Inspection Equipment Research Articles

Design and Application of Intelligent Unmanned Spot Inspection System in Thermal Power Plant

Thermal power plant is a typical equipment-intensive enterprise, and regular inspection and spot inspection of production equipment is the most important measure to find equipment defects in time and ensure safe production of enterprises. In many years of practical application, the traditional inspection method, which relies mainly on visual inspection by spot inspectors, has been widely used. There are many shortcomings, such as missing detection, low efficiency, insufficient depth of data mining and high security risk. Based on the equipment management mode of spot inspection and regular maintenance in power plants, this paper proposes an unmanned intelligent spot inspection system for power plants, which combines intelligent sensors with diagnostic algorithms, by sorting out the work content of traditional manual spot inspection. The intelligent and unmanned management of spot inspection and regular repair in thermal power plants is realized, which has certain reference significance for improving the operation reliability of power plants and reducing the safety risk of spot inspection personnel.

Analysis of methods for assessing the condition of surveyed facilities in Taraz City

Due to the high demand for technical inspection services for buildings and structures, new equipment designed for use in certain conditions appears on the market. On this basis, this article is devoted to the analysis of existing devices and equipment for technical inspection of buildings and structures. The analysis of equipment was carried out during the survey of 8 facilities of Novozhambyl phosphorus plant in Taraz city. In the analysis devices for non-destructive method of strength measurement were used. Based on the results obtained during the tests, the analysis was carried out. Thus, the device UKS MG4 showed the greatest deviation in the measurements when testing the reinforced concrete structures than the devices IPS MG4 and sclerometer. When controlling the reinforcement of reinforced concrete structures by electromagnetic method, the greatest accuracy of measurements showed the device Elcometer 331. The UTM-MG4 device showed a smaller error in measurements compared to a similar A1208 device. As a result of a comparative analysis of devices for determining the humidity of building materials Testo 606 showed a more reliable data, due to a different principle of operation relative to similar devices.

A Case Study of Simulation-based Feasibility Analysis of Replacement of Inspection Equipment for Switchboards

A Case Study of Simulation-based Feasibility Analysis of Replacement of Inspection Equipment for Switchboards

Effect of Equipment Component Generic Frequency Data on Probability of Failure Calculations for Risk-Based Inspection

A software was developed to use equipment component frequency data specific to the facility or provided from different sources in lost event probability calculations for risk-based inspection. Corrosion causes equipment aging and loss events in which hazardous substances are released uncontrollably. The API RP 581 Recommended Practise of the American Petroleum Institute is widely used in the calculation of corrosion-based loss event risks for static pressure equipment such as atmospheric tanks, heat exchangers, columns, reactors, and used for basis of the developed software. In API RP 581, the risk of loss event is defined as the product of the probability of failure and the severity of consequence. Equipment component generic failure frequencies are a variable at the probability of failure calculation. Current software use only equipment component generic failure frequencies given at API RP 581. For this reason, establishment-specific equipment component failure frequency data or data that can be obtained from other sources cannot be used. To solve this problem, a software based on API RP 581 methodology has been developed and provided with the opportunity for the user to enter equipment component failure frequency data from different sources. The findings showed that when using data from different literature sources, there are different results up to 1491% in the probability of failures. Since the increase in the probability of the failures will increase the risk, that creates results such as pulling the equipment inspection dates forward, performing more effective and therefore more costly inspections, increasing the precautions and costs to be taken. Therefore, software which are based on API RP 581 methodology should be developed in such a way that different generic frequency data can be used.

Exploring Empirical Rules for Construction Accident Prevention Based on Unsafe Behaviors

This paper is aimed at exploring rules for construction accident prevention based on unsafe behaviors. The literature review demonstrates a clear connection between construction accident prevention and unsafe behaviors, followed by a 2-year field investigation resulting in 2207 observations based on convenient sampling with 95% confidence and 5% limit of errors in the 50–50 category. There are 80.43% unsafe behaviors categorized into “Regulations for the Occupational Safety and Health Equipment and Measures”, where there are 66.37% of regulations and law VII violations, linking fall prevention with the most cases (94.48%) of Fall Protection and Structure Strengthening. The Apriori yields 13 association rules, where the top 3 rules show that 44.11% of the Passage and lighting category is linked to construction equipment inspections; 29.41% of the high-pressure gas category is linked to construction equipment inspections; 100% of the fire prevention category is linked to fire protection unsafe behavior. The findings clarify the association rules that can prevent workers from accidents in construction sites.

