Intelligent Inspection Research Articles

Remote Wind Farm Path Planning for Patrol Robot Based on the Hybrid Optimization Algorithm

Globally, wind power plays a leading role in the renewable energy industry. In order to ensure the normal operation of a wind farm, the staff will regularly check the equipment of the wind farm. However, manual inspection has some disadvantages, such as heavy workload, low efficiency and easy misjudgment. In order to realize automation, intelligence and high efficiency of inspection work, inspection robots are introduced into wind farms to replace manual inspections. Path planning is the prerequisite for an intelligent inspection robot to complete inspection tasks. In order to ensure that the robot can take the shortest path in the inspection process and avoid the detected obstacles at the same time, a new path-planning algorithm is proposed. The path-planning algorithm is based on the chaotic neural network and genetic algorithm. First, the chaotic neural network is used for the first step of path planning. The planning results are encoded into chromosomes to replace the individuals with the worst fitness in the genetic algorithm population. Then, according to the principle of survival of the fittest, the population is selected, hybridized, varied and guided to cyclic evolution to obtain the new path. The shortest path obtained by the algorithm can be used for the robot inspection of the wind farms in remote areas. The results show that the proposed new algorithm can generate a shorter inspection path than other algorithms.

Active Compliance Control System for Intelligent Inspection Robot in Power Room

In order to solve the problems of blind spot and time-consuming and laborious monitoring in manual inspection of power information and communication rooms, a kind of active compliant control system for intelligent inspection robots of power information and communication rooms is proposed. This research includes the noncontact detection method of the inspection robot in the power room based on machine vision; the optimization environment of the power room; the control of the inspection robot through the monitoring system terminal; the automatic visual inspection of the machine through the monitoring network transmission; the display of the monitoring terminal and the use of the calibration of the machine; and the edge detection method of the inspection image. The motion model of the inspection robot in the power room is constructed and the compliant control method of the inspection robot is designed. The experimental results show that the ineffective fluctuation range of torque of each joint is the smallest when the inspection robot is disturbed by obstacles in the process of executing signal change instructions, and the average fluctuation range of robot limbs is 0.65%. Conclusion. Based on the research results of this essay, it is proved that the controllability of power signal lamp change can be optimized separately in the future, and the safety of circuit operation can be improved.

Engineering Machinery Parts Inspection System Based on AI Intelligent Inspection Technology

Engineering Machinery Parts Inspection System Based on AI Intelligent Inspection Technology

Multi-source fusion of substation intelligent inspection robot based on knowledge graph: A overview and roadmap

As the core part of the power grid, the substation must carry out regular patrol inspection to ensure the normal operation of the equipment. In recent years, with the continuous increase of load, the area of the substation has gradually expanded, which makes its operating environment more complex, the intensity of patrol inspection has increased, and the manual patrol inspection method cannot meet the needs of patrol inspection. Therefore, it is necessary to introduce high-tech equipment and use patrol inspection robots to conduct regular patrol inspection, which inevitably leads to a large number of different working environments with matching standards and regulations for different equipment. In order to facilitate the staff to implement the delivery of robots matching the standards for the projects in the corresponding actual environment, the concept of knowledge atlas has been introduced and revolutionarily applied to the multi-source fusion of substation patrol robots, which not only more clearly analyzes the relationship between multi-source data, which can also make the operation and application in practical projects more convenient. This paper creatively uses the method of knowledge graph to summarize and analyze the multi-source fusion data of the field patrol robot in different working environments of a substation in Southwest China, and provides the research direction of this field and a one-stop manual for potential researchers and engineers in the future.

A visual and reversible nanoprobe for rapid and on-site determination of hexavalent chromium and lysine based on dual-emission carbon quantum dots coupled with smartphone.

A straightforward, largely instrument-free, smartphone-based analytical strategy for hexavalent chromium and lysine (Lys) on-site detection via exploitation of dual-emission carbon quantum dots (DECQDs) has been demonstrated. DECQDs show dual-emission peaks at 439 and 630nm with the excitation at 375nm. As a dual-mode detection probe, the fluorescence and ultraviolet adsorption spectra of DECQDs vary with hexavalent chromium concentrations. Most importantly, Lys can restore the fluorescence of the hexavalent chromium added DECQD nanoprobe and change the color of the probe under natural light. At the same time, based on the participation of smartphones, the prepared DECQD probes favor the establishment of visual smart sensors that can also be used for the in-situ detection of targets. The on-site quantitative analysis exhibited a linear range of 5.3-320μM with a detection limit of 1.6μM towards Cr(VI) and the differentiation of Lys variation from 1 to 75mM with a detection limit of 0.3mM. The probe has beenapplied for the first time to enable vision-based colorimetric in complex samples such as water, milk and egg. The recoveries of Cr(VI) and Lys in real samples were between 90 and 104%, and the relative standard deviation (RSD) was as low as 0.4%. This work offers new perspectives for fundamental understanding and new design of functional luminescent materials that areapplicable for food-safetyand rapid and intelligent inspection. A straightforward, large instrument-free, smartphone-based analytical strategy with dual-emission carbon quantum dots was developed for hexavalent chromium and Lys on-site detection via fluorescent and colorimetric twofold readout measure.

