Inspection Equipment Research Articles

A review of indoor-orbital electrical inspection robots in substations

PurposeThe purpose of this study is to provide an in-depth understanding about the indoor-orbital electrical inspection robot, which is useful for motivating the further investigation on the inspection of electrical equipment. Currently, electric energy has a strong correlation with the economic development of the country. Intelligent substations play an important role in the transmission and distribution of the electricity; the maintenance of the substation has attracted intensive attention due to the requirement of reliability and safety. The indoor-orbital electrical inspection robot has increasingly become the main tool to realize the unmanned. Hence, a systematic review is conducted systematically reviewing the current technical status of the indoor-orbital electrical inspection robot and discuss the existed problems.Design/methodology/approachIn this paper, the most essential achievements in the field of indoor-orbital electrical inspection robots were reported to present the current status, and the mechanical structures and key inspective technologies were also discussed.FindingsFour recommendations are provided from the analyzed review, which have made constructive comments on the overall structural design, functionality, intelligence and future development direction of the indoor-orbital electrical inspection robot, respectively.Originality/valueTo the best of the authors’ knowledge, this is the first systematic review study on indoor-orbital electrical inspection robots; it fills the theoretical gap and proffers design ideas and directions for the development of the indoor-orbital electrical inspection robot.

A Reliability Improvement Technology of Transformer Internal Inspection Robot and Its Control System

The oil-immersed transformer internal inspection robot has played a good role in the inspection of large power transformers, effectively reducing the working intensity of staff and improving the automation level of power equipment inspection. However, in practical work, this kind of internal inspection robot often has various quality problems and a high failure rate. Aiming at the various complex environmental stresses faced by the internal detection robot, this paper analyzes the performance failure mechanism and completes the research on the reliability improvement method of the internal inspection robot and its control system, which effectively improves the reliability of the internal inspection manipulator.

Research on behaviour planning for power substation inspection robot based on fuzzy cognitive map

A fuzzy cognitive map based behavior planning method for mobile robot is proposed in this paper. Aiming at the intelligentialize advanced application problems of power substation equipment inspection robot, a logical structure of inspection tasks planning based on hierarchical hybrid complex system is presented, and the operating principle of task decomposition and task planning are given in detail. The dependent events in inspecting tasks are considered as concept nodes to present the connection weights between nodes and establish the fuzzy cognitive map model of task and behavior planning, so as to realize task decomposition and generate the action logic sequences. Meanwhile, the action sequences can be adjusted in real time with the sensor information. The proposed method is simple without introducing complicated calculations and much training. Simulation results verify the feasibility and effectiveness.

Fault Analysis and Treatment of GE Brivo MR355 1.5 T Magnetic Resonance Gradient System and Cooling System

The GE Brivo MR355 1.5 T magnetic resonance system is difficult to maintain due to its complex structure and principle. Clinical engineers should be familiar with the basic structure and operation principle of the equipment. After the failure of the equipment occurs, they should first check the error information, combine the fault phenomenon and maintenance manual, clear their thoughts and carefully analyze the possible causes of the failure, and adopt the method of step-by-step elimination, principle analysis and exchange of the same principle. After replacing the chip tantalum capacitor and sumitomo cold head of the same model, the equipment resumed normal operation. As a clinical engineer, regular equipment inspection and preventive maintenance should be done for large equipment, which is conducive to reducing the equipment failure rate.

Predicting Residual Astigmatism in Cataract Surgery.

The purpose of this review is to evaluate the prediction of postoperative residual astigmatism and to determine the best prediction method for astigmatism correction. In recent findings for residual astigmatism in non-toric monofocal intraocular lens (IOL) implanted eyes, vector analysis can be used to correctly evaluate residual astigmatism by decomposing it. In predicting residual astigmatism, the with-the-rule (WTR) and against-the-rule (ATR) astigmatism components can now be almost predicted. This may be due to advances in inspection equipment and surgical technique. However, there are still issues with the oblique astigmatism component. In addition, corneal astigmatism is the most important predictor of postoperative residual astigmatism, and other predictors, such as refractive astigmatism, age, and lens thickness, have also been mentioned. However, all but corneal astigmatism are questionable because of the possibility of confounding variables. Total corneal astigmatism is more accurate in predicting residual astigmatism than anterior corneal astigmatism. Several predictions of residual astigmatism have been reported, but complete prediction has not been possible. Further research is needed, especially in predicting oblique astigmatism. However, I emphasize that the accuracy of predicting WTR and ATR astigmatism has improved considerably and can be predicted using regression equations with total corneal astigmatism.

