Inspection Costs Research Articles

Economic Sustainability for Supply Chain with Multiple Instances Of Imperfect Machinery and Controllable Inspection Costs Under Paris Accord

Economic Sustainability for Supply Chain with Multiple Instances Of Imperfect Machinery and Controllable Inspection Costs Under Paris Accord

Power Line-Guided Automatic Electric Transmission Line Inspection System

The existing unmanned aerial vehicle (UAV)-based electric transmission line inspection systems generally adopt manual control and follow the predefined path, which reduces the efficiency and makes a high inspection cost. In this article, a UAV system with advanced embedded processors and binocular visual sensors is developed to generate guidance information from power lines in real-time and achieve automatic transmission line inspection. To realize 3-D autonomous perception of power lines, we first propose an end-to-end convolutional neural network (CNN) to extract complementary information from multilevel features and detect power lines with different pixel widths and orientations. Specifically, multilevel feature aggregation module fuses multilevel features within the same stage by learning the weight vector related to the content. The joint attention (JA) module is proposed to extract rich semantic information and suppress the background noises. Meanwhile, multistage detection results are fused to enhance the robustness of the proposed network. Subsequently, power lines are grouped according to the morphological characteristics of thinning detection results, and 3-D point sets of power lines are constructed based on the epipolar constraint of binocular images. Finally, the target point of current stereo images is generated by projecting 3-D power line points to the horizontal and vertical planes. The few-waypoint trajectory is generated based on continuous target points, and automatic inspection is finished with the proposed real-time motion planning strategy. Experimental results on four datasets show that the proposed power line detection method outperforms other state-of-the-art methods. The developed UAV platform and the proposed autonomous inspection strategy are evaluated in practical environments to validate the robustness and effectiveness.

In loco nursing practice inspection costs in a Brazilian setting.

To identify the average direct cost related to the direct labor of the inspectors involved in the "in loco inspection" step of the inspection process carried out at the Headquarters of the Regional Nursing Council of São Paulo. Quantitative, exploratory-descriptive research, in the form of a single case study. The non-probabilistic convenience sample consisted of records of initial and return "in loco inspections", carried out by inspectors working at the Headquarters of The Regional Nursing Council of São Paulo, from January 13, 2020 to March 13, 2020. The average direct cost of initial in loco inspection (N = 182) corresponded to BRL 331.67 (SD = 140.32), ranging from BRL 115.80 to BRL 1071.15, and that of return in loco inspection (N = 98) to BRL 256.16 (SD = 130.90), ranging from BRL 77.20 to BRL 694.80. Time and cost variables analysis of initial and return in loco inspections showed an alpha significance level of 0.05, and it was possible to statistically state that the time (p ≤ 0.001) and the cost of initial in loco inspection (p ≤ 0.001) are higher than those for return in loco inspection. the cost of the step of "in loco inspection" will support the Nursing Council in the decision-making process aiming at allocating efficiency of human resources required in the inspection process.

Study on Pollution Cost Control Model under Asymmetric Information Based on Principal Agent

Abstract Pollution cost control is key to solve pollution problem. The paper takes pollution control cost of pollution control contract between management authority and pollutant discharge enterprise as research object, considers pollution control quality level, pollution control quality inspection and pollution control cost model, and establishes pollution control cost model of management authority and pollutant discharge enterprise, including rational constraints of pollutant discharge enterprise. And it analyzes principal-agent relationship between the two under condition of asymmetric information, and un-observability of pollution control level is shown as hiding information of sewage enterprises. In essence, it is problem of adverse selection in principal-agent. Pollution control cost of management is objective function. The first order condition of pollution control cost of sewage enterprise is transformed into state space equation, and optimal control of problem is solved by using maximum principle. In particular, management authority, as principal, uses pollution control provisions to reward, punish and encourage pollutant discharge enterprises as agents.