Knowledge Extraction and Quality Inspection of Chinese Petrographic Description Texts with Complex Entities and Relations Using Machine Reading and Knowledge Graph: A Preliminary Research Study

(1) Background: Geological surveying is undergoing a digital transformation process towards the adoption of intelligent methods in China. Cognitive intelligence methods, such as those based on knowledge graphs and machine reading, have made progress in many domains and also provide a technical basis for quality detection in unstructured lithographic description texts. (2) Methods: First, the named entities and the relations of the domain-specific knowledge graph of petrography were defined based on the petrographic theory. Second, research was carried out based on a manually annotated corpus of petrographic description. The extraction of N-ary and single-entity overlapping relations and the separation of complex entities are key steps in this process. Third, a petrographic knowledge graph was formulated based on prior knowledge. Finally, the consistency between knowledge triples extracted from the corpus and the petrographic knowledge graph was calculated. The 1:50,000 sheet of Fengxiangyi located in the Dabie orogenic belt was selected for the empirical research. (3) Results: Using machine reading and the knowledge graph, petrographic knowledge can be extracted and the knowledge consistency calculation can quickly detect description errors about textures, structures and mineral components in petrographic description. (4) Conclusions: The proposed framework can be used to realise the intelligent inspection of petrographic knowledge with complex entities and relations and to improve the quality of petrographic description texts effectively.

Substation Equipment Temperature Prediction Method considering Local Spatiotemporal Relationship

Temperature prediction of substation equipment is one of the important means for intelligent inspection of substation equipment. However, there are still three challenges: (1) Limited extracted samples; (2) Typical nonlinearity, seasonality, and periodicity; (3) Changes in equipment and working conditions. To solve the problems above, a substation equipment temperature prediction method considering Spatio-temporal relationship (SETPM-CLSTR) is proposed. First, according to the time series of equipment temperature from two aspects of temporal and spatial, it is determined that the equipment temperature has seasonal, temporal, and spatial correlation; second, aiming at the problem that the spatial location correlation cannot be described quantitatively, grey relational analysis (GRA) is adopted to determine the spatial location monitoring points closely related to the prediction target; then, the daily maximum temperature and daily minimum temperature from the environment, the predicted target temperature from the past several times in time and the temperature from the spatial location monitoring point with close correlation in space are constructed as Spatio-temporal feature vectors; finally, CNN-BiLSTM double-layer depth network model is proposed to predict the equipment temperature. SETPM-CLSTR has applied to temperature prediction of phase A contact from primary equipment of a substation in Taizhou City, Zhejiang Province. Under the two prediction performance evaluation indexes of MASE and RMSE, compared with three correlation models of LSTM, BiLSTM, and CNN-LSTM from two aspects of different features and models, it is verified that SETPM-CLSTR in this study has better prediction performance.

Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition.

The intelligent inspection of ceramic decorative defects is one of the hot research at present. This work aims to improve the defect inspection automation of finished decorative ceramic workpieces. First, it introduces the multi-target detection algorithm and compares the performance of different network models on the public data set. Second, the initial images are collected on the spot. The initial pictures are easy to produce noise in actual deployment, affecting the image quality. Therefore, image preprocessing is performed for the initial images, and a median filtering method is used to calculate the denoising. Finally, the original You Only Look Once version 3 network model is realized. Based on this, the decorative ceramic-oriented Automated Surface Defect Inspection model is proposed. Then, decorative ceramic defect images are inputted for model training. The experimental conclusions are deeply studied and analyzed. The results show that the proposed decorative ceramic-oriented Automated Surface Defect Inspection model based on Deep Learning technology has good feature extraction and inspection ability. The detection accuracy is 94.90% on the test set, and the detection speed reaches 25 frames per second. Compared with the traditional manual inspection method, the proposed model greatly improves the inspection effect and can meet the on-site inspection requirements of surface defects of decorative ceramics under complex backgrounds. It is of great significance to improve the quality inspection efficiency and economic benefits of China's decorative ceramics industry.

