On risk-based maintenance: A comprehensive review of three approaches to track the impact of consequence modelling for predicting maintenance actions

Since gas plants are progressively increasing near urban areas, a comprehensive tool to plan maintenance and reduce the risk arising from their operations is required. To this end, a comparison of three Risk-Based Maintenance methodologies able to point out maintenance priorities for the most critical components, is presented in this paper. Moreover, while the literature is mostly focused on probabilistic analysis, a particular attention is directed towards consequence analysis throughout this study. The first developed technique is characterized by a Hierarchical Bayesian Network to perform the occurrence analysis and a Failure Modes, Effects and Criticality Analysis to assess the magnitude of the adverse outcomes. The second approach is a Quantitative Risk Analysis carried out via a software named Safeti. Finally, another software called Synergi Plant is adopted for the third methodology, which provides a Risk-Based Inspection plan, through a semiquantitative risk analysis. The proposed study can assist asset manager in adopting the most appropriate methodology to their context, while highlighting priority components. To demonstrate the applicability of the approaches and compare their rankings, a Natural Gas Regulating and Measuring Station is considered as case study. The results showed that the most suited method strongly depends on the available data

On reliability assessment of ship machinery system in different autonomy degree; A Bayesian-based approach

Analyzing the reliability of autonomous ships has recently attracted attention mainly due to epistemic uncertainty (lack of knowledge) integrated with automatic operations in the maritime sector. The advent of new random failures with unrecognized failure patterns in autonomous ship operations requires a comprehensive reliability assessment specifically aiming at estimating the time in which the ship can be trusted to be left unattended. While the reliability concept is touched upon well through the literature, the operational trustworthiness needs more elaboration to be established for system safety, especially within the maritime sector. Accordingly, in this paper, a probabilistic approach has been established to estimate the trusted operational time of the ship machinery system through different autonomy degrees. The uncertainty associated with ship operation has been quantified using Markov Chain Monte-Carlo simulation from likelihood function in Bayesian inference. To verify the developed framework, a practical example of a machinery plant used in typical short sea merchant ships is taken into account. This study can be exploited by asset managers to estimate the time in which the ship can be left unattended. Keywords: reliability estimation, Bayesian inference, autonomous ship, uncertainty.</p

A probabilistic model to evaluate the resilience of unattended machinery plants in autonomous ships

Over the next few years, digitalization and automation are expected to be key drivers for maritime transport innovation to be key drivers for maritime transportation innovation. This revolutionary shift in the shipping industry will heavily impact the reliability of the machinery which is intended to be operated remotely with minimum support from humans. Despite a large amount of research into autonomous navigation and control systems in maritime transportation, the evaluation of unattended engine rooms has received very little attention. For autonomous vessels to be effective during their unmanned mission, it is essential for the engine room understand its health condition and self-manage performance. The unattended machinery plant (UMP) should be resilient enough to have the ability to survive and recover from unexpected perturbations, disruptions, and operational degradations. Otherwise, the system may require unplanned maintenance or the operation will stop. Therefore, the UMP must continue its operation without human intervention and safely return the ship to port. This paper aims to develop a machine learning-based model to predict an UMP's performance and estimate how long the engine room can operate without human assistance. A Random Process Tree is used to model failures in the unattended components, while a Hierarchical Bayesian Inference is adopted to facilitate the prediction of unknown parameters in the process. A probabilistic Bayesian Network developed and evaluated the dependent relationship between active and standby components to assess the effect of redundant units in the performance of unattended machinery. The present framework will provide helpful additional information to evaluate the associate uncertainties and predict the untoward events that put the engine room at risk. The results highlight the model's ability to predict the UMP's trusted operation period and evaluate an unattended engine room's resilience. A real case study of a merchant vessel used for short sea shipping in European waters is considered to demonstrate the model's application.</p

Prognostic health management of repairable ship systems through different autonomy degree; From current condition to fully autonomous ship