A sustainable smart multi-type biofuel manufacturing with the optimum energy utilization under flexible production

Biofuel manufacturing from renewable biomass through a smart manufacturing system is an important alternative to fossil fuels, which helps to decrease dependability on conventional fuel and decrease carbon emanations. By utilizing efficient labors, smart machines, and minimized energy utilization, the conventional biofuel manufacturing framework can be converted into a smart sustainable manufacturing framework. The existing conventional fuel demand can be replaced by biofuel. This study efforts to make pure biofuel with less amount of carbon emanations and energy utilization through a smart multi-type biofuel manufacturing framework, where the demand of the biofuel is selling price dependent. The different energy costs including air handling cost, lighting cost are calculated in this work-in-process inventory. The carbon emissions cost is included in every stage of this model. A variable demand has introduced for the maximization of profit. To reduce energy consumption and carbon emissions, a two-stage inspection cost with a variable manufacturing rate is taken to make the manufacturing process flexible such that the amount of impure biofuel is minimized. Although a random manufacturing rate is applied through a smart manufacturing system, still impure biofuel is manufactured. The impure biofuel is remanufactured again through refining just after the well-planned manufacturing ends. In this model, the multi-delivery technique is used such that a fixed amount of biofuel of n segments of the pure biofuel is transported to market places at predetermined intervals at the time of delivery frame. The classical optimization technique is used for continuous and differentiable variables and the mixed integer programming technique is utilized for discrete variables to maximize the total profit globally. To validate the model’s usefulness, four numerical observations are studied. It is shown that by utilizing the said procedure, the percentage of impure biofuel can be decreased truly through the minimized energy consumption. The graphical representation and the sensitivity experiment show the impact of every parameter with the whole cost of the research paper. Numerical results assist to get the maximum profit and the optimum selling price of biofuel globally in a sustainable smart multi-type biofuel manufacturing system.

Instance Segmentation for Governmental Inspection of Small Touristic Infrastructure in Beach Zones Using Multispectral High-Resolution WorldView-3 Imagery

Misappropriation of public lands is an ongoing government concern. In Brazil, the beach zone is public property, but many private establishments use it for economic purposes, requiring constant inspection. Among the undue targets, the individual mapping of straw beach umbrellas (SBUs) attached to the sand is a great challenge due to their small size, high presence, and agglutinated appearance. This study aims to automatically detect and count SBUs on public beaches using high-resolution images and instance segmentation, obtaining pixel-wise semantic information and individual object detection. This study is the first instance segmentation application on coastal areas and the first using WorldView-3 (WV-3) images. We used the Mask-RCNN with some modifications: (a) multispectral input for the WorldView3 imagery (eight channels), (b) improved the sliding window algorithm for large image classification, and (c) comparison of different image resizing ratios to improve small object detection since the SBUs are small objects (<322 pixels) even using high-resolution images (31 cm). The accuracy analysis used standard COCO metrics considering the original image and three scale ratios (2×, 4×, and 8× resolution increase). The average precision (AP) results increased proportionally to the image resolution: 30.49% (original image), 48.24% (2×), 53.45% (4×), and 58.11% (8×). The 8× model presented 94% AP50, classifying nearly all SBUs correctly. Moreover, the improved sliding window approach enables the classification of large areas providing automatic counting and estimating the size of the objects, proving to be effective for inspecting large coastal areas and providing insightful information for public managers. This remote sensing application impacts the inspection cost, tribute, and environmental conditions.

Formulation of Measurement Sampling Plan for Small and Medium Batch Products in Supply Chain

The article studies the supply chain of suppliers and manufacturers, considering the overall benefits of the supply chain, the measurement sampling plan is the small batch sampling plan with the smallest amount of sampling and the strongest discriminative ability. If the quality characteristic data is a measurement value, and the inspection workload is large and the inspection cost is high, it is recommended to adopt the measurement sampling plan based on the Laplace distribution. On the premise of ensuring quality, try to reduce the number of inspected samples as much as possible to reduce the workload and cost of inspection.

