Actual Inspection Research Articles

Autonomous image localization for visual inspection of civil infrastructure

Low-cost, high-performance vision sensors in conjunction with aerial sensing platforms are providing new possibilities for achieving autonomous visual inspection in civil engineering structures. A large volume of images of a given structure can readily be collected for use in visual inspection, overcoming spatial and temporal limitations associated with human-based inspection. Although researchers have explored several algorithms and techniques for vision-based inspection in recent decades, a major challenge in past implementations lies in dealing with a high volume of images while only a small fraction of them are important for actual inspection. Because processing irrelevant images can generate a significant number of false-positives, automated visual inspection techniques should be used in coordination with methods to localize relevant regions on the images. When combined, automated visual inspection will be able to meet the objectives and quality of human visual inspection. To enable this technology, we develop and validate a novel automated image localization technique to extract regions of interest (ROIs) on each of the images before utilizing vision-based damage detection techniques. ROIs are the portions of an image that contain the physical region of the structure that is targeted for visual interrogation, denoted as the targeted region of interest (TRI). ROIs are computed based on the geometric relationship between the collected images and the TRIs. Analysis of such highly relevant and localized images would enable efficient and reliable visual inspection. We successfully demonstrate the capability of the technique to extract the ROIs using a full-scale highway sign structure in the case where weld connections serve as the TRIs.

Early warning modeling and analysis based on analytic hierarchy process integrated extreme learning machine (AHP-ELM): Application to food safety

Since the actual food safety monitoring data have characteristics of high-dimension, complexity, discreteness and nonlinear properties, it is difficult to accurately predict the risk of actual food inspection process. Therefore, this paper proposes a predictive modeling approach based on analytic hierarchy process (AHP) integrated extreme learning machine (ELM) (AHP-ELM). The proposed approach utilizes the AHP model to obtain the effective process characteristic information (PCIs). Compared with the analytic hierarchy process (AHP) integrated traditional artificial neural network (ANN) approach, the AHP-ELM prediction model is effectively verified by executing a linear comparison between all PCIs and the effective PCIs through daily inspection data source from the supervision and inspection department repository of China quality supervision system. Finally, the PCIs and the prediction value are obtained to provide more reliable food information and identification of potentially emerging food safety issues. The proposed method is applied to the food safety early warning and monitoring system in China. The result shows that the proposed model is effective and feasible in processing the complex food inspection data. Meanwhile, it can help to improve the quality of food products, ensure food safety and reduce the risk of food safety.

A survey on inspecting structures using robotic systems

Advancements in robotics and autonomous systems are being deployed nowadays in many application domains such as search and rescue, industrial automation, domestic services and healthcare. These systems are developed to tackle tasks in some of the most challenging, labour intensive and dangerous environments. Inspecting structures (e.g. bridges, buildings, ships, wind turbines and aircrafts) is considered a hard task for humans to perform and of critical importance since missing any details could affect the structure’s performance and integrity. Additionally, structure inspection is time and resource intensive and should be performed as efficiently and accurately as possible. Inspecting various structures has been reported in the literature using different robotic platforms to: inspect difficult to reach areas and detect various types of faults and anomalies. Typically, inspection missions involve performing three main tasks: coverage path planning, shape, model or surface reconstruction and the actual inspection of the structure. Coverage path planning ensures the generation of an optimized path that guarantees the complete coverage of the structure of interest in order to gather highly accurate information to be used for shape/model reconstruction. This article aims to provide an overview of the recent work and breakthroughs in the field of coverage path planning and model reconstruction, with focus on 3D reconstruction, for the purpose of robotic inspection.

