Poor Inspection Research Articles

Learning from a poor inspection

Learning from a poor inspection

Empirical Case Study on Applying Artificial Intelligence and Unmanned Aerial Vehicles for the Efficient Visual Inspection of Residential Buildings

Continuous inspections and observations are required to preserve the safety and condition of buildings. Although the number of deteriorated buildings has increased over the years, traditional inspection methods are still used. However, this approach is time-consuming, costly, and carries the risk of poor inspection owing to the subjective intervention of the inspector. To overcome these limitations, many recent studies have developed advanced inspection methods by integrating unmanned aerial vehicles (UAVs) and artificial intelligence (AI) methods during the visual inspection stage. However, the inspection approach using UAV and AI can vary in operation and data acquisition methods depending on the building structures. Notably, in the case of residential buildings, it is necessary to consider how to operate UAVs and how to apply AI due to privacy issues of residents and various exterior contour shapes. Thus, an empirical case study was adopted in this study to explore the integration of UAVs and artificial intelligence (AI) technology to inspect the condition of structures, focusing on residential buildings. As a result, this study proposed the field-adopted UAV operation method and AI-based defect detection model for adopting the residential buildings. Moreover, the lessons learned from holistic and descriptive analyses, which include drone application limitations, points of improvement of data collection, and items to be considered when AI and UAV based inspection for residential buildings, are summarized in this paper. The discussed problems and results derived from this study can contribute to future AI- and UAV-based building inspections.

Research on the Inspection Method of Extra High Voltage Grid Based on 3D Model Fusion

The current traditional grid inspection method eliminates potential hidden problems of the power system by ranking fault points, but the lack of extraction and fusion of grid scene features leads to poor inspection results. In this regard, a three-dimensional model fusion-based inspection method for an extra-high voltage power grid is proposed. The key motion metric function is defined by calculating the local minima. And the 3D model is constructed to extract and fuse the UHV grid scene feature data, and finally, the path of UAV inspection is planned. In the experiment, the inspection performance of the proposed method is verified. The analysis of the experimental results shows that when the proposed method is used to inspect the extra-high voltage grid, the KRS index value is high and has a more excellent UAV inspection path planning capability.

Enhancement of all-optical photoacoustic detection sensitivity via elliptical reflector

All-optical photoacoustic detection has poor inspection quality when it is used on rough surfaces due to strong light scattering. We report on the use of an elliptical reflector to improve the sensitivity of all-optical photoacoustic inspection on rough surface samples. By employing the bi-focal property of the elliptical reflector, the amplitude of the photoacoustic signal can be increased by 3 times. It also greatly improves the imaging quality of photoacoustic inspection of rough surface samples. This technique has the potential to extend the applications of all-optical photoacoustic detection in industrial applications, where rough surface samples are common.

A socio-technical approach to food safety incident analysis using the AcciMap model in the hospitality sector

A theory-based systems approach, such as AcciMap accident analysis, has been widely used over the years in multiple safety critical sectors such as the nuclear, petrochemical, aviation and railway industries to provide a detailed understanding of complex systems and the chain of events contributing to accidents resulting from system failure. However, despite its advantages, the use of a systems approach in the food safety context has to date been limited. The purpose of this study was to investigate three established norovirus incidents using the AcciMap accident analysis approach to determine its efficacy at informing the design of food safety policies following a norovirus outbreak to prevent reoccurrence. This approach was found to be of value in analysing norovirus outbreaks. The findings of the AcciMap analysis reveal the norovirus outbreaks were not the outcome of a single causal incident, but a chain of events and interactions that involved governmental failure to control and enforce safety regulations and the impact on managerial and individual behaviours at a lower level in the system. The analysis identified the common contributory factors such as poor inspections, lack of regular monitoring of quality of water supply, inadequate management of wastewater and ineffective communication that led to each incident across the hierarchical levels within a socio-technical system. The value of using the AcciMap approach is that it does not constrain the analysis to individual components or particular types of incident allowing for a more holistic and interconnected risk assessment.

Individualized feedback on colonoscopy skills improves group colonoscopy quality in providers with lower adenoma detection rates.