Power Inspection Design by Internet of Things and RFID Technology in Smart City

As the basic element and module unit in the construction architecture of smart city, the Internet of things (IoT) is also the key foundation and important support for realizing "automatic perception, rapid response, and scientific decision-making" of smart city. The study aims to improve the efficiency of power inspection in power enterprises and provide intelligent technology for power inspection. After the theoretical analysis of IoT and RFID technology under the background of smart city, the study discusses the information acquisition principle of RFID technology. Second, based on IoT and smart grid, the study takes the power equipment of power company A as an example, and puts forward the power inspection scheme, including the information required for inspection and the inspection of primary and secondary equipment. The specific contents are as follows: health assessment is carried out according to the real-time alarm of power equipment, and the control command is sent to the downstream control equipment to confirm the alarm information. Meantime, based on the alarm information of power equipment and related equipment, the expert analysis system is used for reasoning and analysis, and the RFID tag is used to supervise the inspectors to arrive at the site and inspect the situation according to the predetermined route. The temperature of the cable of power company A is measured under normal and hidden dangers, and the body insulation test is conducted on No. 1 main transformer. The results show that under normal operation, the cable temperature at each point is slightly different, which is basically about 40 °C. The temperature before the fourth point of the cable rises obviously, indicating that the fourth point has faulty. Over time, the more chemical molecules are detected in phase A. When phase B and phase C are tested on the same day, the difference of chemical molecular content is relatively small. It indicates that the chemical molecular content detected by chromatographic test of bulk insulating oil is normal. To sum up, smart power, as the objective need of urban intelligent development, is an important foundation of smart city. The power inspection scheme based on IoT and RFID technology proposed has high detection efficiency.

Blockchain and deep learning technologies for construction equipment security information management

Construction equipment security information management (CESIM) is a critical component of equipment security management. However, equipment-related information's authenticity and integrity cannot be guaranteed in centralized systems currently, which limits the effectiveness of CESIM. In addressing this issue, a framework that combines blockchain and deep learning technologies is proposed by providing decentralized data storage, keeping data authentic, and protecting data from tampering. In accordance with the proposed framework, a prototype system is developed and further evaluated. Results show that the prototype system can improve the efficiency of equipment supervision, decision making, and accident tracking in equipment security management. In conclusion, this study aims to place emphasis on: (1) applying blockchain technology to ensure equipment-related information authentic and secure; (2) developing word2vec + CNN-based deep learning to automatically identify keywords and categories of equipment inspection reports; and (3) verifying the feasibility of the proposed framework and developing a prototype system for further application

Design and Application of Intelligent Patrol System in Substation

With the continuous development and improvement of power grid technology, intelligence has become the development goal of power grid technology. By designing an intelligent substation inspection system, simplifies the operational tasks of inspectors, and makes inspection of power equipment more accurate, efficient, and traceable. In the implementation of the system, the mobile terminal is responsible for the collection, input, and upload of inspection data, and the server completes the data reception and statistics and displays it visually. And the application of the case proves that the intelligent patrol system is convenient and quick to operate, the social and economic benefits produced are significant, and it has strong practicability.

Research and Design of Remote Online Supervision System of Coal Mine Electromechanical Equipment

Increasing the supervision of coal mine electromechanical equipment is an urgent need to ensure the safe production of coal mines. This paper designs and develops a coal mine electromechanical equipment supervision system, which includes four parts: mine-side monitoring subsystem, mine-side data acquisition and transmission module, local-side data analysis and storage module, and coal mine electromechanical equipment supervision platform. It realizes coal mine ventilator, drainage pump, air compressors, hoists and other major electromechanical equipment online monitoring and equipment failure analysis. The system satisfies the remote online inspection and management of equipment by multi-level departments, reduces the possibility of enterprise accidents, innovates the supervision mode, and improves the efficiency of supervision.

