Risk-based Inspection Research Articles

Defect Trends in Fire Alarm Systems: A Basis for Risk-Based Inspection (RBI) Approaches

This article presents a comprehensive statistical evaluation of defect frequency in fire alarm systems under real operating conditions, focusing on risk-based factors. The aim is not to introduce a complete RBI approach but rather to assess defect trends that can inform future RBI-based inspection strategies. The study categorizes and evaluates defects by frequency, particularly examining components such as cable and wire systems, acoustic signal devices, and the impact of detector contamination. These findings establish a foundation for developing tailored risk-based inspection and predictive maintenance strategies. A three-stage explanatory research design was employed, analyzing 4629 inspection reports with findings verified through expert surveys and cross-sample analysis. Results indicate that certain components, including acoustic devices and detectors, exhibit a significant increase in defects after 10 years, especially under challenging environmental conditions. Additionally, while ring bus technology supports less frequent functional testing, cable and wire systems require heightened attention in the early operational years. The study also identifies statistically significant trends and their potential for application to a broader system population, supporting enhanced RBI-based maintenance practices. These insights contribute to refining current maintenance approaches and offer practical recommendations for optimizing inspection routines based on risk factors. The article does not propose a system overhaul but lays essential groundwork for further research and improvement in fire alarm system reliability through targeted, risk-informed practices.

Efficient risk-based inspection framework: Balancing safety and budgetary constraints

Efficient equipment maintenance is paramount across various industries to mitigate energy wastage and avert potential disasters such as hazardous emissions, fires, and explosions. Within this context, the adoption of risk-based inspection strategies has emerged as a crucial method for assessing equipment integrity. This study integrates the principles of Risk-Based Inspection (RBI) with a novel two-objective mathematical model, resulting in a comprehensive framework for equipment inspection programs. The primary aim of this framework is to reduce overall risk exposure while optimizing inspection expenditures. Unlike conventional approaches, this methodology eliminates the necessity to define threshold risk levels. By integrating inspection costs into the model, the assessment of Failure Consequences, and thereby, the decision-making process has been streamlined. This innovative algorithm effectively balances the reduction of failure likelihood with the minimization of inspection costs, enhancing decision-making capabilities. Importantly, this approach offers significant protection against energy wastage and the occurrence of leaks through robust risk management strategies. The algorithm employs specialized operators to expedite the discovery of optimal solutions. Empirical validation through a case study conducted at a Petrochemical Plant highlights the practicality and effectiveness of the proposed framework.

Development of Risk-Based Methodologies for Highway Pavement Construction Inspection

Construction inspection plays a critical role in Portland cement concrete pavement (PCCP) and hot mix asphalt (HMA) pavement projects. To ensure the quality of pavement projects, state departments of transportation (DOTs) in the U.S. typically conduct construction material tests and inspect workmanship processes using quality assurance (QA) programs. Given today’s reality of reduced QA inspection resources such as funding, staff ability, and availability, several state DOTs have used a risk-based inspection (RBI) approach to optimize their QA processes. A thorough review of existing literature revealed limited studies examining RBI methodologies in highway pavement construction projects. The aim of this study is to investigate the use of RBI for pavement construction projects. It employed a triangulation research methodology, including an extensive literature review, a survey questionnaire of 50 state DOTs, focus groups, and a vetting workshop. A list of 16 core items in field inspection for both PCCP and HMA pavement projects was developed and verified. A generic risk rating of these critical inspection activities was determined. Additionally, inspection frequency, documentation effort, and inspector experience associated with these critical inspection activities identified through focus groups and the vetting workshop served as a reference point when conducting RBI for PCCP and HMA pavement projects. This study contributes to the body of knowledge and the highway construction industry by identifying and prioritizing 16 core inspection items for PCCP and HMA pavement construction. Construction project managers and inspectors may benefit from this study by analyzing the risks of core inspection items and implementing appropriate risk mitigation plans.