Weld Seam Identification and Tracking of Inspection Robot Based on Deep Learning Network

The weld seams of large spherical tank equipment should be regularly inspected. Autonomous inspection robots can greatly enhance inspection efficiency and save costs. However, the accurate identification and tracking of weld seams by inspection robots remains a challenge. Based on the designed wall-climbing robot, an intelligent inspection robotic system based on deep learning is proposed to achieve the weld seam identification and tracking in this study. The inspection robot used mecanum wheels and permanent magnets to adsorb metal walls. In the weld seam identification, Mask R-CNN was used to segment the instance of weld seams. Through image processing combined with Hough transform, weld paths were extracted with a high accuracy. The robotic system efficiently completed the weld seam instance segmentation through training and learning with 2281 weld seam images. Experimental results indicated that the robotic system based on deep learning was faster and more accurate than previous methods, and the average time of identifying and calculating weld paths was about 180 ms, and the mask average precision (AP) was about 67.6%. The inspection robot could automatically track seam paths, and the maximum drift angle and offset distance were 3° and 10 mm, respectively. This intelligent weld seam identification system will greatly promote the application of inspection robots.

Doublet Metalens with Simultaneous Chromatic and Monochromatic Correction in the Mid-Infrared.

Metalenses provide a powerful paradigm for mid-infrared (MIR) imaging and detection while keeping the optical system compact. However, the design of MIR metalenses simultaneously correcting chromatic aberration and off-axis monochromatic aberration remains challenging. Here, we propose an MIR doublet metalens composed of a silicon aperture metalens and a silicon focusing metalens separated by a fused silica substrate. By performing ray-tracing optimization and particle-swarm optimization, we optimized the required phase profiles as well as the sizes and spatial distributions of silicon nanopillars of the doublet metalens. Simulation results showed that the MIR doublet metalens simultaneously achieved chromatic and off-axis monochromatic aberration reduction, realizing a continuous 400 nm bandwidth and 20° field-of-view (FOV). Thanks to its planar configuration, this metalens is suitable for integration with CMOS image sensor to achieve MIR imaging and detection, which has potential application in troubleshooting and intelligent inspection of power grids. This work may facilitate the practical application of metalens-integrated micro/nanosensors in intelligent energy.

Insulators’ Identification and Missing Defect Detection in Aerial Images Based on Cascaded YOLO Models

Insulators identification and their missing defect detection are of paramount importance for the intelligent inspection of high-voltage transmission lines. As the backgrounds are complex, some insulators may be occluded, and the missing defect of the insulator is so small that it is not easily detected from aerial images with different backgrounds. To address the above issues, in this study, a cascaded You Only Look Once (YOLO) models are mainly explored to perform insulators and their defect detection in aerial images. Firstly, the datasets used for insulators location and missing defect detection are created. Secondly, a new model is proposed to locate the position of insulators, which is improved in the feature extraction network and multisacle prediction network based on previous YOLOv3-dense model. An improved YOLOv4-tiny model is used to conduct missing defect detection on the detected insulators. And then, the proposed YOLO models are trained and tested on the built datasets, respectively. Finally, the final models are cascaded for insulators identification and their missing defect detection. The average precision of missing defect detection can reach 98.4%, which is 5.2% higher than that of faster RCNN and 10.2% higher than that of SSD. The running time of the cascaded YOLO models for missing defect detection can reach 106 frames per second. Extensive experiments demonstrate that the proposed deep learning models achieve good performance in insulator identification and its missing defect detection from the inspection of high-voltage transmission lines.

Multi-Geometric Reasoning Network for Insulator Defect Detection of Electric Transmission Lines.

To address the challenges in the unmanned system-based intelligent inspection of electric transmission line insulators, this paper proposed a multi-geometric reasoning network (MGRN) to accurately detect insulator geometric defects based on aerial images with complex backgrounds and different scales. The spatial geometric reasoning sub-module (SGR) was developed to represent the spatial location relationship of defects. The appearance geometric reasoning sub-module (AGR) and the parallel feature transformation (PFT) sub-module were adopted to obtain the appearance geometric features from the real samples. These multi-geometric features can be fused with the original visual features to identify and locate the insulator defects. The proposed solution is assessed through experiments against the existing solutions and the numerical results indicate that it can significantly improve the detection accuracy of multiple insulator defects using the aerial images.