Analysis of the possibility of reducing spare parts in the maintenance context 4.0

Equipment inspection arose from the need to maintain industrial facilities in satisfactory physical condition, providing the minimum level of safety and reliability in operation and maintenance. Machines and equipment cannot stop operating without planning and managing the supply chain, maintaining large quantities of spare parts stocks. The objective of this work is to analyze the possibility of predictive and / or prescriptive monitoring to contribute to the preventive maintenance of the real condition of the equipment, that is, only in real need, avoiding preventive maintenance for pre-established intervals, consequently reducing the stock of spare parts. To this end, a Systematic Literature Review (RSL) was carried out using the keywords Maintenance 4.0 and Supply Chain Management and Industrial Logistics, identifying the main authors and most relevant journals.

Safety first: The consequence of tourists' recreation safety climate

Safety is among the most critical prerequisites for outdoor recreation and tourism development. This study expands the framework of recreation safety climate (RSC) in outdoor recreation by examining the relationship between RSC and its outcomes, including situational involvement, recreation satisfaction, place dependence, place identity, and destination loyalty. A self-administered questionnaire was designed to collect empirical data from tourists engaged in surfing in Taiwan. Based on a structural equation modeling approach, our findings indicate that tourist perceptions of RSC positively affected their situational involvement, recreation satisfaction, place dependence, place identity, and destination loyalty. This study can help recreation service managers to realize the importance of RSC. Such managers should adapt RSC indicators to compensate for any lack of safety measures in their facilities. RSC can improve the degree of involvement and satisfaction of tourists in terms of their activities, dependence, identity, and loyalty to the place of recreation. This study provides several management implications for safety strategies to improve the tourist experience. RSC is a critical component of recreational activity participation. Based on the importance of RSC in this study's framework, we propose the following suggestions for the reference of recreational activity managers. • Establish standard operation procedures for safety equipment inspection so that tourists can use equipment that meets the relevant safety standards for their activities. • Establish clear rules and regulations regarding safety so that tourists have guidelines to follow. • Design safety training processes for tourists to improve their capabilities and commitment regarding safety matters, so that they enjoy the recreation facilities and have fun while surfing.

Target-Aware Deep Feature Compression for Power Intelligent Inspection Tracking

Deep learning has brought revolutionary progress to computer vision, so intelligent inspection equipment based on computer vision has developed rapidly. However, due to the large number of existing deep features, it is difficult to deploy it on mobile devices to achieve real-time tracking speed. This paper presents a target-aware deep feature compression for power intelligent inspection tracking. First, a negative balance loss function is designed to mine channel features suitable for the current inspection scene by shrinking the contribution of pure background negative samples and enhancing the impact of difficult negative samples. Based on this, the deep feature compression model is combined with Siamese tracking framework to achieve real-time and robust tracking. Finally, we evaluate the proposed method on real application scenarios and general data to prove the practicability of the proposed method.

Asynchronous parallel disassembly sequence planning method of complex products using discrete multi-objective optimization

Disassembly is a necessary link to realize the integrity of product life cycle. Asynchronous Parallel Disassembly (APD) is an important way to achieve efficient disassembly. Due to the asynchronous start of disassembly tasks by operators in APD, the calculation complexity of disassembly sequence planning optimization increases more obviously with the increase of product complexity. Further, the disassembly sequence is a discrete numerical value, which is difficult to be efficiently realized by existing optimization algorithms. To overcome these difficulties, we introduced a complex products APD description. It helped to describe the APD problem with multiple disassembly resources. Based on the APD resources overall matrix, a three-objective, viz., the total disassembly time-consuming, the disassembly direction change times, and the disassembly tools replacement times, optimization model was constructed. To obtain the optimal disassembly planning sequence, the improved discrete NSGA-II (IDNSGA-II) was proposed, which introduced a novel population restart mechanism. The proposed method was verified in a bevel gearbox disassembly process from the EAS4633 rescue inspection equipment. The optimal results showed the APD sequence planning multi-objective optimized results obtained by the IDNSGA-II algorithm can be close to the single object optimized results. For the rest objects, the optimal solution obtained from the multi-objective genetic algorithm is obviously better.

