Previous Inspection Research Articles

Continuous digester process safety improvements – Stress corrosion cracking and overpressure protection lessons learned and opportunities

Georgia-Pacific has recent experience with continuous digester stress corrosion cracking (SCC) repairs where the extent of SCC was more than previous spot inspections had predicted (one digester had anodic protection, one did not). This paper offers case studies of reviewed and improved digester inspections by use of “boat” samples to quantify the depth of cracking mechanism. Boat sample test data is used to support repair recommendations based on hardness testing. Georgia-Pacific also reviewed overpressure protection systems and corrected gaps found in these systems. These included updating to the latest original equipment manufacturer (OEM) designs and formalizing functional testing procedures and practices. This review of digester inspection, testing, and repairs since 2020 provides: (1) a basis for including a 3rd party corrosion expert to be a part of inspections; (2) removes the use of power-wire brushes; (3) enhances digester inspection with 100% phased-array ultrasonic testing to detect SCC; and (4) ensures overpressure protection design and testing is aligned with corporate needs and the site-specific challenges.

Testing for sequential bias in school inspections

ABSTRACT Inspectors are tasked with judging the quality of provision based on visits to schools. They conduct these inspections sequentially, completing one before moving on to the next. However, empirical research in a range of settings outside education suggests that prior judgements in a sequence can influence subsequent judgements, despite being logically irrelevant. We investigate whether school inspectors in England display such sequential bias by testing whether they judge similar schools differently, depending on the judgements they reached in prior inspections. We find only limited evidence of sequential bias in primary school inspections. In particular, an inspector reaching an ‘Inadequate’ judgement in their previous inspection is associated with a 42 per cent reduction in the odds of reaching another ‘Inadequate’ judgement in their next inspection. Only around 5 per cent of inspection judgements result in an ‘Inadequate’ and we do not find consistent evidence of sequential bias at other grades, meaning this bias only affects a small minority of judgements. We also do not find the same results for secondary schools, albeit in a much smaller sample.

Failure investigation of an AISI 316L pipe of the flare system in an off-shore oil platform

The oil and gas industries have severe operations conditions, which impose several failures in off-shore equipment. The Flare System is a pipe designed to safely dispose of relieving hydrocarbon gases and liquids during start-up, operational upsets, emergency shutdown, and maintenance activities. This work reported the failure investigation in a pipe of a high-pressure flare system. The pipe has a diameter of 220 mm and 4 mm of thickness, and according to the equipment datasheet, it was made in AISI 316L stainless steel. Two cracks were detected during the previous non-destructive inspection. Those failures were repaired, but another crack was detected after some months. Chemical composition was investigated using optical emission spectroscopy and elemental analysis, and the results are compatible with AISI 316L steel. The microstructure observed by light optical and scanning electron microscopes shows an austenitic matrix with deformation bands. The microhardness measurements and the XRD analysis show the martensite formation in those deformation bands. The tensile properties are in accordance with the standards. The fracture surface was investigated using scanning electron microscopy, the results point to fatigue failure.

Technologies and Platforms for Remote and Autonomous Bridge Inspection – Review

Recent scientific and technological advancements have enabled a more efficient structural condition assessment of bridges, mainly through the implementation of intelligent inspection strategies. These intelligent strategies can provide early identification of critically damaged components before failure, and will therefore play a key role in extending the life of infrastructure. The latest inspection technologies can provide inspection plans with damage conditions, create a damage report as well as provide statistics and comparisons to the previous inspection findings. The new and existing inspection technologies are directed to help with the digitalization of the Bridge Management System (BMS). The complexity of maintenance/inspection requires organized, automated, open and transparent digital processes, which should consider both—structure and asset management data. Further, the inspection/monitoring findings serve as a source for decision-making models. The digitalized aspects of autonomous inspection provide better performance prediction models and guarantee safety for the users. This paper presents the latest findings in the field of remote inspection of bridges. In particular, the main technologies for inspection and geometrical assessment are depicted, especially those based on computer vision systems installed in UAVs and robots, LiDAR, radar, satellites and other non-contact systems including on-board monitoring. The accompanying article entitled: “Methodologies for remote bridge inspection” deals with the methodologies used for data processing based on Artificial Intelligence (AI).

