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Industry decision makers often rely on a risk-based approach to perform inspection and maintenance planning. According to the Risk-Based Inspection and Maintenance Procedure project for the European industry, risk has two main components: probability of failure (PoF) and consequence of failure (CoF). As one of these risk drivers, a more accurate estimation of the PoF will contribute to a more accurate risk assessment. Current methods to estimate the PoF are either time-based or founded on expert judgement. This paper suggests an approach that incorporates the proportional hazards model (PHM), which is a statistical procedure to estimate the risk of failure for a component subject to condition monitoring, into the risk-based inspection (RBI) methodology, so that the PoF estimation is enhanced to optimize inspection policies.

To achieve the overall goal of this paper, a case study applying the PHM to determine the PoF for the real-time condition data component is discussed. Due to a lack of published data for risk assessment at this stage of the research, the case study considered here uses failure data obtained from the simple but readily available Intelligent Maintenance Systems bearing data, to illustrate the methodology.

The benefit of incorporating PHM into the RBI approach is that PHM uses real-time condition data, allowing dynamic decision-making on inspection and maintenance planning. An additional advantage of the PHM is that where traditional techniques might not give an accurate estimation of the remaining useful life to plan inspection, the PHM method has the ability to consider the condition as well as the age of the component.

This paper is proposing the development of an approach to incorporate the PHM into an RBI methodology using bearing data to illustrate the methodology. The CoF estimation is not addressed in this paper.

This paper presents the benefits related to the use of PHM as an approach to optimize the PoF estimation, which drives to the optimal risk assessment, in comparison to the time-based approach.

Railway transport maintenance plays an important role in delivering safe, reliable and competitive transport services. An appropriate maintenance strategy not only reduces the assets’ lifecycle cost, but also will ensure high standards of safety and comfort for rail passengers and workers. In recent years, the majority of studies have been focused on the application of risk-based tools and techniques to maintenance decision making of railway infrastructure assets (such as tracks, bridges, etc.). The purpose of this paper is to present a risk-based modeling approach for the inspection and maintenance optimization of railway rolling stock components.

All the “potential failure modes and root causes” related to rolling stock systems are identified from an extensive literature review followed by an expert’s panel assessment. The failure causes are categorized into six groups of electrical faults, structural damages, functional failures, degradation, human errors and natural (external) hazards. Stochastic models are then proposed to estimate the likelihood (probability) of occurrence of a failure in the rolling stock system. The consequences of failures are also modeled by an “inflated cost function” that involves safety-related costs, corrective maintenance and renewal (M&R) costs, the penalty charges due to train delays or service interruptions as well as the costs associated with loss of reputation (or loss of fares) in the case of trip cancellation. Lastly, a time-varying risk-cost function is formulated to determine the optimal frequency of preventive inspection and maintenance actions for rolling stock components.

For the purpose of clearly illustrating the proposed risk-based inspection and maintenance modeling methodology, a case study of the Class 380 train’s pantograph system from a Scottish train operating company is provided. The results indicate that the proposed model has a substantial potential to reduce the M&R costs while ensuring a higher level of safety and service quality compared to the currently used inspection methodologies.

The railway rolling stocks should be regularly inspected and maintained so as to ensure network availability and reliability, passenger safety and comfort, and operations efficiency. Despite the best efforts of the maintenance staff, it is reported that a considerable amount of maintenance resources (e.g. budget, time, manpower) is wasted due to insufficiency or inefficiency of current periodic M&R interventions. The model presented in this paper helps the maintenance engineers to assess the current maintenance practices and propose or initiate improvement actions when needed.

There are few studies investigating the application of risk-based tools and techniques to inspection and maintenance decision making of railway rolling stock components. This paper presents a modeling approach aimed at planning the preventive repair and maintenance interventions for rolling stock components based on risk measures. The author’s model is also capable of incorporating real measurement information gathered at each inspection epoch to update future inspection plans.

Steam explosions are a major safety concern in many modern furnaces. The explosions are sometimes caused by water ingress into the furnace from leaks in its high-pressure (HP) cooling water system, coming into contact with molten matte. To address such safety issues related to steam explosions, risk based inspection (RBI) is suggested in this paper. RBI is presently one of the best-practice methodologies to provide an inspection schedule and ensure the mechanical integrity of pressure vessels. The application of RBIs on furnace HP cooling systems in this work is performed by incorporating the proportional hazards model (PHM) with the RBI approach; the PHM uses real-time condition data to allow dynamic decision-making on inspection and maintenance planning.

To accomplish this, a case study is presented that applies an HP cooling system data with moisture and cumulated feed rate as covariates or condition indicators to compute the probability of failure and the consequence of failure (CoF), which is modelled based on the boiling liquid-expanding vapour explosion (BLEVE) theory.

The benefit of this approach is that the risk assessment introduces real-time condition data in addition to time-based failure information to allow improved dynamic decision-making for inspection and maintenance planning of the HP cooling system. The work presented here comprises the application of the newly proposed methodology in the context of pressure vessels, considering the important challenge of possible explosion accidents due to BLEVE as the CoF calculations.