Efficient Detection of Underwater Natural Gas Pipeline Leak Based on Synthetic Aperture Sonar (SAS) Systems

Natural gas is an important source of energy. Underwater gas pipeline leaks, on the other hand, have a serious impact on the marine environment; hence, the need for a reliable and preferably automated inspection method is essential. Due to the high impedance difference and strong scattering properties of gas bubbles in the marine environment, sonar systems are recognized as excellent tools for leak detection. In this paper, a new method for gas leak detection is proposed based on gas bubble acoustic scattering modeling using Synthetic Aperture Sonar (SAS) technology, in which a coherent combination of gas bubble and pipeline scattering fields at different angles along synthetic apertures is used for leak detection. The proposed method can distinguish leak signals from the background noise using coherent processing in SAS range migration. SAS as an active sonar can collect accurate information at wide area coverage rate, independent of operating range and frequency, which can potentially reduce the time and cost of pipeline inspection. The simulation and comparison results of the proposed method based on coherent processing of synthetic aperture technology and the real aperture system show that the proposed method can effectively distinguish gas bubble signals at different ranges even in a single pass and improves pipeline leak detection operations.

A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods

Large-scale structural health monitoring and damage detection of concealed underwater structures are always the urgent and state-of-art problems to be solved in the field of civil engineering. With the development of artificial intelligence especially the combination of deep learning and computer vision, greater advantages have been brought to the concrete crack detection based on convolutional neural network (CNN) over the traditional methods. However, these machine learning (ML) methods still have some defects, such as it being inaccurate or not strong, having poor generalization ability, or the accuracy still needs to be improved, and the running speed is slow. In this article, a modified fully convolutional network (FCN) with more robustness and more effectiveness is proposed, which makes it convenient and low cost for long-term structural monitoring and inspection compared with other methods. Meanwhile, to improve the accuracy of recognition and prediction, innovations were conducted in this study as follows. Moreover, differed from the common simple deconvolution, it also includes a subpixel convolution layer, which can greatly reduce the sampling time. Then, the proposed method was verified its practicability with the overall recognition accuracy reaching up to 97.92% and 12% efficiency improvement.

Optimization of Inspection Plan for Transformer Verification Line Under Uninterrupted State

At present, transformer verification line of metering centre adopts fixed cycle inspection method manually. This method requires downtime for detection, which costs a lot of time and cost. Moreover, the inspection cycle is determined based on experience and lacks rigorous basis. To solve this problem, a hybrid delivery of inspection devices is proposed to realize non-stop detection and reduce the cost of inspection time. Considering impact of cost and false detection risk on inspection cycle, a multi-objective optimization model of inspection cycle based on inspection and false detection cost is proposed. Based on NSGA-II algorithm, perturbation population is introduced to enhance the global search ability, which aims to minimize the cost of inspection and false detection. Taking the verification line’s inspection plan of the metering centre as an example. It is solved by ENSGA-II algorithm, and feasibility of hybrid delivery mode is verified, which reduced downtime by 14.58%. A more reasonable inspection cycle is obtained, inspection cost is reduced by 29.57%, and false detection cost is reduced by 6.34%. It provides a reference for the formulation of inspection plan in the actual production process.

Rapid positioning method of power line inspection unmanned aerial vehicle assisted by BeiDou ground-based augmentation station

BeiDou high-precision power line inspection can improve the efficiency and automation level of inspection operations, reduce labor intensity, and save inspection costs. However, although the satellite-based augmentation technology can solve the problem of large-scale positioning, it will introduce too many parameters into the positioning system through the method of parameter estimation. As a result, the behavior of the equation deteriorates, which makes it difficult to separate some parameters, and the convergence time of ambiguity parameters is too long. Under normal circumstances, the satellite-based augmentation standard positioning service needs at least about 30 min to converge to the centimeter level, and the ground-based augmentation system can provide an accurate model. This paper proposes a fast positioning method for satellite-based augmentation assisted by the BeiDou ground-based augmentation station. The receiver adopts the least square method, combined with the extended Kalman filter positioning, to achieve the improvement of the regional positioning effect of the satellite-based augmentation system. Positioning accuracy error does not exceed 1.6 meters. The error in the three directions of X, Y, and Z can be converged to the centimeter level in about 8 min. It can significantly improve the convergence time of satellite-based augmentation, and maintain continuous, stable and high-precision positioning of unmanned aerial vehicle in power line inspection.