Grid-moving equivalent source method in a probability framework for the transformer discharge fault localization

The transformer discharge fault localization is important for the power supply reliability. The audible sound radiated by the transformer can reflect the extensive operation information. Thus, discharge faults can be localized by high spatial resolution acoustic imaging techniques to facilitate the inspection and maintenance of power equipment. In this paper, the grid-moving equivalent source method (GMESM) is proposed to visualize the discharge fault on a transformer, where the equivalent source grids can approach the discharge fault position adaptively. A probability framework is established in GMESM. The noise power can be firstly estimated by the Bayes rule to avoid an additional measurement of the background noise. Then, the expectation maximization algorithm is used to optimize the proposed model. Two experiment cases are used to validate the proposed method. It turns out that the proposed GMESM can visualize the discharge fault on the transformer with high spatial resolution and accuracy.

Standardized use inspection of workers' personal protective equipment based on deep learning

Falling from height (FFH) and object strike (OS) accidents frequently occur at construction sites, threatening personnel safety and affecting the construction quality. In the hierarchy of controls, personal protective equipment (PPE) is the most easily achievable measure. However, the presence of hazards and the differentiation in subjective protection awareness make PPE the least effective control measure. Therefore, it is necessary to promote proper and standardized use of PPE to meet the requirements from the perspective of administrative control. Taking two behaviors that can lead to OS and FFH accidents as a research case—loosening the hardhat and not using the safety harness's hook—this study proposes a deep-learning-based inspection method. First, we established a detection model for hardhats and hooks based on You Only Look Once v5. Thereafter, the object-detection model and Openpose algorithm were applied to recognize 1200 video clips containing three unsafe behaviors and one safe behavior and generate 1200 data files that vary in a time series. Finally, a one-dimensional convolutional neural network (1D-CNN) model was trained with the data of 600 videos, and the model was used to test the data of the other 600 videos. The accuracy attained was 0.9467 in the experimental scenario. Using the proposed method, the improper use of PPE can be determined without affecting the normal behavior of individuals, which can improve the efficiency of safety management.

Computer System Integrated with Digital Models for Reconstruction of Underwater Structures with High Definition

The development of research aimed at underwater inspection of subsea equipment has been gaining importance due to the exploration of oil and gas in deep waters. Often, the lack of accurate geometric information on subsea equipment used by the oil and gas industry leads to a series of difficulties in carrying out maintenance operations on this equipment. Currently, the use of sound-based technology is used for this purpose and its limits are known. In this sense, it is necessary to explore new methods that allow generating high-resolution three-dimensional models to represent, with adequate precision, underwater structures. The general objective of this research work presents a novel computational system that provides an accurate three-dimensional representation of structures of underwater equipment. The visualized application is in the oil exploration and production sector offshore, to fill important gap technology available for robotic underwater operations. This representation is a valuable resource to facilitate the planning and execution of the monitoring and maintenance activities in these assets. The feasibility of implementing this system is confirmed by the maps obtained during the testing phase.

Semiconductor Defect Pattern Classification by Self-Proliferation-and-Attention Neural Network

Semiconductor manufacturing is on the cusp of a revolution: the Internet of Things (IoT). With IoT we can connect all the equipment and feed information back to the factory so that quality issues can be detected. In this situation, more and more edge devices are used in wafer inspection equipment. This edge device must have the ability to quickly detect defects. Therefore, how to develop a high-efficiency architecture for automatic defect classification to be suitable for edge devices is the primary task. In this paper, we present a novel architecture that can perform defect classification in a more efficient way. The first function is self-proliferation, using a series of linear transformations to generate more feature maps at a cheaper cost. The second function is self-attention, capturing the long-range dependencies of feature map by the channel-wise and spatial-wise attention mechanism. We named this method as self-proliferation-and-attention neural network. This method has been successfully applied to various defect pattern classification tasks. Compared with other latest methods, SP&A-Net has higher accuracy and lower computation cost in many defect inspection tasks.