This paper however aims to optimise the inspection schedule on the HP cooling system, by incorporating PHM into the RBI methodology, as was recently proposed in the literature by Lelo et al. (2022). Moisture and cumulated feed rate are used as covariate. At the end, risk mitigation policy is suggested.

In this paper, the proposed methodology yields a dynamically calculated quantified risk, which emphasised the imperative for mitigating the risk, as well as presents a number of mitigation options, to quantifiably affect such mitigation.

The purpose of this paper is to analyze the reliability of the quantitative risk model used for planning inspection and maintenance activities. The objective is to critically discuss the factors that contribute to the probability and consequence of failure calculations.

The case study conducted using one of the most widely deployed risk models in the oil and gas industry where a full assessment was performed on an offshore gas producing platform.

The generic failure frequencies used as the basis for calculating the probability of failure are set at a value representative of the refining and petrochemical industry's failure data. This failure database does not cover offshore. The critical discussion indicated the lack of basis of the coefficient of variances, prior probabilities and conditional probabilities. Moreover, the risk model does not address the distribution of thickness measurements, corrosion rates and inspection effectiveness, whereas only overall deterministic values are used; this requires judgment to determine these values. Probabilities of ignition, probabilities of delayed ignition and other probabilities in Level 1 event tree are found selected based on expert judgment for each of the reference fluids and release types (i.e. continuous or instantaneous). These probabilities are constant and independent of the release rate or mass and lack of constructed model. Defining the release type is critical in the consequence of the failure methodology, whereas the calculated consequences differ greatly depending on the type of release, i.e. continuous or instantaneous. The assessment results show that both criteria of defining the type of release, i.e. continuous or instantaneous, do not affect the calculations of flammable consequences when the auto-ignition likely is zero at the storage temperature. While, the difference in the resulted toxic consequence was more than 31 times between the two criteria of defining the type of release.

There is a need to revamp this quantitative risk model to minimize the subjectivity in the risk calculation and to address the unique design features of offshore platforms.

This case study critically discuss the risk model being widely applied in the O&G industry and demonstrates to the end-users the subjectivity in the risk results. Hence, be vigilant when establishing the risk tolerance/target for the purpose of inspection and maintenance planning.

The purpose of this paper is to review the evolution of inspection and maintenance (I&M) practices used for aging and newly built oil and gas (O&G) facilities. It also proposes a framework and an approach for mechanizing inspection planning to perform preventive maintenance (PM) activities, taking technical condition (TC) and relative degradation (RD) into consideration.

The paper systematically collects, categorizes, and analyzes the published literature of both researchers and practitioners. It also utilizes industrial experience that has been accrued and utilized from inspection planning practices for static mechanical equipment on aging O&G production plants.

The paper defines significant issues in I&M of O&G assets related to: different philosophies; stakeholders’ requirements trade-off; dependability and asset deterioration challenges; items interacting with inspection planning mechanization processes and I&M optimization approaches. A framework is identified to mechanize the inspection planning process in order to reduce the effect arising from human involvement, while improving the effective utilization of data from different sources. The suggested approach improves the quality of an inspection program, while minimizing the variability and cost to the engineering contractors as well as to the owners of O&G facilities.

The mechanization of inspection planning (MIP) is vital to have inspection programs with uniform quality. The currently employed inspection practices face challenges in maintaining uniform quality from one inspection program to another due to the variability present in the planning process, especially among the different inspection planning engineers. The suggested fuzzy logic-based MIP supports the minimization of the variability and increases the quality of inspection programs.

The paper provides a comprehensive review of research contributions and industrial development efforts. These will be useful to the life cycle stakeholders in both academia and industry in understanding the inspection planning problem and solution space within the O&G asset I&M context.

Corrosion loop development is an integral part of the risk-based inspection (RBI) methodology. The corrosion loop approach allows a group of piping to be analyzed simultaneously, thus reducing non-value adding activities by eliminating repetitive degradation mechanism assessment for piping with similar operational and design characteristics. However, the development of the corrosion loop requires rigorous process that involves a considerable amount of engineering man-hours. Moreover, corrosion loop development process is a type of knowledge-intensive work that involves engineering judgement and intuition, causing the output to have high variability. The purpose of this paper is to reduce the amount of time and output variability of corrosion loop development process by utilizing machine learning and group technology method.

To achieve the research objectives, k-means clustering and non-hierarchical classification model are utilized to construct an algorithm that allows automation and a more effective and efficient corrosion loop development process. A case study is provided to demonstrate the functionality and performance of the corrosion loop development algorithm on an actual piping data set.

The results show that corrosion loops generated by the algorithm have lower variability and higher coherence than corrosion loops produced by manual work. Additionally, the utilization of the algorithm simplifies the corrosion loop development workflow, which potentially reduces the amount of time required to complete the development. The application of corrosion loop development algorithm is expected to generate a “leaner” overall RBI assessment process.

