Internal Inspection Research Articles

SPIDER4LEGS MOBILE ROBOT DESIGN CONTROLLED BY THE ESP32 PLATFORM

The grazing robot is a dynamic system, consisting of a platform (the body of the robot) and a number of legs with structures similar to the kinematic chains of manipulator robots. Grazing robots can have a redundant number of legs, therefore they can ensure a static stability even if one or more legs are destroyed [10]. The objective of the work is to design a mobile robot controlled with the Arduino board controlled by an ESP 32 logic processor. The robot proposed in the paper is a spider-type robot with four legs, named SPIDER4LEGS. The components used to make the robot are the following: ESP32 development board, ESP ROOM-32, WiFi and Bluetooth BLE, Dual Core; PCA9685 module, I2C interface, 16 CH, servo motor; 12 Servo motors SG90 180 degrees. A possible field of application of these robots is the internal inspection of pipelines transporting petroleum products [7],[8],[9].

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.

Control system of the autonomous underwater helicopter for pipeline inspection

For decades, in order to fully transport energy and rationally redistribute resources, humans have laid a large number of underwater pipelines in rivers, lakes and oceans to transport resources. However, due to corrosion, load, natural vibration, and crustal movement, underwater pipelines will undergo fatigue fracture, causing resource leakage, environmental pollution and economic losses. In recent years, the problem of maintenance of underwater pipelines has become one of the most difficult problems that need to be solved. And the internal inspection of the pipeline is the key problem. In this paper, we successfully realize this through algorithm and experiments. Here we show a circular dish-shaped underwater helicopter (AUH) that used to realize the depth-fixed line tracking control in the underwater pipeline. This work proposed a priority control system which combines depth control and line tracking control well and an underwater image processing algorithm flow to identify the line well.

The essence of production stocks, accounting problems and directions for its improvement at the enterprise

Production stocks are the subject of labor, which, together with the means of labor, provide the production process of the enterprise where they are used. This topic is relevant, because in the conditions of a market economy, production stocks are an important element of the activity of any enterprise, therefore, their appropriate use in production becomes important. All this leads to the need to study the modern features of the organization of inventory accounting at enterprises in order to achieve more effective results. The paper substantiates the theoretical and methodological aspects of accounting for production stocks at enterprises, examines their economic essence and the main regulatory documents regulating the order of their display in accounting. The importance of effective use of materials in production and proposed actions contributing to the improvement and increase in the efficiency of accounting for production stocks are outlined. The problems that arise with the existing procedure for keeping records of production stocks at the enterprise, which are related to ensuring the efficiency of the enterprise's financial and economic activities, are identified. Such problems as: insufficient automation of the primary accounting of production stocks or the problem of determining the valuation of stocks on the balance sheet date. The following actions were proposed to solve these and other problems in order to improve the quality of accounting at the enterprise: the creation of a nationwide database regarding average market prices for various types of assets; carrying out an internal inspection at the enterprise with the involvement of qualified specialists; the use of automation of calculations for planning, accounting and analysis of the use of material resources. Also, improvements were proposed regarding inventory accounting, which will increase their efficiency and effectiveness of their use at the enterprise. It can be concluded that the improvements mentioned above will increase the efficiency of inventory accounting at the enterprise and improve its effectiveness in financial and economic activity.

Application of Magnetic Flux Leakage Internal Detection Technology in Evaluation and Analysis of Crude Oil Pipeline Corrosion

There are many abnormal points in the magnetic flux leakage internal inspection results of a crude oil pipeline. For 20 of the more serious metal loss anomalies Internal corrosion evaluation standard specification, corrosion evaluation and pressure analysis. The results of corrosion evaluation and pressure analysis are taken as the basis for theoretical screening, and the final selection is preferred. The excavation verification and defect repair were carried out at 3 abnormal points of internal inspection. The accuracy of magnetic flux leakage internal detection results is verified through excavation verification, which proves the magnetic flux leakage internal detection. The measurement technology can play a role in the evaluation and analysis of pipeline corrosion.