Inspection, Testing and Examination System at Gangsa 2 Laboratory at Uitm Bukit Besi

Examination and testing of the (LEV) system was conducted at the Gangsa 2 laboratory of (UiTM) Kampus Bukit Besi to evaluate performance and compliance with safety standards. The inspection aimed to obtain data on airflow, static pressure, and velocities at key points in the LEV system, focusing on a single fume hood (FH-GANGSA 2). Methodology followed guidelines from the (ACGIH) and adhered to (USECHH) 2000 Regulations. Face velocities were measured at different sash heights, and duct transport velocities were assessed. Smoke tests were performed to visually evaluate containment efficiency. Instruments used included a hot wire anemometer, pitot tube, digital manometer, and smoke tester kit. Results showed the fume hood had an average face velocity of 117.8 fpm at the recommended working height of 19.7 inches, within the 80-100 fpm range specified by ACGIH. Duct transport velocity was 3102 fpm, exceeding the recommended 1000-2000 fpm range. The smoke test demonstrated excellent containment with smooth inflow and no escape of smoke outside the hood. A comparison with the previous year's inspection revealed a slight decrease in performance, with face velocity dropping from 109 fpm to 88.5 fpm. While for the flowrate, there is a notable increase in the flow rate from 2023 to 2024. The LEV system was found to meet acceptable working conditions based on ACGIH standards and comply with (USECHH) 2000 Regulations. Recommendations included designing the system by a registered professional engineer as stipulated in (USECHH) Regulation 2000, continuing monthly performance monitoring, and improving accessibility to the centrifugal fan for more comprehensive measurements. This examination helps establish baseline measurements for ongoing monitoring of the LEV system's effectiveness in protecting worker health and safety in laboratory settings.

Imperfect maintenance modelling and estimation for interval-censored data

The paper proposes a new maintenance model for a repairable system. The system is inspected at deterministic times. If a failure occurs between two successive inspections, a minimal repair is performed. At inspection times, two types of maintenance actions can be performed. The first originality of the paper is to consider that the choice between these two types of maintenance depends on the fact that there has been a failure or not since the previous inspection. If no failure has occurred since the last inspection, the maintenance, simply denoted PM, corresponds to a basic preventive maintenance action, whose effect is planned by the initial maintenance plan. If at least one failure has occurred since the last inspection, the maintenance, denoted PCM, includes both the preventive action planned by the initial maintenance plan, and a specific corrective action which aims to fix the defects that have happened since the last inspection. Both types of maintenances are assumed to be imperfect, of the ARA (Arithmetic Reduction of Age) type, with different parameters. Moreover, the paper derives several statistical inference methods for this model. The usual case of complete data is first considered. The second originality of the paper is to analyse interval-censored data in this imperfect maintenance framework. This situation holds when failure times are not observed, and the only information is the fact that failures have occurred or not between two inspections. The quality of the proposed methods is assessed on simulated data. Finally, a real dataset from a gas transmission system operator is provided and analysed with the proposed approach.

Assessment of the suitability of degradation models for the planning of CCTV inspections of sewer pipes

ABSTRACT The degradation of sewer pipes poses significant economical, environmental and health concerns. The maintenance of such assets requires structured plans to perform inspections, which are more efficient when structural and environmental features are considered along with the results of previous inspection reports. The development of such plans requires degradation models that can be based on statistical and machine learning methods. This work proposes a methodology to assess their suitability to plan inspections considering three dimensions: accuracy metrics, ability to produce long-term degradation curves and explainability. Results suggest that although ensemble models yield the highest accuracy, they are unable to infer the long-term degradation of the pipes, whereas the Logistic Regression offers a slightly less accurate model that is able to produce consistent degradation curves with a high explainability. A use case is presented to demonstrate this methodology and the efficiency of model-based planning compared to the current inspection plan.