Optimization Model of Engineering Specifications Based on Grey Quality Gain-Loss Function

In view of the fact that the target values of some quality characteristics are grey, the grey quality gain-loss function model was applied in the analysis of the quality characteristics. At the same time, based on the analysis of engineering specifications and process capability, an optimization model of engineering specifications was proposed to minimize the expected total loss of each product and maximize the expected compensation with inspection costs, scrap costs and grey quality gain-loss into consideration. The optimal engineering specification can be obtained by using the optimization model. Through the example analysis and its application in dam concrete construction, the practicability of the model is verified, which provides an important reference for the research of the new theory of dam concrete construction quality control.

Farm-Level Cost Drivers of Salmonid Fish Health Inspections.

Regulatory costs on aquaculture farms have been shown to be of a magnitude that warrants additional analysis. The drivers of farm-level costs of fish health inspections were identified in this study from national survey data on U.S. salmonid farms. The greatest costs identified were related primarily to state fish health requirements for inspection and testing to certify that fish are free of specific pathogens prior to approval of necessary permits to sell and/or transport animals. Fish health inspection costs included laboratory testing, farm personnel time, veterinary fees, and shipping samples to laboratories, with laboratory testing and the value of farm personnel time being the most expensive components. Principal cost drivers were the number of tests and whether required sampling was farmwide or for each lot as identified by the collector. Farmers who primarily sold into recreational markets had greater fish health costs than farmers who primarily sold food fish because of the greater numbers of species and size-/age-classes of salmonids on their farms. Regulatory requirements to test all species and size-/age-classes on farms increased inspection costs by increasing the total number of tests, the total value of fish sacrificed, and shipping costs. Consequently, for farms with more than one species or more than one size-/age-class, annual farm-level testing was less costly than annual lot-based testing. Increased numbers of tests in a given year, although reported by only a few respondents, can increase costs dramatically and turn profitable farms unprofitable, even food fish farms. Smaller salmonid farms experienced disproportionately greater inspection cost burdens than did larger farms. The fish health inspection scenario of only one annual inspection of only the most susceptible species and size-/age-class showed a cost burden that did not generate economic distress, even on smaller salmonid farms. Other scenarios modeled (based on survey data) that included lot-based surveys of multiple species and size-/age-classes resulted in substantially greater fish health inspection costs that led to unprofitability for various farm sizes and business types. Study results suggest that implementing Comprehensive Aquaculture Health Program Standards might allow for risk- and pathogen-based reductions in the total number of inspections and fish sampled while maintaining equivalent or greater health status compared to current methods. American Fisheries Society-Fish Health Section Blue Book inspection methods are interpreted and applied inconsistently across states and generally yield lot- rather than farm-level health attestations because the history of testing results, risk assessment, and biosecurity practices are not typically taken into account. The cost effects of alternative fish health sampling and testing requirements should be considered in decisions and policy on fish health regulation.

Microbiologically Influenced Corrosion Prediction Via Artificial Intelligence and Electrochemical Impedance Spectroscopy; How Fuzzy Logic and Convolutional Neural Network Can Predict MIC?