Background and study aims Colonoscopy inspection quality (CIQ) assesses skills (fold examination, cleaning, and luminal distension) during inspection for polyps and correlates with adenoma detection rate (ADR) and serrated detection rate (SDR). We aimed to determine whether providing individualized CIQ feedback with instructional videos improves quality metrics performance. Methods We prospectively studied 16 colonoscopists who already received semiannual benchmarked reports of quality metrics (ADR, SDR, and withdrawal time [WT]). We randomly selected seven colonoscopies/colonoscopist for evaluation. Six gastroenterologists graded CIQ using an established scale. We created instructional videos demonstrating optimal and poor inspection techniques. Colonoscopists received the instructional videos and benchmarked CIQ performance. We compared ADR, SDR, and WT in the 12 months preceding (“baseline”) and following CIQ feedback. Colonoscopists were stratified by baseline ADR into lower (≤ 34 %) and higher-performing (> 34 %) groups. Results Baseline ADR was 38.5 % (range 26.8 %–53.8 %) and SDR was 11.2 % (2.8 %–24.3 %). The proportion of colonoscopies performed by lower-performing colonoscopists was unchanged from baseline to post-CIQ feedback. All colonoscopists reviewed their CIQ report cards. Post-feedback, ADR (40.1 % vs 38.5 %, P = 0.1) and SDR (12.2 % vs. 11.2 %, P = 0.1) did not significantly improve; WT significantly increased (11.4 vs 12.4 min, P < 0.01). Among the eight lower-performing colonoscopists, group ADR (31.1 % vs 34.3 %, P = 0.02) and SDR (7.2 % vs 9.1 %, P = 0.02) significantly increased post-feedback. In higher-performing colonoscopists, ADR and SDR did not change. Conclusions CIQ feedback modestly improves ADR and SDR among colonoscopists with lower baseline ADR but has no effect on higher-performing colonoscopists. Individualized feedback on colonoscopy skills could be used to improve polyp detection by lower-performing colonoscopists.

Validation of a Stewart platform inspection system with an artificial neural network controller

The use of thermographic cameras attached to Stewart platforms may become a valuable approach to build non-destructive inspection systems. However, achieving successful inspections requires positioning the platform that holds the camera very accurately, especially when the camera lacks of autofocus capabilities. Thus, if the distance from the lenses to the inspection surface is in range, sharp quality images may be obtained, otherwise, images may suffer defocusing producing poor inspection results. This work presents a new Artificial Neural Network (ANN) control aimed at improving the accuracy of the Stewart platform that holds a camera, hence improving the quality of the inspection system. The proposed ANN architecture positions the platform more accurately than the vendor provided Proportional Integral (PI) control strategy. The system kinematics and dynamics are analyzed in order to design the ANN model for the system. The developed control scheme is based on the inverse ANN model with an additional integral control. The advantages of this novel approach are that (1) the controller considers the interactions among the actuators and (2) includes a training process that continuously fine-tunes the ANN weight matrix. The proposed control scheme was validated experimentally, and the performance compared with tuned PI control, taken as the reference, for two different inspection trajectories. The results show that the ANN control is much more accurate and efficient than the reference PI (above 40% error reduction in the actuator positioning with the same power consumption), hence performing the inspections successfully.

Performance of Abuja MOU on Port State Control in Enforcement of IMO Regulations on Maritime Safety

Abstract The study evaluates the performance of the Abuja Memorandum of Understanding (MoU) on port states control (PSC) with a view to providing empirical evidence and information on the rates of change in inspection efforts, detention efforts, and poor inspection performance of PSC Officers in the Abuja MoU. It examined the performance of the Abuja MoU and the level of compliance with International Maritime Organization (IMO) regulations in the West and Central African sub-region. Secondary data obtained from the database of the Abuja MoU administration was used. The findings indicate that the implementation of the instrument has improved maritime safety in the region. The result also shows that trends of inspection efforts, poor inspection performance, number of inspections with and without detected deficiencies grew in the monitored period. The detention rate declined, which implies a decline in the trend of deploying unseaworthy vessels in the waters of the region, which is good for maritime safety administration in the region. About 92.5 % of the total inspections are fully compliant with the IMO safety regulation while 7.44 % of the inspection efforts show shortcomings and failure to comply with IMO regulations in the region

Analysis of the Optimization Strategy Based on the Cooperation of Drones and Intelligent Robots for the Inspection of Substations

With the continuous development of the power system, the operation environment and scale of the power grid substation are more complex. It will become an inevitable trend for automated detection methods to replace manual detection methods because of their intelligence and security. However, at present, the use of intelligent robot inspection substation technology is not very mature, there are visual blindness, long inspection cycle, poor inspection effect, and other problems. By the development trend of the substation, this paper proposes a high altitude, low altitude, and ground inspection strategy of a substation based on the cooperation of UAV and intelligent robots. This strategy uses the grid method to construct the two-dimensional planning of the substation and then optimizes the inspection path of UAV and intelligent robots based on the improved ant colony algorithm. The UAV and the intelligent robot cooperate to realize the real-time inspection of the substation, forming a three-dimensional inspection system of "low-mid-high", "dynamic + static". The simulation results show that the inspection strategy using UAVs and intelligent robots is safer and can effectively reduce inspection time.