An ensemble fruit fly optimization algorithm for solving range image registration to improve quality inspection of free-form surface parts

Free-form surface part inspection can be conducted by comparing an ideal design model with a real measurement model. Because these two models are in different coordinate systems, the measurement model, represented by a set of 3D points, should be consistently registered to the design model. The final aim of the registration is to determine the optimal transformation matrix. In this research area, the iterative closest point (ICP) method is the best-known algorithm for the registration of two point sets. However, the ICP method needs a good initial parameter to obtain the global optimum transformation matrix, which is difficult to guarantee in the actual inspection process. To improve the precision and robustness of complex parts quality inspection, an ensemble parameters fruit fly optimization (EFFO) algorithm is proposed in this study. This paper provides a parameter pool using the smell-based search process of a fruit fly swarm and sorts the individuals based on the fitness to identify the leaders in a vision-based search process. Additionally, an ICP-based initialization strategy is introduced into the EFFO. We compared the EFFO algorithm with other registration methods and some variants of FFO. The proposed algorithm is superior to other algorithms in terms of accuracy and robustness, and the experimental results show that the proposed algorithm is effective.

Multiobjective Optimization Models for Locating Vehicle Inspection Stations Subject to Stochastic Demand, Varying Velocity and Regional Constraints

Deciding an optimal location of a transportation facility and automotive service enterprise is an interesting and important issue in the area of facility location allocation (FLA). In practice, some factors, i.e., customer demands, allocations, and locations of customers and facilities, are changing, and thus, it features with uncertainty. To account for this uncertainty, some researchers have addressed the stochastic time and cost issues of FLA. A new FLA research issue arises when decision makers want to minimize the transportation time of customers and their transportation cost while ensuring customers to arrive at their desired destination within some specific time and cost. By taking the vehicle inspection station as a typical automotive service enterprise example, this paper presents a novel stochastic multiobjective optimization to address it. This work builds two practical stochastic multiobjective programs subject to stochastic demand, varying velocity, and regional constraints. A hybrid intelligent algorithm integrating stochastic simulation and multiobjective teaching-learning-based optimization algorithm is proposed to solve the proposed programs. This approach is applied to a real-world location problem of a vehicle inspection station in Fushun, China. The results show that this is able to produce satisfactory Pareto solutions for an actual vehicle inspection station location problem.

Defect detection of capacitive touch panel using a nonnegative matrix factorization and tolerance model.

Capacitive touch panels (CTPs), as a medium of information interactions, have become essential parts in many consumer electronics. However, current methods such as image edge matching and frequency notch filter cannot suit the defect detection for the new-type complex CTP patterns, which have neither basic primitives nor periodicity. For solving the issues, we proposed a nonnegative matrix factorization (NMF)-based large-size image registration method, and combined it with image tolerance models to detect defects in such CTP patterns. The NMF-based image registration method can fast extract each CTP from a large image. And then, any three of registered images are selected as reference images, which are further processed by threshold processing and simple mathematical morphological operation to obtain tolerance models. Afterward, we can use the tolerance models to obtain a nondefective template. In the normal inspection stage, the defects in CTP patterns can be identified as long as comparing the tolerance models of the template and sensed images. The experimental results show that the proposed method can efficiently and accurately detect various types of defects in CTP patterns. Moreover, the detection results are robust under different illuminations. Therefore, this algorithm can be reliably applied in actual inspection of such new-type CTP patterns.

JVRC競技における調査点検タスク達成度分析

Japan Virtual Robotics Challenge (JVRC) was held during October 7-10, 2015 in Japan, it is a competition of robots by computer simulation, where the competition tasks of JVRC were the combination of ordinary inspection tasks and disaster response tasks for tunnel. In the actual inspection tasks, visual inspection is often used and hammering test is also used, and the latter is difficult to computer simulate accurately. This competition was proposed the equivalent task that is the standard test method by changing the size of QR codes and the length of pipes, the difficulty of the target can be modified. This paper described about the difficulty of the equivalent task based on the experimental results, and about the consideration based on the analyzation for the inspection tasks of JVRC.

打音検査のための自動校正機能を備えた自動変状診断アルゴリズム

The hammering test has been widely used for inspection of social infrastructures because of its accuracy and efficiency of operation. In order to automate and apply the method to actual inspection sites, it is important for the system to calibrate itself against various environmental noise sources, such as strong winds, that influence the accuracy of the acoustic diagnosis. In this paper, a boosting based diagnostic algorithm with an updation rule for the weak learners that constitute the whole defect detector is proposed. Both clean and defective hammering sounds are templatized in the frequency domain, and inner parameters of the weak learners are optimized by using the pair of frequency template vectors. Furthermore, a new updation rule for template vectors is proposed. In experiments, our method was applied to defect detection of concrete test-pieces having slant cracks, which are considered to be serious defects in inspection sites. From the results, the effectiveness of the proposed method was confirmed.