Pseudomonas Infection Outbreak Associated with a Hotel Swimming Pool - Maine, March 2023.

Treated recreational water venues (e.g., pools and hot tubs) located at hotels represent one third of sources of reported treated recreational water-associated outbreaks; when these outbreaks are caused by Pseudomonas aeruginosa, they predominantly occur during January-April. On March 8, 2023, the Maine Center for Disease Control and Prevention (Maine CDC) initiated an investigation in response to reports of illness among persons who had used a swimming pool at hotel A during March 4-5. A questionnaire was distributed to guests who were at hotel A during March 1-7. Among 35 guests who responded, 23 (66%) developed ear pain, rash, or pain or swelling in feet or hands within days of using the pool during March 4-5. P. aeruginosa, a chlorine-susceptible bacterium, was identified in cultures obtained from skin lesions of three patients; a difference of two single nucleotide polymorphisms was found between isolates from two patients' specimens, suggesting a common exposure. Hotel A management voluntarily closed the pool, and Maine CDC's Health Inspection Program identified multiple violations, including having no disinfectant feeder system, all of which had been identified during a previous inspection. Because chlorine had been added to the pool water after the pool was voluntary closed, environmental samples were not collected. The pool remained closed until violations were addressed. Health departments can play an important role in reducing the risk for outbreaks associated with hotel pools and hot tubs. This reduction in risk can be achieved by collaborating with operators to ensure compliance with public health codes, including maintaining chlorine concentration and otherwise vigilantly managing the pool, and by disseminating prevention messages to pool and hot tub users.

Investigating the use of machine learning algorithms to support risk-based animal welfare inspections of cattle and pig farms.

In livestock production, animal-related data are often registered in specialised databases and are usually not interconnected, except for a common identifier. Analysis of combined datasets and the possible inclusion of third-party information can provide a more complete picture or reveal complex relationships. The aim of this study was to develop a risk index to predict farms with an increased likelihood for animal welfare violations, defined as non-compliance during on-farm welfare inspections. A data-driven approach was chosen for this purpose, focusing on the combination of existing Swiss government databases and registers. Individual animal-level data were aggregated at the herd level. Since data collection and availability were best for cattle and pigs, the focus was on these two livestock species. We present machine learning models that can be used as a tool to plan and optimise risk-based on-farm welfare inspections by proposing a consolidated list of priority holdings to be visited. The results of previous on-farm welfare inspections were used to calibrate a binary welfare index, which is the prediction goal. The risk index is based on proxy information, such as the participation in animal welfare programmes with structured housing and outdoor access, herd type and size, or animal movement data. Since transparency of the model is critical both for public acceptance of such a data-driven index and farm control planning, the Random Forest model, for which the decision process can be illustrated, was investigated in depth. Using historical inspection data with an overall low prevalence of violations of approximately 4% for both species, the developed index was able to predict violations with a sensitivity of 81.2 and 79.5% for cattle and pig farms, respectively. The study has shown that combining multiple and heterogeneous data sources improves the quality of the models. Furthermore, privacy-preserving methods are applied to a research environment to explore the available data before restricting the feature space to the most relevant. This study demonstrates that data-driven monitoring of livestock populations is already possible with the existing datasets and the models developed can be a useful tool to plan and conduct risk-based animal welfare inspection.