Inspection and analysis of a 500kV main transformer excessive acetylene failure

B phase of the no 2 main transformer of 500 kV XX substation was suddenly stopped on August 20, 2021 due to acetylene and an abnormal sudden increase. Acetylene reached 10.5uL / L, far exceeding the acetylene attention value of 1.0uL / L, and there is a trend of continuous growth. An internal inspection was carried out after the power outage, and no obvious abnormality was found. Then it was sent to factory for de-oiling and drying. Inspection found the following problems: 1. The upper and lower iron yoke clamps of main transformer and their pull straps, the fourth pull strap shielding cover from the left of lower clamp on the high pressure side was found to be abnormal; 2. Check the iron core silicon steel sheet, found that there are about 15mm burrs between the iron yoke (high-voltage side) core stages under the side post position; 3. There is a black mark at the connection part of low-voltage lead and the casing oil, but no signs of discharge or overheating are seen; 4. Check that there is no obvious abnormality in other parts of the body, such as insulation, lead wires, and electrical shielding. Combined with the inspection situation, analyze specific causes of the failure and propose follow-up improvement suggestions.

Study on error analysis and correction for catadioptric imaging system: A method for annular inner wall structure measurement

Annular inner wall widely exists in industrial parts, however, because of the limitation of internal narrow space and 360-degree panoramic inspection requirements, the detection of such structures is always difficult. Catadioptric imaging is an effective and rapid detection method for inner wall detection. However, serious distortion always exists due to the alignment deviation and the pose deviation of the structure to be measured, which limits the application of this method. In this paper, firstly, we analyze the main causes and laws of distortion from the perspective of simulation, secondly, based on the simulation result, methods of eliminating distortion are proposed and verified based on the simulation data. Finally, we demonstrate the effectiveness of our proposed method through experiments, based on the experiments, it is proved that the proposed method can solve the limitations of the measurement for catadioptric imaging, and the proposed method can correct distortion to pixel level.

Status diagnosis and feature tracing of the natural gas pipeline weld based on improved random forest model

The health state of natural gas pipeline welds usually needs to be judged by experienced experts based on the results of excavation works or internal inspections. Due to the low sensitivity of internal detection method, high cost, slow speed and large amount of information of excavation work, maintenance personnel are difficult to accurately and efficiently evaluate the health status of in-service pipeline weld. This paper proposes a model based on the improved random forest for weld joint status diagnosis and feature tracing as a solution to that issue. Firstly, the raw data is cleaned and downscaled by data preprocessing, variance threshold function and correlation heat map. Then an improved random forest model based on the cross-validation is established to predict and analyze the above data. Finally, the model's scores, the features' importance scores, and scores can be acquired. And the parts of the relevant features are analyzed retroactively combined with experts' experience. The experimental results show that the improved random forest model has an average score of 0.941, which is higher than that of the support vector machine model (SVM), k-nearest neighbor classification (KNN), decision tree and random forest models, the decisions generated from the results can help maintain and exclude services of the weld joint.

Developing and validating a nomogram to predict myasthenia gravis exacerbation in patients with postoperative thymoma recurrence.

Myasthenia gravis (MG) is one of the most common accessory syndromes for thymoma patients. To reduce MG exacerbation and guide clinical practice, we developed a nomogram for predicting MG exacerbation in patients with postoperative thymoma recurrence. Retrospective study of 176 patients with recurrence following thymoma resection who were admitted to the People's Hospital of Shijiazhuang's Center of Treatment of Myasthenia Gravis between 2013 and 2021. Among them, 120 patients with recurrent thymoma from 2013 to 2020 were selected as the training cohort, and 56 patients in 2021 as the validation cohort. Univariate and multivariate logical regression was used to determine the risk factors and draw the nomogram, and the parameters in the nomogram were proportionally assigned from 0 to 100 points. Finally, the performance of the model is evaluated by internal inspection and external inspection. Multivariate analysis revealed that postoperative treatment plan and the pathologic classification of the thymoma were independent predictors of MG exacerbation in the training cohort (n=120), so they were used to create the nomogram, which had a well-fit calibration curve and good concordance index of 0.77 [95% confidence interval (CI): 0.69-0.86] for the training cohort and 0.74 (95% CI: 0.58-0.91) for the validation cohort, respectively. Calculations were made to determine the nomogram's sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV). The training cohort were 75.7% (95% CI: 66.1-83.4%), 64.7% (95% CI: 38.6-84.7%), 92.9% (95% CI: 84.5-97.1%) and 30.6% (95% CI: 16.9-48.3%) respectively, while the corresponding validation cohort were 84.1% (95% CI: 69.3-92.8%), 66.7% (95% CI: 35.4-88.7%), 90.2% (95% CI: 75.9-96.8%) and 53.3% (95% CI: 27.4-77.7%) respectively. We identified the risk factors for MG exacerbation in patients with postoperative recurrence of thymoma and drew a nomogram, which can be used to calculate the probability of MG exacerbation and guide clinicians to choose post-operative treatment.