Use of Just in Time Maintenance of Reinforced Concrete Bridge Structures based on Real Historical Data Deterioration Models

Concrete is the backbone of any developed economy. Concrete can suffer from a large number of deleterious effects including physical, chemical and biological causes. Large owning bridge structures organizations are facing very serious questions when asking for maintenance budgets. The questions range from needing to justify the need for the work, its urgency, to also have to predict or show the consequences of delayed rehabilitation of a particular structure. There is therefore a need for a probabilistic model that can estimate the range of service lives of bridge populations and also the likelihood of level of deteriorations it can reached for every incremental time interval. A model was developed for such estimation based on statistical data from actual inspection records of a large reinforced concrete bridge portfolio. The method used both deterministic and stochastic methods to predict the service life of a bridge, using these service lives in combination with the just in time (JIT) principle of management would enable maintenance managers to justify the need for action and the budgets needed, to intervene at the optimum time in the life of the structure and that of the deterioration. The paper will report on the model which is based on a large database of deterioration records of concrete bridges covering a period of over 60 years and include data from over 400 bridge structures. The paper will also illustrate how the service life model was developed and how these service lives combined with the JIT can be used to effectively allocate resources and use them to keep a major infrastructure asset moving with little disruption to the transport system and its users.

Reliability-based temporal and spatial maintenance strategy for integrity management of corroded underground pipelines

In this work, a novel stochastic model framework for predicting the external corrosion growth in buried pipeline structures has been developed, and a reliability-based temporal and spatial maintenance strategy is presented. The spatial correlation of soil properties is modelled via hidden Markov random field. The temporal correlation of the corrosion rate is characterised by the geometric Brownian bridge process. A Bayesian inferential framework is employed to estimate the model parameters of the corrosion growth model using in-line inspection data. The proposed corrosion growth model was validated with actual inspection data. In the reliability analysis, the impact of device detectability is considered and hence the estimated failure probability is more realistic. The proposed maintenance strategy is directly based on the time-specific and location-specific failure probability. The application of the proposed model and maintenance strategy is illustrated through a real-life pipeline system. The results indicate that the proposed maintenance strategy is an adaptive and dynamic scheme that is able to improve the efficiency of inspections.

DNA computer based algorithm for recyclable waste paper segregation

This article explores the application of DNA computing in recyclable waste paper sorting. The primary challenge in paper recycling is to obtain raw materials with the highest purity. In recycling, waste papers are segregated according to their various grades, and these are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products, while saving on processing chemicals and energy. In the industry, different sensors are used in paper sorting systems, namely, ultrasonic, lignin, gloss, stiffness, infra-red, mid-infra red, and color sensors. Different mechanical and optical paper sorting systems have been developed based on the different sensors. However, due to inadequate throughput and some major drawbacks related to mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. The automated paper sorting systems offer significant advantages over the manual systems in terms of human fatigue, throughput, speed, and accuracy. This research has two objectives: (1) to use a web camera as an image sensor for the vision system in lieu of different sensors; and (2) to develop a new DNA computing algorithm based on the theme of template matching techniques for segregating recyclable waste papers according to paper grades. Using the concepts of replication and massive parallelism operations, the DNA computing algorithm can efficiently reduce the computational time of the template matching method. This is the main strength of the DNA computing algorithm in actual inspections. The algorithm is implemented by using a silicon-based computer to verify the success rate in paper grade identification.