Innovative risk management techniques in food safety: A review of best practices and emerging trends

Effective risk management in food safety is critical to ensuring consumer protection and maintaining the integrity of global food supply chains. Traditional approaches, such as Hazard Analysis and Critical Control Points (HACCP) and Good Manufacturing Practices (GMP), have long been the foundation of food safety risk management. However, with the increasing complexity of food production and distribution, new challenges demand more advanced techniques. This review explores innovative risk management methods, focusing on best practices and emerging trends that are transforming food safety. Technological advancements are driving much of this innovation. Predictive analytics and machine learning now allow for real-time monitoring and hazard detection, improving the accuracy of risk assessments. Blockchain technology is being leveraged for enhanced traceability and transparency in the supply chain, ensuring faster recall management and improved accountability. The integration of IoT (Internet of Things) sensors provides continuous monitoring of environmental factors like temperature and humidity, which are critical in preventing contamination, especially in perishable goods. Additionally, risk-based inspection systems, supported by regulatory frameworks such as the Food Safety Modernization Act (FSMA), are becoming more prevalent, prioritizing resources where risks are highest. Emerging trends such as artificial intelligence (AI) for predictive risk management, big data analytics, and sustainability-driven risk approaches are reshaping the landscape. These innovations not only enhance risk mitigation but also align with consumer demands for greater transparency and environmental responsibility. Despite challenges such as cost, data privacy, and regulatory hurdles, the future of food safety lies in the continued development and adoption of these advanced techniques. This review highlights the potential of these innovative practices to improve food safety standards and protect public health in an increasingly interconnected world. Keywords: Innovative, Risk Management, Food Safety, Review.

Machine learning-aided risk-based inspection strategy for hydrogen technologies

Although technically challenging, effective, safe, and economical transport is crucial for enabling a widespread rollout of hydrogen technologies. A promising option to transport large amounts of hydrogen lies in employing retrofitted natural gas pipelines. Nevertheless, H2-rich environments tend to degrade pipeline steels, reducing their load-bearing capability and accelerating crack propagation. Regular inspection and maintenance activities can preserve the pipelines’ integrity and guarantee safe operations. The risk-based inspection (RBI) approach is based on estimating the risk for each component item. It focuses most inspection activities on high-risk components to reduce costs while maximizing the plant’s safety and availability. However, the RBI standards do not consider hydrogen-induced degradations and cannot be adopted for industrial equipment operating in H2 environments. This study proposes a novel ad-hoc methodology for the risk-based inspection planning of hydrogen handling equipment. A machine-learning model to predict the fatigue crack growth in gaseous hydrogen environments is developed and integrated with the conventional RBI approach. The proposed methodology is validated on three pipelines transporting hydrogen and natural gas in different concentrations. The results show how similar operating conditions can determine different degradation rates depending on the environment and highlight how hydrogen-enhanced fatigue can reduce the pipelines’ lifetime.

Risk factors affecting the food safety risk in food business operations for risk-based inspection: A systematic review.

Foodborne illnesses result in a high disease burden worldwide, making food safety control of food business operations (FBOs) an urgent issue. With public agencies and FBOs facing challenges in monitoring the complex food supply chain with limited resources, scientific and objective insights into those factors that are related to food safety at FBOs are needed. These factors can be used as input for risk-based inspection. We conducted a systematic review to identify and analyze risk factors affecting the FBOs' food safety risk. We used a set of predefined search strings in Scopus and Web of Science to search for scientific manuscripts published in the English language between January 1 2003 and February 1 2023. The review identified 53 relevant studies and 43 risk factors. The presence of certified personnel turned out to be the most cited factor. Nearly half of the extracted factors had only been investigated in one study. Additional challenges were identified for developing a universal ready-to-use list of factors for the building of a risk-based inspection method, such as the limitation in the applicability of identified factors in different types of FBOs, and the variability in conclusions between publications for certain factors (e.g., FBO location and inspection history), stressing the need for additional research. Future studies should also prioritize standardizing definitions and measurements, particularly regarding compliance factors. In general, the current list of factors brought forward in our review lays the groundwork for building a transparent, objective, and risk-based method for food safety inspections of FBOs.

Inspection Planning of Subsea Pipeline Using Risk-Based Inspection

Inspection Planning of Subsea Pipeline Using Risk-Based Inspection

Water transport and corrosion under insulation: Experimental investigations of drying in mineral wool