Active and passive mixed millimeter wave imaging target recognition method based on multi‐feature fusion

Due to millimeter-wave (MMW) has a strong ability to penetrate clothing, MMW holographic imaging technology can conduct a non-contact inspection of the human body's surface. Therefore, it is of great significance to study the security inspection equipment and target recognition technology based on millimeter wave imaging. In this paper, an active and passive hybrid millimeter wave imaging target recognition method based on multi-feature fusion is proposed, which improves the ability of the imaging system to identify different dangerous targets. First, active and passive millimeter wave imaging techniques are discussed, the reconstruction algorithm of active millimeter wave holographic imaging is derived in detail, and the measured optical photos and data of active and passive imaging are obtained. Secondly, the image preprocessing technology applied to active and passive millimeter wave imaging is studied, which can effectively highlight the target, eliminate background interference, and clearly describe the target contour. Then the methods of image feature extraction, data feature extraction and multi-feature fusion are studied. On this basis, a multi-feature fusion method based on weighted series fusion is proposed to obtain the fusion feature vector form of active and passive MMW imaging. Finally, this paper proposed the target recognition method applied to millimeter wave imaging, and concludes that the fusion feature vector is better than the original feature vector, which provides an idea for the fusion of active and passive millimeter wave imaging at the feature level. It also provides a theoretical basis for the application of security equipment.

Designing and Investigating a Nondestructive Magnetic Flux Leakage Detection System for Quantitatively Identifying Wire Defects

In-haul cables must be examined and maintained regularly to ensure the smooth operation of cable-stayed bridges. The magnetic flux leakage (MFL) detection method is being applied increasingly commonly to inspect defects. For the quantitative identification of the broken wire, we designed an MFL inspection equipment and investigated the performance. A trapezoidal magnetic dipole model is constructed to simulate the leakage distribution, and the axial and circumferential excitation schemes are used to obtain the intensity of magnetic induction. Then, based on the simulation of model parameters, an MFL detection system is configured for detecting the damage in the broken wire. To investigate the adaptability of the developed system, a rectangular defect is inspected in the vertical and spiral climbing modes. The signal acquired under different paths is processed by the median filtering and wavelet transform methods and analyzed. A random forest algorithm is used to quantitatively identify the number of broken wires, defect dimensions, and cross-sectional area loss to verify the proposed method. The results show that the axial excitation generates a single peak signal, which offers better detection of the wire defects. In the vertical climbing mode, the maximum detection errors in the width and cross-sectional area loss are 0.64 mm and 0.46%, respectively, while the values are 0.21 mm and 0.1% in the spiral climbing mode, indicating that the latter mode offers higher identification accuracy. Furthermore, in future work, the detection capability of irregular defects should be studied, expanding its application in health monitoring.

Compensation for Vanadium Oxide Temperature with Stereo Vision on Long-Wave Infrared Light Measurement.

In this paper, using automated optical inspection equipment and a thermal imager, the position and the temperature of the heat source or measured object can effectively be grasped. The high-resolution depth camera is with the stereo vision distance measurement and the low-resolution thermal imager is with the long-wave infrared measurement. Based on Planck's black body radiation law and Stefan-Boltzmann law, the binocular stereo calibration of the two cameras was calculated. In order to improve the measured temperature error at different distances, equipped with Intel Real Sense Depth Camera D435, a compensator is proposed to ensure that the measured temperature of the heat source is correct and accurate. From the results, it can be clearly seen that the actual measured temperature at each distance is proportional to the temperature of the thermal image vanadium oxide, while the actual measured temperature is inversely proportional to the distance of the test object. By the proposed compensation function, the compensation temperature at varying vanadium oxide temperatures can be obtained. The errors between the average temperature at each distance and the constant temperature of the test object at 39 °C are all less than 0.1%.