Corrosion is one of the most important causes to sustain loss in Industrial plants, particularly, Microbiologically Influence Corrosion (MIC) that is a kind of electrochemical corrosion, which can happen in certain patterns. Sometimes, available information is not reliable to justify in numbers, so, Fuzzy logic can compute this target. However, if you have Fuzzy system, you cannot train it, and therefore, convolutional neural networks can be useful by images and videos.This research aims to describe a fuzzy model for predicting MIC and electrochemical corrosion via convoluted neural network and Electrochemical Impedance Spectroscopy (EIS). The results demonstrate this model gathering a generalizable database, minimized inspection costs and increasing human safety. Keywords: Electrochemistry, Microbiologically Influence Corrosion (MIC), Artificial Intelligence (Al), Convolutional Neural Networks (CNN), Fuzzy Logic, Electrochemical Impedance Spectroscopy (EIS)

Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehicles

AbstractPhotovoltaic solar energy is increasing its capacity in the global electric market due to its lower operating costs and higher efficiency, together with the support of the governments. Photovoltaic solar panels require high initial investments, and it is necessary to use advanced and efficient methods that lead to the maintainability and reliability of these systems, extending their life cycle and productivity. New condition monitoring systems are being applied to reduce the cost of inspections and ensure efficient data collection. The main contribution of this paper is a new efficient and low‐cost condition monitoring system based on radiometric sensors. The thermal patterns of the main photovoltaic faults (hot spot, fault cell, open circuit, bypass diode, and polarization) are studied in real photovoltaic panels. Different scenarios are considered, analyzing online the main patterns of the faults by Internet of Things. The accuracies of the results are statistically analyzed and compared with fault‐free scenarios. The validation of the approach is done by thermographic analysis of the experiments and employing a different radiometric sensor. The detection of faults with statistical analysis is achieved in 100% of the cases, and the identification of the specific fault in 96% of the scenarios. An artificial neural network and classification learner algorithms are employed to validate the results, obtaining similar results for fault identification, 95% and 94%, respectively, and 100% for fault detection.

Implementation of field detection devices for antimalarial quality screening in Lao PDR-A cost-effectiveness analysis.

Substandard and falsified (SF) antimalarials have devastating consequences including increased morbidity, mortality and economic losses. Portable medicine quality screening devices are increasingly available, but whether their use for the detection of SF antimalarials is cost-effective is not known. We evaluated the cost-effectiveness of introducing such devices in post-market surveillance in pharmacies in Laos, conservatively focusing on their outcome in detecting SF artemisinin-based combination therapies (ACTs). We simulated the deployment of six portable screening devices: two handheld near-infrared [MicroPHAZIR RX, NIR-S-G1], two handheld Raman [Progeny, TruScan RM]; one portable mid-infrared [4500a FTIR] spectrometers, and single-use disposable paper analytical devices [PADs]. We considered two scenarios with high and low levels of SF ACTs. Different sampling strategies in which medicine inspectors would test 1, 2, or 3 sample(s) of each brand of ACT were evaluated. Costs of inspection including device procurement, inspector time, reagents, reference testing, and replacement with genuine ACTs were estimated. Outcomes were measured as disability adjusted life years (DALYs) and incremental cost-effectiveness ratios were estimated for each device compared with a baseline of visual inspections alone. In the scenario with high levels of SF ACTs, all devices were cost-effective with a 1-sample strategy. In the scenario of low levels of SF ACTs, only four devices (MicroPHAZIR RX, 4500a FTIR, NIR-S-G1, and PADs) were cost-effective with a 1-sample strategy. In the multi-way comparative analysis, in both scenarios the NIR-S-G1 testing 2 samples was the most cost-effective option. Routine inspection of ACT quality using portable screening devices is likely to be cost-effective in the Laos context. This work should encourage policy-makers or regulators to further investigate investment in portable screening devices to detect SF medicines and reduce their associated undesired health and economic burdens.

Drone-Based Non-Destructive Inspection of Industrial Sites: A Review and Case Studies

