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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 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 carry out the application of risk reliability-based underwater inspection (RReBUI).

The consequence of failure factor is calculated qualitatively in accordance with the risk-based underwater inspection (RBUI) method but the criteria are modified as an adjustment to the addition and combination of production and reliability information of the analyzed platforms. The probability of failure (PoF) is determined quantitatively by calculating the structure reliability index based on collapse failure mechanism in which the uncertainty of wave load is considered. The PoF criteria from the RBUI are re-modified to adjust the criterion with the highest and lowest reliability indexes obtained in RReBUI study. Selection of exposure category of the platform is still the same as the RBUI method.

The models of three offshore jacket platforms located in each of Java Sea and Natuna Sea were used for the RReBUI application. These six models were previously used in the traditional RBUI application. The results of RReBUI analysis indicated that including the environmental characteristics in the risk assessment resulted in more reliable inspection interval plans.

The drawback of RBUI is that it cannot be used for platforms spread over a distance or different areas, as the failure parameters of these platforms cannot be compared. Furthermore, the RBUI method does not consider the environmental characteristics in its risk assessment. Unlike RBUI, the purpose of RReBUI method is to assess the reliability of a platform based on both structural and environment characteristics. Therefore, the RReBUI method determines the risk of every platform quantitatively without having to compare the failure parameters based on expert justification. The application of RReBUI for jacket platforms has never been developed in Indonesia.

The purpose of this paper is to focus on developing a knowledge-based engineering (KBE) approach to recycle the knowledge accrued in an industrial organization for the mitigation of unwanted events due to human error. The recycling of the accrued knowledge is vital in mitigating the variance present at different levels of engineering applications, evaluations and assessments in assuring systems’ safety. The approach is illustrated in relation to subsea systems’ functional failure risk (FFR) analysis.

A fuzzy expert system (FES)-based approach has been proposed to facilitate FFR assessment and to make knowledge recycling possible via a rule base and membership functions (MFs). The MFs have been developed based on the experts’ knowledge, data, information, and on their insights into the selected subsea system. The rule base has been developed to fulfill requirements and guidelines specified in DNV standard DNV-RP-F116 and NORSOK standard Z-008.

It is possible to use the FES-based KBE approach to make FFR assessments of the equipment installed in a subsea system, focussing on potential functional failures and related consequences. It is possible to integrate the aforementioned approach in an engineering service provider’s existing structured information management system or in the computerized maintenance management system (CMMS) available in an asset owner’s industrial organization.

The FES-based KBE approach provides a consistent way to incorporate actual circumstances at the boundary of the input ranges or at the levels of linguistic data and risk categories. It minimizes the variations present in the assessments.

The FES-based KBE approach has been demonstrated in relation to the requirements and guidelines specified in DNV standard DNV-RP-F116 and NORSOK standard Z-008. The suggested KBE-based FES that has been utilized for FFR assessment allows the relevant quantitative and qualitative data (or information) related to equipment installed in subsea systems to be employed in a coherent manner with less variability, while improving the quality of inspection and maintenance recommendations.

The purpose of this study is to enable performing reliability-based design optimisation (RBDO) for a composite component while accounting for several multi-scale uncertainties using a large representative volume element (LRVE). This is achieved using an efficient finite element analysis (FEA)-based multi-scale reliability framework and sequential optimisation strategy.

An efficient FEA-based multi-scale reliability framework used in this study is extended and combined with a proposed sequential optimisation strategy to produce an efficient, flexible and accurate RBDO framework for fibre-reinforced composite laminate components. The proposed RBDO strategy is demonstrated by finding the optimum design solution for a composite component under the effect of multi-scale uncertainties while meeting a specific stiffness reliability requirement. Performing this using the double-loop approach is computationally expensive because of the number of uncertainties and function evaluations required to assess the reliability. Thus, a sequential optimisation concept is proposed, which starts by finding a deterministic optimum solution, then assesses the reliability and shifts the constraint limit to a safer region. This is repeated until the desired level of reliability is reached. This is followed by a final probabilistic optimisation to reduce the mass further and meet the desired level of stiffness reliability. In addition, the proposed framework uses several surrogate models to replace expensive FE function evaluations during optimisation and reliability analysis. The numerical example is also used to investigate the effect of using different sizes of LRVEs, compared with a single RVE. In future work, other problem-dependent surrogates such as Kriging will be used to allow predicting lower probability of failures with high accuracy.