Meat quality, biosecurity, and animal welfare: consequences of an inefficient inspection

In livestock, biosecurity, animal welfare and meat quality have a synergetic effect on the final product. To meet minimal safety and humane production, it is necessary to consider biosecurity risks and procedures to mitigate animal welfare problems. Following guidelines to achieve it, the meat quality improves consequently. Here we tested two parameters regarding the meat quality of beef cattle to understand how the poor inspection affects the quality of the final product. Therefore, 12 samples of the longissimus dorsi muscle were evaluated from different butcheries, using fresh meat recently received by the supplier. There were no sanitary inspection records related to the sampled meat. The results showed a large percentage of PSE (pale, soft, and exudative) and DFD (dark, firm, and dry) meats. Given the pH and color values, it is concluded that the slaughtering was not carried out properly. Hence, technological defects in the meat were observed, making it inappropriate for consumption. We concluded that inefficient pre-slaughter handling is compromising animal welfare, increasing the biosecurity risk, and impairing the quality of the final product. We argue that these problems not only decrease the sustainability of livestock systems, but also compromises human health. Currently, livestock plays an important role in global environmental concerns, such as climate change, land degradation, water pollution and biodiversity loss. Then, it is needed to reduce the waste of such products.

An investigation into occupational health and safety of scaffolding practices on construction sites in Malaysia

PurposeStatistics show that the construction sector has the second-highest number of accident cases in Malaysia. A total of 100, 000 construction workers suffer from work-related bad health each year. Scaffolding accidents are the second cause of accidents on construction sites. Therefore, this present research provided answers to the following questions: (1) what are the causes of scaffolding accidents and (2) what are the possible measures to reduce scaffolding accidents?Design/methodology/approachThe research developed a questionnaire instrument that included 24 causes of scaffolding accidents and 21 remedial actions. The research was based on a cross-sectional survey questionnaire administered to 129 members of construction organizations.FindingsData revealed that scaffolding-related cases caused a total of 70% of the deaths/injuries on sites. Furthermore, scaffolding accidents were mainly caused by a lack of guard rails on scaffoldings, poor inspections, improper assembly, a poor safety culture, poor attitudes towards safety, poor footing of scaffoldings and unsecured planking. To reduce scaffolding accidents, there must be a lifeline on scaffolding, proper guardrails and proper assembling of scaffoldings, and preventing access to incomplete or defective scaffoldings. The 24 causes are structured into six factors through factor analysis and the 21 remedial actions into six factors.Originality/valueThis research serves as the first attempt to conduct broad research on the causes and remedial actions concerning scaffolding accidents on construction sites in Malaysia. Theoretically, the research has provided fresh insights into the impact of scaffolding accidents.

General mental state and quality of working life of call center employees

Working conditions of call centers lead to serious mental problems in employees as well as affecting their working life quality due to factors such as excessive workload, complex and uncertain work environment, repetitive tasks, type of tasks that ignore notions of employees, benefiting from only a part of their skills, and poor labor inspection. This study aims to determine the general mental state and quality of working life of call center employees. The sample of this descriptive study consisted of 200 employees working at Sivas ‘ALONET’ call center between December 01, 2018, and December 31, 2018. We used the Personal Information Form, General Health Questionnaire-28 (GHQ-28), and Professional Quality of Life Scale (ProQOL) as data collection tools. We collected the data from the employees through face-to-face interviews during working hours. During the analysis, we used Pearson’s Product Moments Correlation Analysis, t-test, Mann Whitney U test, Kruskal Wallis H test, and Dunnet T3 Post Hoc test. We found that the mean score of the employees in GHQ-28 was 5.34 ± 5.60, and the compassion satisfaction, burnout, and compassion fatigue subscales of the ProQOL were 36.07 ± 9.88, 14.40 ± 7.74, and 13.89 ± 9.31, respectively. While the score of the employees from the overall GHQ-28 was significantly negatively correlated with the score from the compassion satisfaction subscales of the ProQOL, we found a significantly positive correlation between the scores of burnout and compassion fatigue subscales of the ProQOL. The findings of this study revealed that approximately 45.8% of the call center employees were mentally in the at-risk group, that their compassion satisfaction level was moderate, and that their burnout and compassion fatigue levels were low. Therefore, stress factors that employees facing in their working life should be determined, and training, counseling, and support programs should be carried out to prevent them from suffering from burnout and compassion fatigue.