Using aerial platforms for Non-Destructive Inspection (NDI) of large and complex structures is a growing field of interest in various industries. Infrastructures such as: buildings, bridges, oil and gas, etc. refineries require regular and extensive inspections. The inspection reports are used to plan and perform required maintenance, ensuring their structural health and the safety of the workers. However, performing these inspections can be challenging due to the size of the facility, the lack of easy access, the health risks for the inspectors, or several other reasons, which has convinced companies to invest more in drones as an alternative solution to overcome these challenges. The autonomous nature of drones can assist companies in reducing inspection time and cost. Moreover, the employment of drones can lower the number of required personnel for inspection and can increase personnel safety. Finally, drones can provide a safe and reliable solution for inspecting hard-to-reach or hazardous areas. Despite the recent developments in drone-based NDI to reliably detect defects, several limitations and challenges still need to be addressed. In this paper, a brief review of the history of unmanned aerial vehicles, along with a comprehensive review of studies focused on UAV-based NDI of industrial and commercial facilities, are provided. Moreover, the benefits of using drones in inspections as an alternative to conventional methods are discussed, along with the challenges and open problems of employing drones in industrial inspections, are explored. Finally, some of our case studies conducted in different industrial fields in the field of Non-Destructive Inspection are presented.

Condition-based maintenance strategies for stochastically dependent systems using Nested Lévy copulas

Nowadays, industrial systems are more and more complex and components are integrated dependently. Without consideration of dependence between components often leads to overestimate the reliability of system and high maintenance cost. In this paper, a multi-component system with hierarchical stochastic dependencies is considered. Components within subsystem are strongly correlated and dependence between subsystems is heterogeneous. Thanks to its capacity of modeling asymmetric dependence structures, the Nested Lévy copula is used in this paper. Based on the multiple sources of dependence among components and hierarchical structures, a novel condition-based maintenance policy is proposed. The inspection scheme as well as maintenance decision is dynamically planned according to the collected inspection information and the influence of stochastic dependence is investigated. The numerical results show the efficiency and robustness of proposed maintenance policy on reducing the inspection and maintenance cost.

Review on Status of Organic Farming

Organic agriculture started much more recently in thousands of years ago, Agriculture almost equal share of the farming systems of crop production and characterizes the key issues of Ethiopian economy. The quality of organic resources and their fertilizer equivalence are the main criteria to be judged for effective utilization of the organic resources for soil fertility improvement and promotion of agricultural products both in quantity and quality.Organic agriculture is production system that sustains the health of soils, ecosystems and people. The evaluation of organic matter in soils depends on type and nature of organic source. Organic agriculture in the extent to which African farmers certified yields (more than 50% higher than local yield averages). The export main products produced are cotton, sesame and coffee, followed by cocoa, dried fruits, frozen fruit/pulp, fresh mainly tropical fruit and spices. Ethiopia is famous as the origin of coffee and is the largest producer in Africa. Forest coffee has the advantage that it originates from an organic and shaded production area - a quality increasingly important for coffee drinkers worldwide coffee cultivation systems, superior quality and organic in nature. Certified-organic farmers will generally require a higher price to compensate for the costs of inspection, certification and the major source of foreign exchange and thus account more than half of the value of total export. Total number of 12 forest coffee cooperatives in remote areas of southwestern Ethiopia were certified according to Fairtrade, organic (EU standard) and Utz Certified standards. The common understanding of agricultural production in all types of organic agriculture is managing the production capacity of an agro-ecosystem. Pesticides and fertilizers are damaging human health and polluting the surrounding environment and thus violating the sustainability of ecosystem.

A Dynamic Methodology for Setting Up Inspection Time Intervals in Conditional Preventive Maintenance

In periodic condition monitoring, the main problem lies in determining the inspection time intervals. This paper presents a new method for setting an optimum calendar to inspect a critical component that fails due to wear and tear as described by a Weibull probability function. By considering a set of inspection intervals, such that reliability between every two inspections is kept equal or below a pre-set threshold while keeping the total costs of inspection, degraded production, consequences of failure, and repair to a minimum. The resulting calendar may be adjusted dynamically over time as inspections take place and test results are found to be negative, by considering the inspector’s confidence in the test and the likelihood of the method’s yielding false negatives. Consequently, the method becomes self-adjustable as it returns a new calendar after the observations of each test are known and properly interpreted. There are several studies that deal with this issue, but none addresses the concept of safe and unsafe time windows which results from merging two other concepts: descendant inspection time intervals and the time delay between a potential failure and a functional failure (the P–F period).