The integration of the developed multi-scale reliability framework with the sequential RBDO optimisation strategy is proven computationally feasible, and it is shown that the use of LRVEs leads to less conservative designs compared with the use of single RVE, i.e. up to 3.5% weight reduction in the case of the 1 × 1 RVE optimised component. This is because the LRVE provides a representation of the spatial variability of uncertainties in a composite material while capturing a wider range of uncertainties at each iteration.

Fibre-reinforced composite laminate components designed using reliability and optimisation have been investigated before. Still, they have not previously been combined in a comprehensive multi-scale RBDO. Therefore, this study combines the probabilistic framework with an optimisation strategy to perform multi-scale RBDO and demonstrates its feasibility and efficiency for an fibre reinforced polymer component design.

Due to many different types of aleatory and epistemic uncertainty in soil properties, safety factor, which is assessed by deterministic analysis, is not reliable. The purpose of this paper is to determine the difference between critical slip surface in deterministic analysis and critical reliability slip surface in probabilistic analysis.

Deterministic analysis is formulated by the limit equilibrium methods, including Fellenius method, Bishop method, and Janbu’s simplified method. Then, the factor of safety is calculated for different slip surfaces. The stability of the soil is defined as the critical slip surface with the lowest factor of safety in each method. For probabilistic analysis, the value of reliability index, factor of safety, and probability of failure regarding given potential slip surface are considered as the stability index and obtained by the Monte Carlo simulation method.

To compare deterministic and probabilistic analysis as well as the influence of each of the aforementioned methods and stability index, a soil slope with three uncertainty parameters is analyzed and the results indicate that the critical slip surface is significantly different from critical reliability slip; however, the results from the above-mentioned methods are very close.

There are many other methods that could be studied; however, the most usual ones were employed. Furthermore, this study just consider the most important factors as the uncertainty parameters; nevertheless, it can be extended to more geotechnical parameters.

Although there are many studies in this field, the authors conduct a succinct but very noteworthy research to show the difference between the results of mentioned methods as well as deterministic and probabilistic approaches and their influence on slip surface.

The purpose of this paper is to present a risk-based prioritization method (RBPM), using functional failure risk and strategic importance assessment, in order to support public managers' decision-making process for the planning and allocation of scarce resources.

The methodology incorporates two assessment matrices into a management group’s decision-making process for resource allocation. As part of an action research strategy, the authors present the developed RBPM and its application in one Norwegian police district.

The RBPM provides a processual approach to detect risk of failures in the organization, which can facilitate better planning and allocation of resources, to mitigate risks from materializing and enhance performance. The results also indicate that the proposed assessment models provide an easier approach to consolidating different perspectives and weighting the strategic importance of disciplinary functions, when prioritizing the planning and allocation of scarce resources is necessary.

The research is restricted in that it only considers one police district in Norway. Further limitation surrounds the issue of generalizability, as only one unit from the district is used. Further research should adapt and extend the developed RBPM in the district.

The RBPM may be of interest to public managers in the emergency sector operating with a large number of disciplinary functions in a highly dynamic and uncertain environment.

The RBPM applied in this study is based on approaches with advanced application in the oil and gas industry adapted to a contextually different environment which, in contrast, consists of functional hierarchies composed of human resources.

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 study is to propose an extended reliability method for an industrial motor drive by integrating the physics of failure (PoF).

Industrial motor drive systems (IMDS) are currently expected to perform beyond the desired operating conditions to meet the demand. The PoF of the subsystem affects its reliability under such harsh operating circumstances. It is crucial to estimate reliability by integrating PoF, which helps in understanding its impact and to develop a fault-tolerant design, particularly in such an integrated drive system. An integrated PoF extended reliability method for industrial drive system is proposed to address this issue. In research, the numerical failure rate of each component of industrial drive is obtained first with the help of the MIL-HDBK-217 military handbook. Furthermore, the mathematically deduced proposed approach is modeled in the GoldSim Monte Carlo reliability workbench.

From the results, for a 15% rise in integrated PoF, the reliability and availability of the entire IMDS dropped by 23%, resulting in an impact on mean time to failure (MTTF).

The integrated PoF of the motor and motor controller affects industrial drive reliability, which falls to 0.18 with the least MTTF (2.27 years); whose overall reliability of industrial drive drops to 0.06 if it is additionally integrated with communication protocol.

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.