Improvement of the Daily Management System for Sprinkler Systems

The maintenance and status of institutional maintenance of sprinkler facilities are examined to identify problems and suggest improvement plans for the daily maintenance system for sprinkler facilities over time. In addition, the details of poor inspection practices of sprinkler facilities installed in buildings in the Seoul metropolitan area and records prepared by the agency for fire safety management are analyzed. Suggested improvement measures include drafting legislation to establish maintenance guidelines, standardizing work on fire safety management, handling defective contents, strengthening education of business supervisors, and establishing a maintenance space to prevent corrosion and flooding.

Challenging Global Group Audits: The Perspective of US Group Audit Leads*

ABSTRACTRegulators are concerned about the quality of group audits because of poor inspection results, recent enforcement actions against component auditors, and the significance of these audits to the global economy. Yet research in this area is nascent. In response, we survey and interview US‐based global group audit leads to better understand the group audit process and their perceptions of challenges that arise on such engagements. Our findings indicate that group auditors organize their audits consistent with the client's structure, which drives component auditor selection, scoping, and fees. Group auditors routinely find fault with component auditors, perceiving that work performed and/or documentation provided is not sufficient, not appropriate and/or not communicated timely, to comply with US standards and reporting deadlines. In some cases, they perceive that the component auditor is unable and/or unwilling to comply and in others, that the component auditor misunderstands group instructions due to language proficiency or differing interpretation of the standards. Notably this perspective is ethnocentric, as group auditors almost exclusively attribute issues to component auditors. While ethnocentric tendencies appear myopic, they provide insight into unrecognized or overlooked aspects of the global firm model of cooperative arrangements. This study highlights the significance of the global firm's network structure to global group audits. Thus, we encourage intercommunity research, among (quantitative and qualitative) scholars and standard setters, to consider group audits in the broader context of firm networks.

Ball bonding inspections using a conjoint framework with machine learning and human judgement

Ball bonding inspections with human vision are essential in manufacturing processes of semiconductors devices and integrated circuits (ICs). The inspections are an intensive task which involves human labours to detect poor bonds. Prolonged visual inspections cause poor inspection integrity due to eye-fatigue. However, inspections nowadays are mostly conducted manually by humans which cannot satisfy the demanding productions. Motivated by the extraordinary performance of machine learning for manufacturing inspections, a detection framework integrated with machine learning and human judgement is proposed to aid bonding inspections based on visual images. The detection framework is incorporated with the convolution neural network (CNN), support vector machine (SVM) and circle hough transform algorithm (CHT); human judgement is only used when the detection uncertainty is below the threshold. The novel machine learning integration is proposed on the detection framework to improve the generalization capabilities. The CNN architecture is redeveloped by incorporating with the SVM which is generally more effective than the fully connected network in the classical CNN. Also a novel training function is proposed based on the CHT to ensure the inspection reliability; the function not only takes into account real image captures, but also locates important features using pattern analysis of the ball bondings. Experimental results show that significantly better classifications can be achieved by the proposed framework compared with the classical CNN and other commonly used classifiers. Only the machine learning determinations below the threshold are reassessed by human judgements.

Defect-Detection Model for Underground Parking Lots Using Image Object-Detection Method

The demand for defect diagnoses is gradually gaining ground owing to the growing necessity to implement safe inspection methods to ensure the durability and quality of structures. However, conventional manpower-based inspection methods not only incur considerable cost and time, but also cause frequent disputes regarding defects owing to poor inspections. Therefore, the demand for an effective and efficient defect-diagnosis model for concrete structures is imminent, as the reduction in maintenance costs is significant from a long-term perspective. Thus, this paper proposes a deep learning-based image object-identification method to detect the defects of paint peeling, leakage peeling, and leakage traces that mostly occur in underground parking lots made of concrete structures. The deep learning-based object-detection method can replace conventional visual inspection methods. A faster region-based convolutional neural network (R-CNN) model was used with a training dataset of 6,281 images that utilized a region proposal network to objectively localize the regions of interest and detect the surface defects. The defects were classified according to their type, and the learning of each exclusive model was ensured through test sets obtained from real underground parking lots. As a result, average precision scores of 37.76%, 36.42%, and 61.29% were obtained for paint peeling, leakage peeling, and leakage trace defects, respectively. Thus, this study verified the performance of the faster RCNN-based defect-detection algorithm along with its applicability to underground parking lots.