Satellite-derived digital surface models to improve geolocation of InSAR deformation measurements on bridges

Canadian bridges need structural health monitoring (SHM) to ensure their safety and longevity. In-situ inspections are expensive and time-consuming, and climate change effects on river bridges may make previous legacy inspection schedules inadequate. Remote sensing can help supplement in-situ inspections or alert bridge operators to the need for a new inspection. Satellite-based Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) is an emerging approach for bridge deformation monitoring that requires accurate geolocation of the PS targets for best results. If the as-built elevations on bridges are insufficiently accurate, geolocation errors may limit the usefulness of the PS-InSAR data for bridge SHM. Satellite stereo imagery can be used to derive Digital Surface Models (DSMs), which give the elevation of the top surfaces of structures. It is unknown whether DSMs derived from satellite imagery are adequate as height sources for ensuring accurate positioning of PS-InSAR targets when bridge drawings or surveys are unavailable or unreliable. A study evaluating PS-InSAR elevation corrections using a DSM from satellite imagery was conducted on the Samuel de Champlain Bridge in Montreal (QC), Canada. A tri-stereo image triplet was used to create a 1 m resolution DSM, which was evaluated for height accuracy against a historical survey and used to correct PS-InSAR data points for elevation. The PS-InSAR dataset was georeferenced with DSM height corrections and evaluated for accuracy.

Positioning Secondary School Teachers as Passive Actors During School Quality Assurance Policy Implementation in Dodoma, Tanzania

While the impact of the School Quality Assurance (SQA) policy on students’ learning outcomes is well-documented, there exists a limited understanding of how its implementation influences teachers’ power and identity. This study analysed how teachers were positioned during the SQA policy implementation in four purposefully selected secondary schools in Dodoma Region, Tanzania. Data gathered from 19 participants, including heads of schools, department heads, subject teachers, WEOs, and SQAOs through policy documents and semi-structured interviews, unveil a critical observation. Despite teachers being pivotal to school functioning, the SQA policy positions them as passive objects lacking the knowledge to evaluate school quality based on proposed domains. This study contends that such positioning aligns with previous top-down school inspection models, which had limited impact on enhancing secondary school quality. To address this, the study suggests a radical shift towards teacher empowerment through collegial supervision rather than conventional external quality assurance visits.

Non-destructive evaluation (NDE) methods used for condition assessment of concrete structures

As an integral part of the construction industry, concrete is an important part of the building materials industry, as well as an important material for infrastructure and engineering construction projects. It is widely used in various industries. In recent years, with the improvement of the quality awareness of the whole people, the engineering quality inspection has also received more attention. The previous concrete quality inspection methods have different degrees of damage to the original structure. This paper focuses on the state of concrete structure and its detection method, and introduces the EDN technology. At the same time, through comparison, practical experiment, theoretical research and other research methods to describe EDN technology. Based on the research results, it is concluded that ECN technology can provide a more comprehensive concrete quality report, which is beneficial to the maintenance, repair and restoration of Bridges and old buildings, and help engineers use.

Reliability-based fatigue inspection planning for mooring chains of floating systems

The safety of offshore floating units, supporting renewable energy generation or oil and gas production facilities, highly depends on the platform's station keeping capability, which is provided by mooring systems. Lately, there has been a high rate of mooring line failures, leading to increased interest in improving their design and maintenance methodologies. Specifically, upper chain segments at the splash zone are more prone to suffer from the effects of corrosion degradation and fatigue. The fatigue limit state and the corrosion allowance of mooring chains are safety requirements that need to be verified continuously through mandatory regular inspections. Current trends related to mooring chains point to the development of new methodologies for fatigue damage calculation, corrosion degradation models and inspection planning methods. The present work proposes a practical reliability-based method for planning future inspections of such mooring line components, which combines recently-developed fatigue and corrosion methodologies and takes into account the results of the previous inspections. The risk-based assessment of the mooring system is continuously updated with new inspection data, and is a suitable tool for monitoring the current reliability level of the structure. This work presents a case study of a FPSO on the Brazilian coast as a practical example of the application of the proposed inspection planning method. In this way, it aims to help future designers and operators to avoid failures in mooring chains and to optimizing inspection and maintenance costs.