Design and optimization of a quad-directional stereo vision sensor with wide field of view based on single camera

Miniaturized multi-directional stereo vision sensor with a wide field of view (FOV) and high measurement precision is highly desirable for internal inspection in confined spaces. However, the current analysis of the design and optimization strategies for the sensor layout are not quite comprehensive. To make optimal use of the sensor and achieve high measurement accuracy, a thorough understanding of this strategy is required. In this work, we present a quad-directional stereo vision sensor based on a single camera and a dual mirror pyramid with no constraint on the placement of the devices. Geometrical analysis of the sensor is conducted by ray-tracing to examine effects of the parameters on the measurement precision, such as model accuracy, depth of field, and optical path difference. On this basis, we have investigated the method to optimize the structural parameters to maximize the overall axial and radial FOV and the utilization efficiency of the sensor. The design and optimization strategies are summarized to optimally set up the structural parameters and a miniaturized prototype of the sensor is developed and verified by both simulations and experiments.

Internal defects inspection of arc magnets using multi-head attention-based CNN

Arc magnets are the key components of various motor machinery, and their internal defects detection is extremely significant for maintaining system performance and ensuring operational safety. In this paper, an end-to-end improved convolutional neural network (CNN) model based on multi-head attention is presented, where features that play a more important role in defect detection could be efficiently highlighted. In addition, owing to the characteristics of strong parallel working ability in multi-head attention, the training process is greatly accelerated. Meanwhile, to meet the requirements of the model on the amount of data, a data augmentation method is designed accordingly. Then, the performance of the constructed framework is verified in different test scenarios. Experiment results demonstrate that the presented approach owns superior inspection performance based on relatively fewer model parameters compared to other existing methods, even under the small sample, intense noise, and the coexistence of noise and insufficient data.

Design and Development of Internal Pipe Inspection Robot

In this modern era, the robotics has become the most significant key in almost all working fields of engineering where work is done without or with minimal physical interference of humans. In the field of engineering the robots are being used to reduce the work effort, to ease the work, to do the work in the areas where humans are inaccessible, to give precision in work, etc. In essence, robots are utilized to boost performance and efficiency while reducing manufacturing costs. The most reliable and secure method of transferring liquid and gas is through a pipeline. Robots have already taken over the production sector in the pipeline industries, they are also utilized in the inspection sector. Since the majority of the fluids that are delivered from pipes are toxic and hazardous, direct human contact is prohibited in this inspection area. The defects occurs external areas of pipe can be easily noticeable and fixable, but those that arise inside the pipe are more difficult to detect and more challenging to fix. So the internal pipe inspection robots are designed to ease the inspection for internal area of pipeline. These robots are designed to move inside the narrow area of pipe to detect, locate and fix the defect. The main objective of current work is to reduce the human effort and provide safety in pipeline inspection sector. This robot can move along the various diameters pipes within its prescribed range. With the help of DC motor the forward and reverse movement is obtained, while IR sensors and camera used to detect the defects like blockage, holes or cracks, dents, leakages, etc. How to cite this article:Kokane RS, Upadhye CR, Kumbhar BS. Design and Development of Internal Pipe Inspection Robot. J Adv Res Mech Engi Tech 2022; 9(3&4): 6-11. DOI: https://doi.org/10.24321/2454.8650.202202

An Experimental Study on Phased Array Ultrasonic Testing for Internal Inspection of Gas Storage Wells

A reduction in wall thickness, caused by corrosion, of the inner and outer surfaces of underground compressed natural gas storage wells is one of the main causes of failure. Therefore, taking wall thickness measurements is an important task during periodic inspection. This study proposes a corrosion detection technique based on the well’s shape using cylindrical convex ultrasonic phased array and develops a cylindrical convex phased array probe with 512 elements. Using an example well of ∅177 × 10.36 mm, the influences of process parameters such as active aperture, focal length, gain, and gate threshold are studied. In addition, an experiment is conducted on a sample well with artificial defects for parameters optimization. Next, experimental tests are carried out on the well at the test platform according to the optimized parameters. The results show that the proposed technique is of high sensitivity and efficiency for the detection of corrosion defects and is able to detect pitting corrosion of more than ∅2 mm and line corrosion of 1 mm width.