Planning Agricultural Enterprises with the Integration of Environmental Effect Interaction and GHG Calculations

The clarification of the theoretical questions pertaining to the planning of agricultural enterprises is imperative in realizing the goals of the input-transformation-output relation. We also can not disregard pinning theory against actual use, meaning the inclusion of limitations in agricultural corporate practice. Scientifically well-prepared processes can go to waste if their limitations have not been evaluated as a result of the absence of information on implementation, theoretic unpreparedness, lack of motivation, or other various reasons. Therefore, in our analyses, we would also like to devote some time to the feasibility of planning and the outlining of planning methods useful for actual practice via actual plant inspections. During the planning phase, extreme difficulties may arise in the form of including environmental effects and making the impacts of the entrepreneur's decisions on the environment felt. Modeling these environmental and climate interactions and including them in planning models are the focus of this examination. The analyses were conducted at the Experimental Farm of Szent Istvan University in Hungary, which has served as the background of different plant economy analyses since 1992. Our experiences, including more than ten years of planning, preparation of decisions, and analysis in the experimental farm, allow us to draw many useful conclusions.

IJARCCE - Computer and Communication Engineering

1,2,3,4 Abstract: The project consists of an efficient Currently Many researchers have addressed this important problem with varying end goals and have proposed valuation techniques to compute the total number of faults. A group of researchers focuses on finding error-prone modules base on the size of the module. Briand et al. Predict the number of remaining faults during inspection using actual inspection data, where as Strand et al predict which files will contain the most faults in the next release. Zhang and Mockus use data collected from previous projects to estimate the number of faults in a new project. However, these data sets are not always available or, even if they are, may lead to inaccurate estimates. For example, Zhang and Mockus use a naive method base only on the size of the product to select similar projects while ignoring many other critical factors such as project type, complexity, etc. Another alternative that appears to produce very accurate estimates is base on the use of Bayesian Belief Networks (BBNs) .However, these techniques require the use of additional information, such as expert knowledge and empirical data, that are not necessarily collected by most software development companies. Software reliability growth models (SRGMs) are also used to estimate the total number of faults to measure software reliability. Although they can be used to indicate the status of the testing process, some have slow convergence while others have limited application as they may require more input data or initial values that are selected by experts.

Stochastic Approach to the Modeling of CO2Corrosion

Corrosion assessment is an integral part of predictive maintenance estimations. In such cases, a risk-based approach yields results that can easily be implemented. The results from deterministic (mechanistic or semi-empirical) corrosion models are valid for a “general” rate of dissolution based on the mass and charge balance of an exposed surface; however, they have a limited application in the quantitative assessment of corrosion-related damage. This work introduces a general stochastic algorithm for calculating the carbon dioxide (CO2)-related distribution of corrosion damage penetration at a given time interval, coupled with corrosion rates predicted by conventional deterministic models. The model is validated by directly comparing the simulation output with actual inspection results from several real cases. The results obtained indicate that the stochastic modeling of CO2 corrosion based on mechanistic principles allows the assessment of failure probability in a quantitative manner. Additionally, the ...

Increasing the Probability of Fault Detection in Non Perfect Inspection Model of Delay Time Analysis with Compromise on Inspection Time

This study separates the real inspection content (soft portion) within the total maintenance inspection activity and attempts to repeat the same some additional number of times during actual inspection. Effect of repetition of soft portion on inspection related time, fault detection probability and the consequence variable of down time per unit time is analyzed. Statistical test proves that both the inspection time and probability of fault detection has nearly same rate of influence on the consequence variable though in opposite direction. A factor ω is introduced to account for the proportion of soft portion over the maintenance inspection time. As the number of repetitions of soft portion is increased for a given value of ω, it is found from analysis that the new set of inspection time and probability of fault detection improves downtime per unit time until an optimum number of repetitions is reached. Improvement is better as the value of ω is on the lower side. The practitioner is to take this possibility of soft repeatable portion of maintenance inspection time into account while estimating these two input parameters when employing delay time methodology as a preventive maintenance strategy.