Shape from Shading-Based Study of Silica Fusion Characterization Problems

With the rapid development of AI (artificial intelligence) in recent years, AI has been widely used in the image processing of iron tailings. The main component of iron tailings is silica, which is the most difficult part of the iron tailing composition to fuse. Therefore, the melting behavior of iron tailings can be characterized by the melting behavior of silica. First of all, in the actual production process, the temperature of the high-temperature furnace exceeds 1500 °C, which leads to a short service life of the conventional testing equipment. A line array CCD (charge coupled device) camera system with amplification effect was used to acquire data on silica in a high-temperature melt pool in a non-contact manner. The corundum crucible position is fixed, which can be used to establish a two-dimensional coordinate system to reproduce the object’s motion pattern to solve the problem of the short service life of conventional inspection equipment. The color and wobble problems generated by the Rift Valley CCD system when taking pictures need to be corrected for accurate image processing. Secondly, the change in the center-of-mass position of silica during melting was studied using the temporal images of silica in a high-temperature melt pool, and the edge contour features were extracted from the silica images and filled inside the contours, from which the mass, area and perimeter were obtained. Finally, the volume change of silica in the time series image is calculated after eliminating the effect of background factors, and the 2D image is constructed into a 3D image. We built a SFS (shape from shading) model according to the lighting conditions, and the actual melting rate of silica is calculated accordingly. According to the model calculation results, it can be seen that with the change in time, silica surface area under heat gradually decreases, then the silica melting rate gradually decreases; among them, the faster melting rate is about 0.007 cubic millimeter/second, and the slower melting rate is about 0.0015 cubic millimeter/second. Data support was provided for the blast furnace slag direct fiber formation process. The introduction of the algorithmic model into the actual production process has certain advantages. Therefore, the SFS model established in this paper has some practical value and is worth promoting in related enterprises.

Railway Defect Detection Method: A Review

The railway is indeed one of the main transportations means in the world. However, with the rapid development and advancement of the railway industries, more railways accidents occur mainly due to its defects which result in economic losses. Traditionally, the railway defect detections process which is deems to be dirty, difficult and dangerous are done manually by the railway maintenance workers. In the recent years, many sophisticated equipment such as portable detectors, track inspection trolleys, track comprehensive inspection vehicles, etc had been developed. This article outlines two main mode of inspection namely static and dynamic inspection, which are commonly used in the railway defect detection and maintenance work. Furthermore, the railway inspection equipment used by the major countries are summarized and the impact on railway inspection based on deep learning and artificial intelligence are appropriately predicted.

On-Line Temperature Monitoring System of Electrical Equipment Based on Passive Wireless Sensor

In order to solve the temperature measurement problem of fully enclosed equipment for power equipment operation inspection, the authors propose an online temperature monitoring system for electrical equipment based on passive wireless sensors. Aiming at the disadvantages of traditional temperature monitoring methods, the system adopts radio frequency technology. The scheme mainly expounds the key technologies and switchgear implementation in the monitoring system research and discusses the feasibility of the system scheme through experiments. Monitor all important components at different times, and establish a temperature monitoring network to complete monitoring and management. According to the test results, in the case of overheating, the temperature can rise to more than 75°C, and the temperature difference measured wirelessly is 0.5°C, which is sufficient to indicate the health of the change. Electrical equipment, the accuracy, and real-time performance of temperature monitoring are guaranteed.

34.3: High Resolution and High Speed Inspection Equipment for Mini‐LED Substrates

A new mini LED substrate inspection equipment using a non‐connected high‐precision proximity capacitance image sensor system named as “EPIS” (Electrical Picture Inspection System) with one‐unit pixel size of 12μm, the number of pixels of 1080 x 1080 and capacitance detection accuracy of 0.1aF has been developed. In this paper, detection results of a mini LED substrate using EPIS and the equipment are discussed.

A classification model of power operation inspection defect texts based on graph convolutional network

Aiming at the problems of diversification, complexity and islanding of power operation and inspection data and the high dependence of operation and inspection operations on expert experience and normative information, the key technology research of intelligent judgment of defect types of power operation inspection equipment is carried out. For the field of power operation and inspection, the defect text classification algorithm based on graph convolutional neural network is proposed. And the practical tests in a large defect text network diagram built by main transformer defect reports are performed. And the proposed model achieves better classification results than 7 benchmark models in the defect text classification task. Specifically, the Accuracy, Weighed-Precision, and Weighed-F1 indicators reach 73.39, 72.42, and 72.21 respectively, which improves the model’s ability to identify defect types to a greater extent and plays an important role in improving the intelligence and digitalization of power operation and inspection work.