Intelligent inspection technology for cross-country buried petroleum pipelines

Intelligent inspection technology for cross-country buried petroleum pipelines

Application of Intelligent Inspection Robot in Coal Mine Industrial Heritage Landscape: Taking Wangshiwa Coal Mine as an Example.

This study is developed to explore the role of intelligent inspection robot in the protection and utilization of coal mine industrial heritage. Based on the actual situation of the coal mine, the underground planning protection scope is analyzed. Aiming at the problems of imperfect fire early warning detection technology, management mechanism, high labor cost and low work efficiency in underground protection, the intelligent inspection robot technology is proposed to realize safety tour, underground intelligent management and early warning of underground security, fire protection facilities construction, and intelligent early warning system. This paper analyzes the key technology of intelligent inspection robot in coal mine industrial heritage protection, introduces the composition, structure and implementation method, and proposes its construction path and method. Besides, the path planning, motion obstacle avoidance and sensing detection of the robot are studied. The research shows that the intelligent inspection robot has comprehensive functions and stable performance, and can realize the scientific, intelligent and refined management of industrial heritage protection, which provides a guiding basis for the intelligent protection of coal mine industrial heritage.

Appearance Fault Diagnosis of a Transformer Based on Data Set Optimization and Heterogeneous Model Ensemble

Prosumers refer to the integration of production and consumption. Due to a large number of access to distributed power sources, electric vehicles, etc., which have a certain impact on power transformers, and increasing potential failures, transformers need to be monitored. In recent years, image recognition technology based on deep learning has been widely used in intelligent inspection image analysis. Aiming at the problem that the accuracy of appearance fault diagnosis in intelligent inspection images is limited by image quantity and quality, an image data set optimization method based on a seamless cloning algorithm and image cleaning is proposed. First, a sample generation method based on the seamless fusion algorithm is proposed, which seamlessly fuses the corroded texture of other power equipment into the transformer image to generate the rust transformer image. On this basis, an image quality evaluation and screening method based on the XGBoost algorithm is proposed to evaluate the image quality of the data set and clean the low-quality images. In addition, aiming at the limitation of a single diagnosis algorithm, an appearance diagnosis method based on heterogeneous model ensemble learning is proposed. By constructing multiple learning models and using a weighted voting strategy to fuse model outputs as final outputs, the accuracy of fault diagnosis is improved.

Design and Implementation of Ultrasonic Gas Leakage Locating System

The leakage of dangerous gas at the gas station may cause explosions and other serious accidents, threatening the safety of workers. This paper designs and implements a gas leakage locating device based on ultrasonic technique. The device can communicate with an intelligent inspection robot which alarms the gas leakage in real time and tracks the leaking sources. This paper first builds a hardware platform, and then realizes a four-channel signal based leak location algorithm by combining decision tree and neural network algorithm of smoothing pre-treatment. Finally, each software module of the gas leak location device is constructed. Testing of the locating capability of the device was completed in practical working environment.

Investigation of Intelligent Substation Inspection Robot by Using Mobile Data

Substation equipment inspection is essential for the power industry. The expansion of the smart grid scale improves the transmission capacity and enhances the likelihood of power plant facilities failure. To ensure the safety of the electric power supply, it is essential to inspect substation equipment. Metal commercial equipment can be traversed by remote inspection robots equipped with magnetic wheels. It is possible to use robots like this to examine equipment and pipelines remotely. In many cases, these gadgets are able to scale vertical surfaces and even traverse obstacles with a variety of shapes. Finally, researchers in the field of robotics have indicated that challenges such as restricted onboard battery capacity, undependable line fault detection, electrical insulation, power mechanism, and advanced control techniques for outer wind disruption are highly promising research areas. To build an unmanned, intelligent, and succeeded substation, the substation progressively implements inspection robots instead of physical exertion. Hence, in this paper, the mobile-based Intelligent Tracking Framework (MITF) has been proposed using inspection robots. This inspection robot is autonomous and can be used for various tracking tools: visual, infrared, and partial charge–discharge camera. The robot is integrated with a camera and thermal infrared imager sensors that have been collectively designated as workload. These inspection sensors are used to detect environmental parameters such as reading meters, evaluation thermoelectric temperature. The accurate localization of working loads and the inspection robot electromagnetic interference within substations have been resolved. This mobile robot delivers innovative monitoring and precise detection for the unmanned substation and smart substation. The suggested approach’s effectiveness is verified through experiment results based on the electrical equipment of the substation. The experimental outcome of the proposed method boosts the Meter Reading Analysis (94.19%), Transmission Capacity Analysis (98.5%), Workload Analysis (98.9%), Temperature Analysis (97.6%), and Safety Analysis (95.41%).