Evaluating the III-Posedness of Inverse Problem to Size Flaws from Eddy Current NDT Signals Obtained with an Absolute Type Probe

This study discusses the applicability of eddy current testing to the non-destructive evaluation of the depth of a flaw. Three-dimensional finite element simulations are carried out to calculate eddy current signals due to a flaw, and subsequent calculations correlate the difference between signals due to two flaws with the difference between the depths of the flaws. Standard deviation of the difference between the depths of the flaws is used to discuss the ill-posedness of the problem. A flaw is modeled as a sufficiently long rectangular region with a constant width and uniform electrical conductivity; an absolute pancake probe is considered. This study reveals that lift-off and coil diameter do not have a large effect on the ill-posedness, which supports that it is reasonable to decide parameters in actual inspections so that signal-to-noise ratio is maximized. The results obtained also indicate the difficulty in evaluating the depth of a flaw deeper than 1 mm using the signals obtained using an absolute pancake probe.

갱폼 공정 품셈산출을 위한 시뮬레이션 모델 개발

In this study, to resolve several problems in actual inspection stage from the sites for establishment or revision of Construction Standard Production Unit-CSPU such as difficulty of casting objects for inspection from sites, lack of inspectors, etc, we developed Production Unit Module to apply for accessorial work or unproductive work which are occurred in construction process, from Gang-Form which is widely used in formwork for outer wall and we developed Cyclone model for Gang-Form Operation which is having Gang-Form. Also, we verified the application in actual work of Production Unit Module or cyclone Model which are developed in this study by comparing and analysing outfit Production unit through Cyclone Model of Gang-Form Operation and Work Sampling method in actual sites. From the result of comparing or analysing upon outfit Production Unit from Work Sampling and Cyclone Model, we found that both have approximate value within <TEX>${\pm}5%$</TEX> so we can prove the actual application of developed Cyclone Model of Gang-Form Operation.

Selection of Verification Methods for Measurement Instruments Under Restricted Resource Conditions

We consider criteria for selecting deterministic and stochastic decision methods for labor-intensive problems of verification (testing) of measurement instruments. The decision process is modeled using the mathematical framework of game theory. The criterion is based on the comparison of available resources with the cardinality of the set of problem-solving strategies. Keywords: deterministic and stochastic decision methods, game theory, decision-making criterion. Measuring instruments (MI), constantly under improvement, include processors and complex program software (PS) such as a control kernel; they are characterized by a large number of different executable functions including information pro- cessing functions. The verification of the operating integrity of MI prior to their use, in conformance with technical and metrological requirements and validation, is not a simple problem since it requires the comparison of the current test object state with sets of functioning or malfunctioning states. Solving the problem requires significant resources (time, computational power, qual- ified experts participating in the resolution). The most responsible providers of complex MI build in validation resources and automatic program control, but this is far from common for all MI since this significantly increases cost and only a very restricted number of functions are verified. The actual completeness and inspection validity is unknown not only to the tester (user, maintainer), but also to the manufacturer of the MI and measurement systems. We consider the problem of verifying MI PS. The process of verifying (testing) PS is sufficiently laborious that it becomes combinatorial requiring a complicated solution process.

Finite Element Analysis on Seismic Behavior of Typical Residence in Yunnan

Based on the actual inspection to typical Stilt timberwork residence in Puer of Yunnan.This paper find that many places still inadequate, of which the most outstanding part were lack of plinth , the defects of support in the node , the lack of beams and columns ,etc. A contrast analysis of maximum sheers in every storeys and storey drift are made between unreinforced and reinforced slopes by using the finite element software of etabs , which verified the rationality of the strengthening scheme.

Flue Gas Temperature Control Based on Symphony DCS System in Seawater FGD of Coal-Fired Power Plant

With the reinforcement of people’s environmental awareness, the flue gas desulphurization of coal-fired power plant becomes a necessary process. Based on the principle that the temperature of seawater FDG system determines the efficiency of desulphurization in a certain extent, the article introduces the Symphony DCS system which applied to the strategies of the temperature control and the realization methods in the seawater desulphurization. It uses the optimal control principle in the mathematical PADE approximation theory and modern control theory to optimize the temperature control that is with hysteretic. Moreover, it is not only uses C language interface providing by COMPOSER to configuration, but also gives the concrete realization methods. It has been proved be feasible by the actual inspection in the site.