relationship features among scene elements. This method constructs a scenean undirected scene graph to represent the interactive relationship among the elements, extracts the interactive features by using a graph encoder-decoder convolution module, and fuse perceived high-dimensional visual features and spatial topological features for scene recognition, in order to effectively solve addressing the power operation scene understanding problem under multi-element interaction. Finally, a power inspection operation scenario was chosen as the test case. The outcome of the evaluation indicates results indicate that the proposed approach suggested in this study exhibits superior precision in scene identification and shows ademonstrates strong generalization ability.

Detection of fruit tree diseases in natural environments: A novel approach based on stereo camera and deep learning

The occurrence of diseases in orchards has a significant impact on fruit yield and quality. Inspection devices equipped with cameras can effectively replace manual intervention in the process of orchard management by swiftly detecting diseases. However, the images captured by such devices often exhibit a wide vision field and contain a significant amount of extraneous information. This paper presented a method for detecting diseases in natural environments based on binocular cameras and deep learning techniques applied to fruit tree leaf images with a wide visual field. Firstly, the ZED2i binocular camera was utilized to capture image pairs from a long distance, simulating the visual field of an inspection device. These image pairs were then processed using the Unimatch stereo matching algorithm to obtain a disparity map and calculated the corresponding depth map. The depth information was used to create a mask, eliminating irrelevant background information from the images. Secondly, a lightweight disease detection (LDD) model was proposed based on the advanced YOLOv5 framework for detecting pear rust and plum perforation diseases. The backbone network consisted of shuffle channel block, inverted shuffle channel block, and convolutional block attention module, with only one detection head used in the classifier part. The final experiments evaluated the segmentation, model improvement, and disease spots detection performance. The results showed that the depth map obtained using Unimatch for stereo matching was more accurate than that obtained using the ZED software development kit. In ablation experiments, LDD achieved a mean average precision of 93.0%, with a model size of only 3.9 MB, outperforming the original YOLOv5-s model. Preprocessed images with depth information exhibited improved detection performance, achieved a F1 score of 93.62%, which was a 10.92% improvement over direct detection of the original images. Overall, the presented method successfully addresses the issue of background interference when detecting diseases of fruit tree leaf with a wide visual field, providing a technical basis for automated orchard inspection operations.

Designing a digital-twin based dashboard system for a flexible assembly line

The current dashboard system is limited of coordination, flexibility and scalability. This study proposed a digital-twin based dashboard system. The digital twin components are divided into four, namely the operator, screw machine, industrial robot and automatic optical inspection in a flexible GPU card assembly line. This study designs a protocol enabling digital twins to interact and operate the system collaboratively and flexibly. In addition, this study presents a landscape of digital twin dashboards to demonstrate the relationship between the digital twins and the physical modules, providing a use case for the future digital-twin based dashboard design. The proposed system is implemented in a flexible GPU card assembly line and constructed by using JMobile to build a real-time dashboard system capable of scalability. The proposed dashboard framework for the GPU card assembly line offers several advantages over existing dashboards in terms of productivity, quality, and accident reduction. This study contributes to a scalable real-time dashboard system framework by incorporating human–machine collaboration, laying the foundation for advanced dashboard system development.

Creep-fatigue damage level evaluation based on the relationship between microstructural evolution and mechanical property degradation

Creep-fatigue interaction is identified as a primary failure mode for components operating under high temperatures. As operational durations extend, this interaction not only alters the material's microstructures but also initiates a gradual degradation in mechanical properties, significantly impacting its deformation and damage behaviors. In this work, the dynamic microstructural evolution of GH4169 superalloy during creep-fatigue was elucidated via qualitative characterization, and damage level evaluation method was subsequently developed by bridging microstructure degradation to mechanical property degradation. Creep-fatigue tests were performed at 650 °C with various tensile holding times and were interrupted at lifetime fractions of 10 %, 50 % and 80 % for further analysis and tensile evaluations. Results revealed that the prolonged exposure to holding times induced the coarsening of γ″ precipitates alongside an increase in low-angle grain boundaries, culminating a reduction in creep-fatigue strength. The development of voids and cracks exacerbated the degradation of elongation, leading to a hybrid fracture mode encompassing both intergranular and transgranular cracking paths. Synthesizing microstructural evolutions to qualitatively categorize diverse degradation levels imparted a robust physical basis for damage evaluation. A mapping model was established to correlate the average kernel average misorientation (micro-degradation indicator) with the tensile plastic strain energy density (macro-degradation indicator). The damage level evaluation method was endowed with quantitative metrics utilizing this model, and its generality was additionally validated in P92 steel. This work offers an insight into the quantitative damage evaluations of creep-fatigue-induced degradations in materials, thereby providing a theoretical basis for the development of operation management and inspection plans of components.

Numerical Simulation and Experimental Study of the Pneumo-Electric Hybrid-Driven Pipeline Inspection Robot in Low-Pressure Gas Pipeline

Intelligent pipeline inspection is necessary to operate submarine pipelines safely. At present, speed excursion and blockage are the challenges in the inspection of low-pressure gas pipelines. Accordingly, this study proposes a novel pneumo-electric hybrid-driven scheme to improve the traveling stability of inspection robots. To adapt to different working conditions, building blocks and CFD numerical simulation methods are used to study the throttling pressure control flow field of the robot. The results proved that the flow clearance had the most evident effect. The flow clearance was reduced from 30 to 5 mm, and the differential pressure of the prototype increased from 0.3 to 17 kPa. The skeleton diameter has a small effect on the differential pressure. The differential pressure increases as the gas velocity increases. By analyzing the prototype in different positions, it was found that the differential pressure of the prototype while passing the elbow decreased by 45% at 45°, which quantified the fluid-driven force gap of the prototype while passing through the elbow. Finally, by comparing the speed of prototype with that of fluid-driven pig, it is demonstrated that a pneumo-electric hybrid-driven scheme is an effective solution to the problem of unstable inspection operation of low-pressure gas pipelines.