A Study on the Automatic Control System for Quality Assurance of Clinical Pathology Examination Applying Machine Learning Technology

The object of research is a study on a machine learning-based integrated quality control system to provide accurate test results quickly by improving the accuracy of test results and improving the utilization rate of test equipment by analyzing clinical pathology Examination data. As for the development method for research, IEC 62304, the international standard for medical S/W life cycle used in medical software development, was applied, and the S/W life cycle rule was applied to all processors by integrating agile methodology. Random Forest, XGboost (extreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine), and DNN (Deep Neural Networks) to improve Rule Check accuracy to predict abnormality of inspection results, and to monitor abnormal results in real time by rule check calculation method technique was applied. In addition, to improve user convenience, automatic control and user interface technology using a dashboard function applied with machine learning technology, and automatic interlocking interface technology for various heterogeneous inspection devices were applied. Validation and verification were conducted through a qualified testing body (TTA) to ensure reliability. The following results were found through this study. As a result of implementing a module that applied big data-based machine learning technology to the algorithm used for quality control judgment of the first knowledge-based expert system, it was possible to implement a module with more than 95% accuracy. there was. Second, in order to determine whether a real-time alarm function was provided, the development module was linked to the clinical pathology information system and as a result of the experiment, it was found that it was operating normally. In addition, reliability was secured through certification by an accredited certification body. Third, as a communication support method for the interface of the inspection equipment, stability and various technologies were secured through a number of communication tests and certification tests such as RS232C, TCP/IP, and Serial HL7. Fourth, through multiple database tests (Oracle, MSSQL, MySQL, MS Access, etc.), cost savings were secured by resolving duplicate investment by providing database neutrality and interface with other systems. Fifth, utility and user satisfaction were enhanced by providing program functions for outputting the result report in various formats and configuring the UI settings, and the UI settings were modularized to reduce the program development costs and allow the modules to be reused. Through the results of research, small and medium hospitals can improve the reliability of inspection results through the machine learning-based quality control module, and through real-time monitoring of the inspection equipment, it is possible to quickly determine whether there is a failure and improve the operation rate of the inspection equipment. In addition, by providing a module that can be linked with the existing information system, it was made easy to link, and the convenience of the user was improved by providing various UI environments. As a result, it can be expected that the hospital's competitiveness and medical service will be improved by resolving the difficulties of quality control that small and medium-sized hospitals had and providing prompt and accurate test results.

Integrating inspection management (IM) in piping system of petroleum industry in Iran

PurposeThis paper aims to report on the findings from a research project, incorporated by Iran’s Ministry of Petroleum, investigating the integration of inspection management with analysis of document information.Design/methodology/approachA combination of group and individual interviews were undertaken with qualitative methods of analysis to develop an integrated process model. The model developed uses integrity management factors, which allows flexibility and the early integration of inspection management systems at a strategic level, although detailed planning is still required through the use of integrated management tools. The model combines information on responsibilities, tasks and policies, of personnel in the inspection management of the piping system with the oil and gas industry.FindingsThe six inspection activities recognized for the main practical model are, namely, identify pipeline situation; inspect and measure defects; inspection assessment; design inspection; implement inspection activities and measure and report inspection.Originality/valueThis research presented not only a process but also the framework and techniques to manage and improve management effectiveness and inspection efficiency in pipelines of the oil and gas industry. This study will be useful to researchers maintenance professionals and others concerned with inspection and maintenance management of facilities and equipment in the oil and gas industry.

Multi-Classification and Distributed Reinforcement Learning-Based Inspection Swarm Offloading Strategy

In meteorological and electric power Internet of Things scenarios, in order to extend the service life of relevant facilities and reduce the cost of emergency repair, the intelligent inspection swarm is introduced to cooperate with monitoring tasks, which collect and process the current scene data through a variety of sensors and cameras, and complete tasks such as emergency handling and fault inspection. Due to the limitation of computing resources and battery life of patrol inspection equipment, it will cause problems such as slow response in emergency and long time for fault location. Mobile Edge Computing is a promising technology, which can improve the quality of service of the swarm by offloading the computing task of the inspection equipment to the edge server nearby the network. In this paper, we study the problem of computing offloading of multi-devices multi-tasks and multi-servers in the intelligent patrol inspection swarm under the condition of a dynamic network environment and limited resources of servers and inspection equipment. An effective adaptive learning offloading strategy based on distributed reinforcement learning and multi-classification is proposed to reduce the task processing delay and energy consumption of the intelligent inspection swarm and improve the service quality. Numerical experimental results demonstrate that the proposed strategy is superior to other offloading strategies in terms of time delay, energy consumption and quality of service.