HPLC-UV and UPLC-MS/MS methods for the simultaneous analysis of sildenafil, vardenafil, and tadalafil and their counterfeits dapoxetine, paroxetine, citalopram, tramadol, and yohimbine in aphrodisiac products.

In recent times, the counterfeiting of pharmaceuticals has been considered a serious trouble especially in developing countries that acquire poor inspection programs. Sildenafil, vardenafil and tadalafil (phosphodiesterase type 5 inhibitors) products have gained wide popularity in treating sexual disorders, for which they are subjected to counterfeiting. For this purpose, a simple, rapid, and novel HPLC method with ultraviolet detection has been simply developed for the simultaneous determination of vardenafil, sildenafil, and tadalafil, and their counterfeits (dapoxetine, paroxetine, citalopram, tramadol and yohimbine) in pharmaceutical dosage forms and counterfeit products such as instant coffee and honey. The separation was carried out on a C18 column, with acetonitrile and an aqueous 0.05% formic acid solution as the mobile phase with a gradient program and at a flow rate of 1 mL min−1. UV detection was accurately set at 230 nm. The total run time was 11 min for elution of these eight drugs. A UPLC-MS/MS method was also developed, by which compounds were separated in only 6 min, and it was used as a confirmatory tool for studied compounds by identification of their mass spectra. Proposed methods were validated by following ICH guidelines. Both methods were found to be linear, specific, precise and accurate, and they were efficiently applied to analyze 50 commercial products including honey sachets, instant coffee and pharmaceutical products marketed as aphrodisiacs and suspected to contain PDE5-inhibitors.

Leveraging online review platforms to support public policy: Predicting restaurant health violations based on online reviews

Restaurant health inspections aim at identifying health violations and shall reduce the risk that restaurant visitors suffer from foodborne illness. Nevertheless, regulatory authorities' resources are limited, so an efficient mechanism that supports scheduling of health inspections is necessary. We build upon information efficiency theory and investigate whether information extracted from online review platforms is useful to predict restaurant health violations. Furthermore, we examine how the expectation disconfirmation bias impacts classification performance. Analyzing a large sample of health inspections, corresponding online reviews and restaurant visitor data, we propose and evaluate different predictive models. We find that classifiers specifically taking into account information from online review platforms outperform different baseline approaches. We thus show that online reviews encompass private information indicating strong information efficiency. Furthermore, we observe that the expectation disconfirmation bias has an influence on classification performance in case of restaurants with a low star rating and with a poor inspection history. An ensemble classifier can help to mitigate this influence. Thus, online review platforms contain relevant information to predict future health violations. Our results are highly relevant for regulatory authorities, restaurant visitors and restaurant owners.

How can patient experience scores be used to predict quality inspection ratings? A retrospective cross-sectional study of national primary care datasets in the UK

ObjectivesThe relationship between patient feedback in the General Practice Patient Survey (GPPS) and Care Quality Commission (CQC) inspections of practices was investigated to understand whether there is an association between...

Adventure or amusement? Image and identity challenges for the aerial adventure industry and implications for positioning and policy

ABSTRACT This paper seeks to address the changing image and identity of the aerial adventure industry as it becomes increasingly commercialized, which has led to uncertainty over its positioning within either adventure tourism or amusement rides. Such a positioning is critical in order to mitigate the problems caused by an inappropriate identification and image that contributes to poor inspections, poor procedures and policies, and ultimately, poor perceived risks and safety. In an industry where one serious injury impacts all operators, it is essential for all stakeholders to have collective “buy in” to effective policies that are standardized across the entire industry. The current identity confusion has merely led to misconceptions from public stakeholders. Through a qualitative case-study, this paper finds that aerial adventure parks share characteristics with adventure tourism and amusement rides and so resembles a hybrid. This is largely due to the presence of inherent risk and the role of the participant, both of which are less present on amusement rides. The paper therefore calls for state agencies to identify the activity as a stand-alone activity and for the subsequent regulations and policies to reflect this hybrid status.