MSFT-YOLO: Improved YOLOv5 Based on Transformer for Detecting Defects of Steel Surface.

With the development of artificial intelligence technology and the popularity of intelligent production projects, intelligent inspection systems have gradually become a hot topic in the industrial field. As a fundamental problem in the field of computer vision, how to achieve object detection in the industry while taking into account the accuracy and real-time detection is an important challenge in the development of intelligent detection systems. The detection of defects on steel surfaces is an important application of object detection in the industry. Correct and fast detection of surface defects can greatly improve productivity and product quality. To this end, this paper introduces the MSFT-YOLO model, which is improved based on the one-stage detector. The MSFT-YOLO model is proposed for the industrial scenario in which the image background interference is great, the defect category is easily confused, the defect scale changes a great deal, and the detection results of small defects are poor. By adding the TRANS module, which is designed based on Transformer, to the backbone and detection headers, the features can be combined with global information. The fusion of features at different scales by combining multi-scale feature fusion structures enhances the dynamic adjustment of the detector to objects at different scales. To further improve the performance of MSFT-YOLO, we also introduce plenty of effective strategies, such as data augmentation and multi-step training methods. The test results on the NEU-DET dataset show that MSPF-YOLO can achieve real-time detection, and the average detection accuracy of MSFT-YOLO is 75.2, improving about 7% compared to the baseline model (YOLOv5) and 18% compared to Faster R-CNN, which is advantageous and inspiring.

Condition Assessment of the Cable Trench Based on an Intelligent Inspection Robot

Accurate evaluation of the cable trench condition is the key to realizing its intelligent operation and maintenance, and the intelligent cable trench inspection robot is a reliable information acquisition method for the condition assessment of cable trench. According to task requirements and site’s environment, an intelligent inspection robot is designed. The crawler-type motion mechanism is determined based on the demand for sizes and obstacle-surmounting. Cartographer SLAM technology is used to map the underground cable trench environment, and a path planning method combined with the improved A∗ algorithm and dynamic window method (DWA) is presented. Multiple sensors are equipped on this robot, and various kinds of information are obtained. Based on the information obtained, a condition assessment method of an underground cable trench is proposed. An extension neural network model is constructed to assess air quality. Channel capacity and ground flatness are calculated to evaluate the internal structure of the cable trench. The water depth and water surface area are comprehensively used to evaluate the hazard of water accumulation. The evaluation results are used to realize the linkage control of the cable trench’s operation and maintenance. A field test shows that the intelligent inspection robot can reliably obtain seven kinds of information from the underground cable trench, and the proposed condition evaluation method can assess the condition of the cable trench and provide four kinds of suggestions for potential hazards.

Vision-Based Power Line Segmentation With an Attention Fusion Network

Automatic power transmission line detection plays the key role in smart grid which has been widely applied into the path planning and navigation of intelligent inspection platforms, such as Unmanned Aerial Vehicles (UAVs), climbing robots, hybrid inspection robots, etc. Nevertheless, the power lines are always against complex background environment and different illumination conditions. And the power lines occupy a minimal portion image pixels in the aerial images compared with backgrounds which causes the foreground-background class imbalance issue. Therefore, robust and accurate vision-based power line detection still faces a certain challenge. Recently, deep learning has got fast development on pixel-level object segmentation due to strong contextual feature expression ability, especially U-shape network (U-Net) and its variants. However, it still exists a certain shortcomings owing to insufficient process of local contextual features to affect the segmentation precision. Meanwhile, multiple pooling operations in deep convolutional neural networks (DCNNs) also will cause the information loss. To address these issues, with the encoder-decoder architecture, a novel vision-based power line detection network is proposed in this paper to construct an end-to-end detection scheme of power lines from aerial images. To make the segmentation network capture the global contexts and emphasize target regions of power transmission lines, an attention block is proposed to be embedded into the proposed power line detection network to address the class imbalance issue. Meanwhile, faced with the insufficient process of local contextual feature maps of DCNNs, an attention fusion block is proposed for multi-scale feature fusion to acquire more rich information and improve the segmentation precision. Experiments on power lines show that the proposed power line detection network shows a good segmentation performance on real power line environment compared with other advanced detection methods.