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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.

The purpose of this research is to create an expert risk‐based piping system inspection model. The proposed system includes a risk‐based piping inspection system and a piping inspection guideline system. The research procedure consists of three parts: the risk‐based inspection model, the risk‐based piping inspection model, and the piping inspection guideline system model. In this research procedure, a field plant visit is conducted to collect the related domestic information (Taiwan) and foreign standards and regulations for creating a strategic risk‐based piping inspection and analysis system in accordance with the piping damage characteristics in the petrochemical industry. In accordance with various piping damange models and damage positions, petrochemical plants provide the optimal piping inspection planning tool for efficient piping risk prediction for enhancing plant operation safety.

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.

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.

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.

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.

Process plant safety, asset integrity, reliability, quality, profitability and maintenance are traditionally compartmentalized, each being treated as a stand‐alone subject. The purpose of this paper is to examine whether they are closely related, with common drivers of performance. This allows us to consider a holistic approach that is effective and economically viable.

In achieving business performance targets, the focus is often on technology and hardware. The human interface is not always given sufficient importance. This paper attempts to show that sustainable performance depends on achieving a proper balance.

Focusing on just three drivers – reliability, productivity and sustainability – good results can be facilitated in terms of quality, process safety, and profitability. Working on individual initiative alone is not sufficient and all the links in the causal chain have to be strengthened.

There are several factors that affect the above drivers. By managing these factors effectively, high performance can be achieved over the lifetime of the business. An integrated plan of action is thus preferable to flavour‐of‐the‐month initiatives.

The paper shows that combining the technological and human behavioural aspects brings a holistic approach. The links shown between maintenance, reliability, quality, asset integrity, process safety and profitability provide a business focus. A discussion of these concepts with professionals and experts can help create a solid foundation for lasting improvements.

The purpose of this paper is to address reported weaknesses with existing equipment reliability improvement methods through their integration into the Six‐Sigma DMAIC methodology.

The evaluation was done by assessing the weaknesses of traditional methods such as reliability centered maintenance (RCM), evaluating what Six‐Sigma could potentially offer to close the gaps, and testing potential improvements through an example application.

It is concluded that Six‐Sigma addresses many RCM flaws and weaknesses. It is also concluded that Six‐Sigma, if integrated with other reliability techniques, can produce results that are far more objective and dependable.

Six‐Sigma, however, still bears its own cons and limitations. It requires good data which are sometimes unavailable. Six‐Sigma is also lengthier and consumes more resources per single problem since it focuses at one problem at a time.

The introduction of Six‐Sigma into equipment reliability/maintenance applications is quite original since this methodology has traditionally been limited to manufacturing and only recently to administrative processes. The outcome is of significant value as it opens up a new perspective into the development of reliability improvement measures for plant equipment.

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.