Investigation of the unusual deformations of external tendons in concrete highway bridges

AbstractDuring a regular inspection of a multi‐span highway bridge, an unusual deformations of external post‐tensioned tendons were observed. These deformations were not noticed or recorded during the previous main inspections. A detailed visual inspection of the condition of external tendons was performed on all bridges with external tendons by mapping the location and extent of unusual deformation. Based on this, several non‐destructive tests were performed, where deformations were first noticed, from magnetic‐flux leakage to radiographic screening. Some local destructive investigations were subsequently carried out at several locations in two bridges, i.e. opening the tendons. The result of the tendons opening showed the presence of foreign objects and non‐tensioned strands, the latter being a possible cause for the deformations of that tendon. In both locations, the injection mass was compact with only hairy transverse cracks, both on the undeformed part as well as on the moderately deformed part of the tendon. In this paper, the latest results from this study are presented in detail.

A BAYESIAN DATA UPDATING MODEL FOR MANAGING BUILDING ENVELOPE INSPECTIONS

Building envelope management includes all activities, e.g., periodic inspections, condition assessment and maintenance, needed to preserve the integrity of the building exterior. In building envelope management, appropriate decision-making focuses on finding an optimum inspection interval and repair strategy. A Bayesian updating with rating method is presented for conducting planning and decision-making for building envelop inspections using data from previous inspections. The model applies to a zone (strip) of the envelope and uses the previous inspection records to predict the future condition. An artificial neural network (ANN) is used to improve the prediction for the envelope condition. The ANN uses historical data of an envelope condition to determine a correction factor, for the predicted condition, to account for the effect of factors that promote damage. The Bayesian updating will improve the capabilities of the model in predicting the future conditions as more data becomes available. Used along with a cost optimization and reserve analysis, the model will result in a more sustainable and cost-effective building envelope management process.

Evaluating remote post-mortem veterinary meat inspections on pig carcasses using pre-recorded video material

BackgroundOfficial meat inspections at small-scale slaughterhouses and game-handling establishments in geographically remote areas place a heavy burden on the meat-producing food business operators. By performing meat inspections remotely using live-streamed video, instead of on-site, the official control could meet the goals of sustainability, resilience and logistics. We investigated the agreement between the two approaches at pig slaughter. Two official veterinarians (OVs) inspected 400 pig carcasses at a Swedish slaughterhouse, with each pig being inspected on site by one OV and remotely by the other. After a period of 3 to 6 months, video recordings of the remote inspections were assessed again by the same OVs, thus enabling direct comparisons of previous on-site inspections and renewed video-based inspections within the same OV.ResultsAgreement across 22 finding codes was generally very high for both OVs. In all but one case (whether to fully condemn a carcass), for both OVs, Prevalence-Adjusted Bias-Adjusted kappa was well above 0.8, indicating ‘almost perfect agreement’.ConclusionsThis study supports earlier findings that reliable post-mortem inspections can be performed using video, and indicates higher agreement between remote and on-site inspections if the same OV performs both.

Intelligent ship inspection analytics: Ship deficiency data mining for port state control

To provide safe, secure, and environment-friendly maritime transport, it is an essential point to define and take appropriate countermeasures against the substandard ships in maritime transport. Port State Control (PSC) is one of the critical inspection regimes recognized worldwide to define substandard ships. However, one of the biggest challenge faced in ship inspection is the necessity of checking many items through limited time and qualified human resources. Therefore, in this study, it is aimed to develop a decision support system to increase the effectiveness of ship inspections. In accordance with this, we proposed an intelligent ship inspection analytics (I-SIA) model based on the Knowledge Discovery in Database (KDD) process by utilizing fuzzy c-means clustering and apriori algorithms. The I-SIA model provides the determination of ship deficiency patterns based on specific ship attributes, and subsequently predicts main/sub inspection items to be focused on through former inspection records of the ship to be inspected. In the case study performed for a selected ship, I-SIA predicts critical 5 main and 23 sub-inspection items that need to be checked with high priority, among 17 main inspection items and out of more than 500 sub-inspection items. Herewith, I-SIA provides a ship-specific and dynamic ship inspection based on deficiencies recorded in previous inspections and detected during inspection. Thus, it becomes possible to enhance the effectiveness of the inspection via defined deficiency patterns.