Current Situation and Development Trend of Daily Workshop Equipment Inspection in Mechanical Enterprises

Current Situation and Development Trend of Daily Workshop Equipment Inspection in Mechanical Enterprises

Simulation Design of a Live Working Manipulator for Patrol Inspection in Power Grid

The distribution line network is the electric power infrastructure directly facing the users, with the characteristics of large coverage and complex network, and its operation safety is directly related to the stability and reliability of the power supply system, which is the key link to ensure the safety of power supply and the reliability of residential electricity consumption. In order to realize the autonomous obstacle avoidance and autonomous navigation of the live working manipulator for inspection and maintenance of the power grid equipment, a mobile manipulator intelligent control method combining SumTree-weighted sampling and deep deterministic policy gradient (DDPG) is proposed. Firstly, the traditional DDPG algorithm is improved to optimize the action value function of Q-learning to get a better control strategy, and the weighted sampling technique is used to add priority to each sample in the replay buffer, which improves the learning speed and accelerates the convergence speed. The corresponding environmental state space is designed, and simulation experiments are conducted to verify the proposed manipulator control method. Simulation results demonstrate that the proposed method performs better than traditional DDPG and DQN methods in obstacle avoidance and navigation tasks, with faster convergence, better path planning ability, and lower offset cost, which can provide theoretical and technical references for realizing fully autonomous power grid inspection operations.

Research on Equipment Inspection and Emergency Repair Mode of Urban Rail Transit

Research on Equipment Inspection and Emergency Repair Mode of Urban Rail Transit

Variational Wavelet Ensemble Empirical (VWEE) Denoising Method for Electromagnetic Ultrasonic Signal in High-Temperature Environment with Low-Voltage Excitation

Low excitation voltage for an electromagnetic acoustic transducer (EMAT) is necessary for the petrochemical equipment and facilities inspection, which work at high-temperatures, to avoid potential explosion. However, low excitation voltage causes low signal-to-noise ratio (SNR) signals that are difficult to extract features, especially in a high-temperature environment, which causes high noise. In this study, a denoising method called the variational wavelet ensemble empirical (VWEE) method was proposed by combining the advantages of the variational modal decomposition (VMD), wavelet threshold (WT) denoising, and ensemble empirical mode decomposition (EEMD) methods. To validate the VWEE method, EMAT signals obtained in the temperature range of 25 to 700 °C were analyzed. The results show that, compared with VMD, WT and empirical mode decomposition denoising methods, the SNR of proposed method is improved more than two times. The VWEE method dramatically improved the SNR of a high-temperature EMAT signal and enhanced the accuracy of defect echos extraction.

Audit and Accreditation of ART Facilities in Japan; JISART’s Commitment Focusing on Quality Management Systems (QMS) and Patient Satisfaction

In Japan in 2018, 247,402 cycles of oocyte retrieval were performed at 592 facilities and 56,979 ART babies were born. 1 in 16 new births were as a result of ART. There is presently no law regulating ART and each facility operates under a system of self-regulation based on the requirements of the Japan Society of Obstetrics and Gynaecology. In 2000, systems for staffing, quality and safety management were not clearly structured. JISART (The Japanese Institution for Standardizing Assisted Reproductive Technology) was set up in 2003 with a view to improving quality of treatment and patient satisfaction. It is comprised of 31 private clinics with over 500 annual oocyte retrievals and all members are ISO9001 certified. With the Reproductive Technology Accreditation Committee playing a central role, a Code of Practice, guidelines for quality and safety management in ART, and audit checklists have been created. Specialist physicians, embryologists, nurses, administration staff and a patient support group conduct audits triennially, covering some 245 points. With respect to physicians, the audits look at whether there are systems in place for patient examinations and professional development, and at treatment outcomes and risk management. For embryologists and nurses, they examine qualifications, knowledge and technique along with records of equipment inspections and consent forms. Further, patients are interviewed to ascertain their satisfaction with and suggested areas for improvement to facilities and personnel. In Japan, the situation regarding ART is undergoing great changes, with audits having changed from on-site to online due to the COVID-19 pandemic and the government considering whether ART should be covered by national health insurance. In the conference, I will talk about the current situation regarding audits and suggest issues for further consideration.

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.

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.