Although the algorithm allows a part of corrosion loop development workflow to be automated, it is still deemed as necessary to allow the incorporation of the engineer’s expertise, experience and intuition into the algorithm outputs in order to capture tacit knowledge and refine insights generated by the algorithm intelligence.

This study shows that the advancement of Big Data analytics and artificial intelligence can promote the substitution of machines for human labors to conduct highly complex tasks requiring high qualifications and cognitive skills, including inspection and maintenance management area.

This paper discusses the novel way of developing a corrosion loop. The development of corrosion loop is an integral part of the RBI methodology, but it has less attention among scholars in inspection and maintenance-related subjects.

Inspection and maintenance of plant and machinery has traditionally been based on prescriptive industry practices. However, increased experience and a greater understanding of operational hazards is leading sections of industry to take a more informed approach to planning inspection and maintenance, targeting resources to reduce the risk to as low as reasonably practicable. The purpose of this paper is to present an approach to asset management to minimize risks in the most cost effective way.

The approach shown optimizes run‐repair‐replace decision‐making in the integrity management of assets with the ultimate aim of maximising the impact of money spent on risk mitigation actions. The risk‐based approach, as opposed to the more conventional approaches, assesses failure in its wider context by considering not just the likelihood of failure, but also the consequences should the failure event occur.

The risk‐based methodology presents a cost‐effective way to minimise life cycle costs in the management of assets whilst maintaining reliability or availability targets, and operating within safety and environmental regulation.

In this paper, for demonstration, a wind turbine system consisting of a number of components including structural components is used. However, the methodology can be extended to any system in which components can be analyzed to provide the required inputs to the risk model.

At a time when competitive pressures force asset managers to prioritize their maintenance, the risk‐based methodology presented here is a rational, efficient and somewhat flexible way to asset integrity management.

Turnaround maintenance (TAM) is a planned stoppage of production for conducting a comprehensive maintenance of equipment or plant with the purpose of improving plant availability and performance. The purpose of this paper is to investigate trends in the operation and management of TAM, as reported in the literature, and identify gaps, in the context of a system approach that views a plant as part of a network of a supply chain.

This literature review is based on over 80 subject-relevant papers and uses content analysis. The literature subjects are classified into several managerial areas that include organization, planning, scope and risk analysis, execution, performance measurement and learning. The gap in the literature is identified in light of the proposed system view for TAM.

The system view of TAM opens new opportunities for new research areas for improving the operation and management of TAM. These areas include optimizing TAM scheduling and developing methods for managing risks along the entire business supply chain. In addition, new approaches for collaboration, sharing knowledge, best practices and expertise within the supply chain become necessary for effective TAM planning and control.

This paper reviews the literature and provides a new classification of TAM. It adopts the system view for TAM that has brought new insights in the operation and management of TAM. New trends for research in the area of TAM are identified.

The purpose of this paper is to identify and discuss maintenance challenges and maintenance practices for subsea petroleum production systems.

Maintenance challenges, current practices and factors that influence the maintenance and support practices were identified by a literature review and by using a case study conducted in the Norwegian oil and gas industry. The case study was based on semi‐structured face‐to‐face interviews with a number of experts working in the subsea systems’ design, installation and support services in the Norwegian oil and gas industry.

The paper identifies and discusses subsea petroleum production system failures, maintenance, inspection, modification and support practices. Findings from literature are validated, and new challenges are identified and discussed.

The research is based on a case study in the Norwegian petroleum industry, but may be applicable in other countries as well. The subsea production systems are critical production systems, and failures may result in long downtime and costly maintenance, inspection and support services. Hence, inspection, maintenance and modification intervention support services requires careful project planning, implementation and execution, taking into account all influencing factors.

The identified challenges can be used by decision makers in offshore maintenance projects.

The purpose of this paper is to review the operation and maintenance practices within wind power applications and to clarify practical needs as gaps between researchers and practitioners.

The paper collects, categorizes, and analyzes the published literature of both researchers and practitioners systematically.

The paper defines significant issues in operation and maintenance of wind energy related to: site and seasonal asset disturbances; stakeholders’ requirements trade‐off; dependability and asset deterioration challenges; diagnostic, prognostic and information and communication technologies (ICTs) applications; and maintenance optimization models. Within each category, the gaps and further research needs have been extracted with respect to both an academic and industrial perspective.

The use of wind energy is growing rapidly and the associated practices related to maintenance and asset management are still lacking. Therefore, the literature review of operation and maintenance is a necessity to uncover the holistic issues and interrelationships of what has so far been published as detailed and fragmented topics to specific issues. Wind energy assets represent modern renewable energy assets which are affected by environmental disturbances, rapid technological development, rapid scaling‐up processes, the stochastic and dynamic nature of operations and degradation, the integrity and interoperability of system‐to‐support.

The paper provides a comprehensive review of research contributions and industrial development efforts. That will be useful to the life cycle stakeholders in both academia and industry in understanding the maintenance problem and solution space within the wind energy context.