Digital Fringe Projection-Based Clamping Force Estimation Algorithm for Railway Fasteners

The inspection of railway fasteners to assess their clamping force can be used to evaluate the looseness of the fasteners and improve railway safety. Although there are various methods for inspecting railway fasteners, there is still a need for non-contact, fast inspection without installing additional devices on fasteners. In this study, a system that uses digital fringe projection technology to measure the 3D topography of the fastener was developed. This system inspects the looseness through a series of algorithms, including point cloud denoising, coarse registration based on fast point feature histograms (FPFH) features, fine registration based on the iterative closest point (ICP) algorithm, specific region selection, kernel density estimation, and ridge regression. Unlike the previous inspection technology, which can only measure the geometric parameters of fasteners to characterize the tightness, this system can directly estimate the tightening torque and the bolt clamping force. Experiments on WJ-8 fasteners showed a root mean square error of 9.272 N·m and 1.94 kN for the tightening torque and clamping force, demonstrating that the system is sufficiently precise to replace manual measurement and can substantially improve inspection efficiency while evaluating railway fastener looseness.

Towards deep computer vision for in-line defect detection in polymer electrolyte membrane fuel cell materials

Polymer Electrolyte Membrane (PEM) fuel cells are a promising source of alternative energy. However, their production is limited by a lack of well-established methods for quality control of their constituent materials like the membrane-electrode assembly during roll-to-roll manufacturing. One potential solution is the implementation of deep learning methods to detect unwanted defects through their detection in scanned images. We explore the detection of defects like scratches, pinholes, and scuffs in a sample dataset of PEM optical images using two deep learning algorithms: Patch Distribution Modeling (PaDiM) for unsupervised anomaly detection and Faster-RCNN for supervised object detection. Both methods achieve scores on performance metrics (ROC-AUC and PRO-AUC for PaDiM and AP for Faster-RCNN) that are comparable to their scores on benchmark datasets. These methods also have the potential to detect a wider range of defects compared to IR thermography and previous optical inspection methods. Overall, deep learning shows promise at detecting relevant defects of interest and has the potential to achieve real-time defect detection.

Do pupils at research-informed schools actually perform better? Findings from a study at English schools

IntroductionAcross the globe, many national, state, and district level governments are increasingly seeking to bring about school “self improvement” via the fostering of change, which, at best, is based on or informed by research, evidence, and data. According to the conceptualization of research-informed education as inquiry cycle, it is reasoned that there is value in combining the approaches of data-based decision-making and evidence-informed education. The originality of this paper lies in challenging common claims that teachers’ engagement with research supports development processes at schools and pupil performance.MethodsTo put this assumption to test, a data-set based on 1,457 staff members from 73 English primary schools (school year 2014/2015) was (re-)analyzed in this paper. Not only survey information about trust among colleagues, organizational learning and the research use climate was used (cf. Brown et al., 2016), but also the results from the most recent school inspections and the results from standardized assessment at the end of primary school. Of particular interest was, as to whether the perceived research use climate mediates the association between organizational learning and trust at school on the one hand and the average pupil performance on the other, and whether schools that were rated as “outstanding,” “good,” or “requires improvement” in their most recent school inspection differ in that regard. Data was analyzed based on multi-level structural equation modelling.ResultsOur findings indicate that schools with a higher average value of trust among colleagues report more organizational and research informed activities, but also demonstrate better results in the average pupil performance assessment at the end of the school year. This was particularly true for schools rated as “good” in previous school inspections. In contrast, both “outstanding” schools and schools that “require improvement” appeared to engage more with research evidence, even though the former seemed not to profit from it.DiscussionThe conclusion is drawn that a comprehensive model of research-informed education can contribute to more conceptual clarity in future research, and based on that, to theoretical development.