Biomimetic Aquatic Robots Based on Fluid-Driven Actuators: A Review

Biomimetic aquatic robots are a promising solution for marine applications such as internal pipe inspection, beach safety, and animal observation because of their strong manoeuvrability and low environmental damage. As the application field of robots has changed from a structured known environment to an unstructured and unknown territory, the disadvantage of the low efficiency of the propeller propulsion has become more crucial. Among the various actuation methods of biomimetic robots, many researchers have utilised fluid actuation as fluid is clean, environmentally friendly, and easy to obtain. This paper presents a literature review of the locomotion mode, actuation method, and typical works on fluid-driven bionic aquatic robots. The actuator and structural material selection is then discussed, followed by research direction and application prospects of fluid-driven bionic aquatic robots.

A novel identification approach for corrosion and gouging of oil and gas pipelines based on low magnetisation level MFL inspection

The ability to characterise corrosion and gouging associated with metal loss and the identification of gouge type from metal loss defect types are two of the primary obstacles affecting magnetic flux leakage (MFL) internal inspection technology. Gouges in pipelines are not extraordinarily severe; however, the depth of corrosion increasing due to a certain corrosion rate can be quite serious. In this paper, a novel theoretical model combined with a new approach for the analysis of the signals that distinguish gouging and corrosion using a low magnetisation level is presented for MFL detection, since the traditional MFL internal detection tools are insensitive to stress characteristics in the case of saturation magnetisation. A two-stage finite element (FE) model for the prediction of magnetic flux leakage resulting from two types of defect is built. In the first stage, the stress distribution associated with gouging is obtained from a solid mechanics model and, in the second stage, the stress distribution is incorporated into a magnetic finite element model by mapping the stress levels to permeability. The possibility of detection and identification of corrosion and gouging using the MFL technique at low-level magnetisation was confirmed by experimentally comparing the characteristics of the MFL signals for each defect type.

Investigation on the concrete strength performance of underlying tunnel structure subjected to train-induced dynamic loads at an early age

The strength tests of vibrated concrete were carried out to investigate the influences of train dynamic loads on the later strength performance of concrete in the underlying tunnel structure. This study is based on a cast-in-place tunnel project subjected to the dynamic loads from the upper railway. With the measured data from the project site and the analysis results of numerical simulation, the primary train-induced vibration spectrum was determined and applied on concrete. At an early age, e.g. within the first 48 h after pouring, the concrete in the mold was subjected to gap vibration by imitating the actual train running interval. Compressive and tensile strength tests, as well as the internal ultrasonic inspection, were conducted on C40 concrete of 0.5, 1, 2, 3, 7, 14, and 28 days old which had been subjected to different levels of dynamic loads. The experimental results show that the strength of C40 concrete under train-induced dynamic loads with a basic frequency of approximately 114 Hz and a peak acceleration of approximately 0.2 g at an early age is the same as that of the specimen without vibration. Loads lower than the acceleration benchmark (frequency of 114 Hz and peak acceleration of 0.2 g) increase the strength in the later stage, while larger loads resulted in irreparable damage inside the specimen and reduced the strength. The peak acceleration value of 0.2 g can be regarded as the quasi-critical point of C40 concrete performance affected by the train dynamic load at the basic frequency of 114 Hz.

Inline nondestructive internal disorder detection in pear fruit using explainable deep anomaly detection on X-ray images