Corrosion under insulation (CUI) is a major risk factor for the material integrity of insulated equipment that is encountered i.a. in the chemical process industries. Undetected CUI has been the root cause for major accidents, including loss-of-site, loss-of-life, prolonged plant shutdown, and environmental pollution. Reducing CUI risk via labour-intensive inspection routines is associated with billions of dollars of annual maintenance costs worldwide. CUI is caused by water bypassing a weather shield, migrating through the insulation and contacting the equipment surface. The resulting corrosion rates are very high and the process is hidden from view. Surprisingly, the fundamental mechanisms that drive water migration through insulation have been largely unexplored. In this work we present novel experimental investigations of the evaporation and drying dynamics for a heated vertical pipe with preformed mineral wool insulation and metal cladding. Relative humidity and temperature sensors provide measurements of how the water evaporates and travels through the system during controlled water ingress experiments. The experimental data shows that the drying time of the insulation material is proportional to the amount of water added. We find that the drying process proceeds in two stages, where the length of the first drying regime is directly related to the diffusion-limited evaporation from the bulk liquid water, while the second drying time is influenced also by other factors such as the adsorption and desorption of water on the insulation material fibres. The findings have implications for how time of wetness, used in CUI risk-based inspection methods, can be obtained from humidity sensor data. The new insights into water transport in insulation enables a paradigm shift towards predictive CUI maintenance using humidity spot sensors.

Integrated Application of Dynamic Risk-Based Inspection and Integrity Operating Windows in Petrochemical Plants

Integrated Application of Dynamic Risk-Based Inspection and Integrity Operating Windows in Petrochemical Plants

A framework for implementing structural integrity management of an aging fixed offshore platform using wave modeling for risk-based underwater inspection provision

Offshore platforms have become lucrative in the oil and gas industries due to their capabilities, which are exposed to harsh environmental hazards that affect the Structural Integrity.The scope of this study represents a risk assessment strategy for evaluating the structural integrity and proposed a framework for the Risk-Based Underwater Inspection to implement Structural Integrity Management for a fixed offshore jacket platform.The jacket platform is adopted for the Non-linear Quasi-Static Analysis (Push-over) using relevant code criteria. Regarding the required site-specific meteorological and oceanographic parameters, wave is simulated using WAVEWATCH III, third-generation version 4.18, considered input data and uncertain parameters.The Long-Term Load Distribution parameters are localized to determine the Probability of Failure due to the extreme wave loading action, and the platform structural damage rate is obtained by referring to the latest available underwater inspection carried out in the understudied oilfield. Moreover, the platform's structural failure may lead to safety, environmental, and economic impacts, which are the essential factors for evaluating the Consequence of Failure.Finally, the risk level is estimated by ranking the Probability and Consequences of Failure in the risk matrix, and resulting from the risk analysis, the framework for the Risk-Based Underwater Inspection is carried out.

Costs and benefits of a risk-based PCAOB inspection regime

We investigate the costs and benefits of the PCAOB's risk-based inspection regime by studying how auditors respond to engagement-level inspection risk. Using an established inspection selection model, we find evidence that auditors behave consistent with accountability theory when auditing clients with elevated ex-ante inspection risk. Specifically, we observe an increased propensity to report material weaknesses, a decreased propensity to assert that previous material weaknesses have been remediated, increased audit effort, and a decreased likelihood of subsequent financial statement restatement. In general, these outcomes reflect auditors' attempts to minimize negative inspection outcomes in a way that could be beneficial to investors. However, auditors' apparent focus on relative inspection risk also creates potential costs for investors as evidenced by an increased likelihood of resignation from the client and inattention to clients with relatively lower inspection risk when auditor resources are most constrained. Importantly, several tests provide evidence that auditors' response to inspection risk is distinct from and incremental to their response to misstatement risk. Overall, our findings suggest that there are potential benefits and potential costs associated with auditors' responses to a selection approach that is primarily risk-based.

The case for integrated IMR programs: increasing operational efficiency with a holistic approach that reduces risk and carbon emissions

This paper discusses the positive impact of adopting an integrated, customisable inspection, maintenance, and repair (IMR) approach when addressing subsea assets. Primary focus and expenditure are typically committed to the design, planning, and engineering of infrastructure. Of paramount importance, but often overlooked, are the ongoing health checks required of the subsea assets. These activities are regularly left until an issue is found, production is affected, or subsea asset data is required to enable decision making to ensure the lowest level of risk and maximise uptime. How the operations and maintenance approach used for subsea assets is best addressed using an integrated philosophy that streamlines not only contract management, but the collection and analysis of data is detailed. By supporting a more holistic view of assets not only by field, but regionally and globally, operators are best positioned to apply advanced technologies including machine learning for the development of risk-based inspection programs that provide insight into potential failures and anomalies. Building and adopting truly optimised IMR programs also reduces vessel days by leveraging already deployed assets, advanced technologies, and remote piloting to complete varied inspection tasks simultaneous to other ongoing operations, lowering emissions and risk associated with mobilisations and personnel on board. The impact of optimised inspection planning on users’ in-house resources is detailed. With internal experts dealing with IMR planning, analysis, and data management often stretched, adopting this forward-thinking approach to IMR has the potential to alleviate the burden associated with targeted planning and clearing backlogs that manage risk.

Framework for Integrating the Reliability of the Inspection Technique into Risk-Based Inspection Practice

In compliance with recent (2022) federal legislation found in the Code of Federal Regulations Part 650 Subpart C, risk-based inspection (RBI) practices for determining inspection intervals for highway bridges are now permitted. The RBI approach allows for a systematic assessment of risk and prioritizes inspection resources where they are most needed. However, to ensure an optimal level of safety and serviceability, it is necessary to utilize reliable inspection techniques in conjunction with an appropriate inspection interval, as emphasized by National Cooperative Highway Research Program (NCHRP) Report 782. Utilizing ineffective inspection techniques may lead to uncertainty about the element’s condition and thus increase the risk associated with the component. Therefore, this paper proposes a framework that integrates a new factor, called the inspection effectiveness factor (IEF), into the RBI process to rationally estimate the inspection interval considering the reliability of the inspection technique. The inspection interval in the proposed approach is determined based on the damage mode, associated likelihood, consequences, and effectiveness of the inspection technique, offering a rational approach for decision-makers in estimating the inspection interval of bridges and identifying appropriate inspection techniques for different defects. The effectiveness of the proposed method is validated through experts’ judgment. In addition, the proposed approach is demonstrated through a case study utilizing historical inspection reports.

Quantitative research on stress failure risk assessment for girth welds with unequal wall thickness of the X80 pipeline under lateral load

The cumulative length of the X80 steel pipeline extends to 17,000 km, with a predominant portion traversing mountainous terrains characterized by intricate geological formations. Recurrent geological hazards significantly impede the safe operation of oil and gas pipelines. Landslides can induce substantial axial forces on steel pipelines, significantly increasing the risk of pipeline failure. Moreover, as a result of sudden changes in mechanical structure, the pipeline's unequal wall thickness leads to local stress concentration, making it more vulnerable to external loads than the steel pipeline itself. Therefore, to analyze the mechanical response of steel pipelines under different working conditions, we establish a smooth particle hydrodynamics, finite element transverse landslide pipeline model for girth welds with unequal wall thickness and investigate a tearing accident caused by a lateral landslide in a section of the large-diameter natural gas pipeline. The landslide volume is found to be the main controlling factor for the failure of pipeline circumferential welds. According to the genetic algorithm backpropagation neural network, the von Mises stress of the most dangerous section of the pipeline is calculated based on pipeline and landslide parameters, and the error is 11.67 %. According to the API RP 581–2016 Risk-Based Inspection Methodology, a failure risk assessment matrix is established using the pipeline statute as an evaluation indicator to evaluate the landslide risk to pipelines in different regions. A variable-wall-thickness girth weld on the ZG pipeline was selected for application. This method has good applicability for evaluating the failure risk of circumferential welds with wall thicknesses of 12.8/15.3 mm under the action of small landslides.

Reliability Analysis of Electric Vehicle Charging Station Using Risk-Based Inspection Method and Reliability Block Diagram

Due to the increasing market demand for electric vehicles, particularly electric cars, the infrastructure for electric vehicles is also being fulfilled. This research utilized two methods: Risk Based Inspection and Reliability Block Diagram. Through the Risk Based Inspection method, a Risk Priority Number was obtained which identifies critical points in the Electric Vehicle Charging Station (EVCS). The Block Diagram method established relationships between interconnected subsystems. The research findings indicate that the critical point of the EVCS lies within the Body subsystem. This discovery significantly contributes to raising awareness about EVCS, ensuring that stakeholders entering the EVCS business have a clearer understanding and knowledge of the system.

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.