To preserve the quality of fresh fruit after harvest and to meet the year-round demand for high-quality fruit, pears are stored under a controlled atmosphere. However, due to preharvest events or suboptimal storage conditions, internal disorders might develop resulting in severe quality loss. Examples include internal browning and cavities, which are invisible externally. Here, X-ray radiography is investigated as a technique for internal quality inspection. The detection of defect fruit is approached as an anomaly detection (AD) problem, in which a model is constructed using nominal data and an anomaly score is used to identify defect fruit. In this work, multiple deep AD methods are shown to be effective to detect pears with internal cavity and browning disorders using X-ray radiographs (mean area under the receiver operating characteristic curve (AUC) up to 0.962). The best performing methods were found on par with a state-of-the-art multisensor disorder detection method (mean AUC up to 0.966). By investigating AD performance in function of internal disorder severity, it was shown that defect fruit with a cavity volume percentage > 1.0% could be detected 100% accurate using inline X-ray imaging. For lower cavity area percentages, the accuracy depended on the internal browning severity. Additionally, the explainability of the deep AD methods, i.e., how well human interpretable insight can be provided from each method’s predictions, were qualitatively evaluated using anomaly heatmaps, which provided useful insight in the execution of the deep learning algorithms.

Thrust Prediction Method of Jet Propulsion for Transformer Internal Inspection Robot

Abstract Robot technology has been successfully applied to the internal inspection of large power transformers. In order to further improve the level of robot intelligent control and realize robot motion control accuracy of the floating state, this paper analyzes the factors that affect the jet thrust of the robot propulsion system on the developed spherical transformer internal inspection robot platform based on jet propulsion, and builds an experimental platform. Combining experimental data to establish jet thrust equation. On this basis, the working state of the robot power system is further analyzed, the equivalent discharge model of the robot battery is established, the battery state parameters are introduced into the jet thrust equation. Finally, a mathematical model of jet thrust prediction is established that comprehensively considers the robot power system, control system, and environmental parameters. The establishment of this model lays a good theoretical foundation for the establishment of the robot’s precise dynamics model, fine motion control and intelligent control algorithm.

Tree Health Monitoring of Risky Trees in the Hotel Open Space: A Case Study in Rancamaya, Bogor

Tree health monitoring of risky trees is necessary, especially in areas with a high level of accessibility, such as in hotels and tourist areas. The increased accessibility of hotel visitors and the green space require increased awareness of the risk of falling trees. This research aimed to estimate the amount of internal decay and damage of living trees, with special attention to the large tree in the open space of R Hotel Rancamaya. Tree health monitoring was carried out using two approaches, namely visual observation Sonic Tomography method. The visual parameters were observed following the standard method of the International Society of Arboriculture. A total of 8 trees consists of four types of plants such as Melia azedarach, Durio zibethinus, Falcataria falcata, and Ficus subcordata. The eight trees visually showed no significant damage to their organs. The results of internal trunk inspection by PiCUS-3 Sonic Tomograph also showed a similar result with decay or weathering ranges ranging from 1-3% in healthy trees, except for the stem base of tree 7 (M. azedarach) with weathering of 18%. Handling that needs to be done on six trees at the green area hotel is through light pruning and installing warning signs so that visitors and staff are careful in the tree area. Substantial pruning is recommended specifically for tree 7 because there is significant weathering, and it could be dangerous if the tree is not appropriately handled. The assessment showed that mitigation, warning signs, and physical handling are essential to prevent any unpredicted fallen trees, especially for tree 3 (D. zibethinus) and tree 7 (M. azedarach). Keywords: hotel open space, risky trees, Sonic Tomography, tree health monitoring, visual observation

Bayesian Optimization-Based Gradient Boosting Method of Fault Detection in Oil-Immersed Transformer and Reactors

This article aims at developing a gradient boosting (GB) model with parameters optimized by the Bayesian optimization (BO) method for detecting various types of fault in an oil-immersed transformer and reactor. The developed model depicts higher accuracy in comparison with other machine learning (ML) techniques, such as XGBoost, AdaBoost, random forest, and support vector machine parameters being optimized by BO. A comparison of the skill score depicts the superiority of the developed Bayesian optimization-based gradient boosting (BO-GB) model with respect to Bayesian optimized other ML techniques. Further comparison of the model with conventional Duval pentagon verifies the accuracy of detecting the faults, which were missed by the former. Also, the software has been developed based on the proposed BO-GB method for the on-site detection of the fault. In addition, actual values of dissolved gases with respect to fault confirmed after the internal inspection of the transformer and reactor are tabulated and compared. The data so presented will help in utilizing the authentic dissolved gas data in addition to the already available IEC TC10 databases for future works. The study undertaken in this article will help utilities in diagnosing the incipient faults more accurately and precisely, thereby preventing hazardous operating conditions along with minimizing the downtime cost.