Probabilistic inspection planning for fatigue cracks in floating production, storage, and offloading units

Probabilistic inspection planning for fatigue cracks in floating production, storage, and offloading units

Fault detection of a wind turbine generator bearing using interpretable machine learning

Introduction: During its operational lifetime, a wind turbine is subjected to a number of degradation mechanisms. If left unattended, the degradation of components will result in its suboptimal performance and eventual failure. Hence, to mitigate the risk of failures, it is imperative that the wind turbine be regularly monitored, inspected, and optimally maintained. Offshore wind turbines are normally inspected and maintained at fixed intervals (generally 6-month intervals) and the program (list of tasks) is prepared using experience or risk-reliability analysis, like Risk-based inspection (RBI) and Reliability-centered maintenance (RCM). This time-based maintenance program can be improved upon by incorporating results from condition monitoring involving data collection using sensors and fault detection using data analytics. In order to properly carry out condition assessment, it is important to assure quality & quantity of data and to use correct procedures for interpretation of data for fault detection. This paper discusses the work carried out to develop a machine learning based methodology for detecting faults in a wind turbine generator bearing. Explanation of the working of the machine learning model has also been discussed in detail.Methods: The methodology includes application of machine learning model using SCADA data for predicting operating temperature of a healthy bearing; and then comparing the predicted bearing temperature against the actual bearing temperature.Results: Consistent abnormal differences between predicted and actual temperatures may be attributed to the degradation and presence of a fault in the bearing.Discussion: This fault detection can then be used for rescheduling the maintenance tasks. The working of this methodology is discussed in detail using a case study.

Risk-based maintenance and inspection of riverine flood defence systems

The condition of flood defence revetments is influenced by many different degradation processes such as animal burrowing, rutting and growth of weeds. Many of these processes are shock-based rather than progressive continuous. As shocks can cause a drop in performance, this means that the condition of a revetment can suddenly decrease, meaning that revetments can have significant initial damage at the beginning of a storm. Combined with the limited detection probability of common visual inspections of flood defences, this can have a significant influence on the reliability of flood defence systems, something typically not considered in reliability analysis. In this paper we study the reliability of a flood defence system subject to shock-based degradation. Various maintenance concepts are compared for a case study of a riverine flood defence of 20 kilometres length. This demonstrates that the current maintenance concept is insufficient to satisfy the reliability requirements for failure of the revetment. Overall, the joint influence of degradation and the existing maintenance concept leads to a 20 times higher failure probability estimate compared to a typical assessment without these aspects. Next, we demonstrate that both additional inspections, and targeted interventions to reduce the impact of for instance animal burrowing, can significantly reduce total cost and improve robustness of the considered flood defence system.

Bovine cysticercosis epidemiology and the economic impact of the triceps brachii incision in a South African export abattoir.

Taenia saginata is a zoonotic tapeworm of humans with bovines as its intermediate host (bovine cysticercosis). Traditional meat inspection is the main measure to identify the larval stage in carcasses and prevent human infection, but has a notoriously low sensitivity, especially in low prevalence settings. The legislation in multiple African countries mandates an incision in both triceps brachii muscles to detect the parasite as part of the normal post-mortem inspection, but this has an economic cost and is not universally mandated in other countries. The primary aim of this study was to investigate the epidemiology of bovine cysticercosis at an export abattoir in South Africa and determine the validity and cost of the triceps incision. Risk factors were investigated, and the effect of additional heart incisions on the current inspection. Four incisions were made into the heart in addition to the normal post-mortem inspection in 3353 carcasses. The proportion of positive animals with and without the cardiac and triceps incisions were compared using McNemar's chi-square tests, while risk factors were assessed using binary logistic regression. The economic impact of the triceps incision was estimated using a stochastic economic cost model. Thirty-three positive carcasses were identified for an apparent prevalence of 0.98 % (95 %CI, 0.69-1.36 %), while the true prevalence was estimated at 6.6 % (95 %CI, 4.3-8.8 %). All cysts were confirmed as T. saginata on histopathology with 70 % (95 %CI, 53-83 %) located in the heart. The additional cardiac incisions resulted in the detection of significantly more cases compared to the normal inspection method prevalence of 0.72 % (95 %CI, 0.47-1.05 %; P<0.001). The apparent prevalence of T. saginata when omitting the triceps incision was not significantly lower compared to the prevalence when included in the inspection(P=0.480). External feedlots (OR= 4.17, 95 %CI: 2.04-8.54, P<0.001) and older animals (OR=3.90, 95 %CI: 1.17-13.03, P=0.027) were associated with a positive detection. The current median annual financial cost to the food business operator from the triceps incision was estimated at $30387 (95 %CI: $0-$130696), with the proportion of deboned meat exported identified as the most important factor affecting cost (Spearman's rho=0.853). The identification of risk factors could aid in the development of a more effective risk-based inspection system. The current inspection should be modified to increase exposure of the heart and remove the triceps incisions. The latter should especially be considered given the minimal contribution to cysticercosis detection, the unsupported requirement for its inclusion specifically in Africa, and its economic impact, especially as beef exports increase.