Maritime characteristics make the progress of automatic operations in ships slow, especially compared to other means of transportation. This caused a great progressive deal of attention for Autonomy Degree (AD) of ships by research centers where the aims are to create a well-structured roadmap through the phased functional maturation approach to autonomous operation. Application of Maritime Autonomous Surface Ship (MASS) requires industries and authorities to think about the trustworthiness of autonomous operation regardless of crew availability on board the ship. Accordingly, this paper aims to prognose the health state of the conventional ships, assuming that it gets through higher ADs. To this end, a comprehensive and structured Hierarchal Bayesian Inference (HBI)-based reliability framework using a machine learning application is proposed. A machinery plant operated in a merchant ship is selected as a case study to indicate the advantages of the developed methodology. Correspondingly, the given main engine in this study can operate for 3, 17, and 47 weeks without human intervention if the ship approaches the autonomy degree of four, three, and two, respectively. Given the deterioration ratio defined in this study, the acceptable transitions from different ADs are specified. The aggregated framework of this study can aid the researchers in gaining online knowledge on safe operational time and Remaining Useful Lifetime (RUL) of the conventional ship while the system is being left unattended with different degrees of autonomy.</p

Machine learning and deep learning for safety applications : Investigating the intellectual structure and the temporal evolution

Publisher Copyright: © 2023 The Author(s)Over the last decades, safety requirements have become of primary concern. In the context of safety, several strategies could be pursued in many engineering fields. Moreover, many techniques have been proposed to deal with safety, risk, and reliability matters, such as Machine Learning (ML) and Deep Learning (DL). ML and DL are characterised by a high variety of algorithms, adaptable for different purposes. This generated wide and fragmented literature on ML and DL for safety purposes, moreover, literature review and bibliometric studies of the past years mainly focus on a single research area or application field. Thus, this paper aims to provide a holistic understanding of the research on this topic through a Systematic Bibliometric Analysis (SBA), along with proposing a viable option to conduct SBAs. The focus is on investigating the main research areas, application fields, relevant authors and studies, and temporal evolution. It emerged that rotating equipment, structural health monitoring, batteries, aeroengines, and turbines are popular fields. Moreover, the results depicted an increase in popularity of DL, along with new approaches such as deep reinforcement learning through the past four years. The proposed workflow for SBA has the potential to benefit researchers from multiple disciplines, beyond safety science.Peer reviewe

Multi-level optimization of maintenance plan for natural gas system exposed to deterioration process

In this paper, a risk-based optimization methodology for a maintenance schedule considering Process Variables (PVs), is developed within the framework of asset integrity assessment. To this end, an integration of Dynamic Bayesian Network, Damage Modelling and sensitivity analysis are implemented to clarify the behaviour of failure probability, considering the exogenous undisciplinable perturbations. Discrete time case is considered through measuring or observing the PVs. Decision configurations and utility nodes are defined inside the network to represent maintenance activities and their associated costs. The regression analysis is considered to model the impact of perturbations on PVs for future applications. The developed methodology is applied to a case study of Chemical Plant (Natural Gas Regulating and Metering Stations). To demonstrate the applicability of the methodology, three cases of seasonal observations of specific PV (pressure) are considered. The proposed methodology could either analyse the failure based on precursor data of PVs or obtain the optimum maintenance schedule based on actual condition of the systems

A systematic approach for predicting loyalty behavior of tourist destinations

Purpose: Destinations to be able to compete with each other need to equip themselves with as many competitive advantages as possible. Tourists' loyalty to a destination is considered as a prominent competitive tool for destinations. Tourists' loyalty manifests itself in recommendation of the destination to others, repeat visit of the destination and willingness to revisit the destination. Although a plethora of studies have tried to define models to show the relation between loyalty and the antecedent factors leading up to it, few of them have tried to integrate these models with mathematical approaches for better understanding of loyalty behavior. The purpose of this paper is to integrate a tourist destination model with Bayesian Network in order to predict the behaviour of destination loyalty and its antecedent factors. Design/methodology/approach: This paper has developed a probability model by the integration of a destination loyalty model with a Bayesian network (BN) which enables to predict and analyze the behavior of loyalty and its influential factors. To demonstrate the application of this framework, Tehran, the capital of Iran, was chosen as a destination case study. Findings: The outcome of this research will assist in identifying the weak key points in the tourist destination area for giving insights to the marketers, businesses and policy makers for making better decisions related to destination loyalty. In the analysis process, the most influential factors were recognized as the travel environment image, natural/historical attractions and, with a lower degree, infrastructure image which help the decision maker to detect and reinforce the weak factors and put more effort in focusing on improving the necessary parts rather than the irrelevant parts. Originality/value: The research identified all critical factors that have the most influence on destination loyalty while driving the associate uncertainty which is significant for the tourism industry. This resulted in better decision-making which is used to identify the impact of tourism destination loyalty.</p

Reliability Estimation under Scarcity of Data: A Comparison of Three Approaches

During the last decades, the optimization of the maintenance plan in process plants has lured the attention of many researchers due to its vital role in assuring the safety of operations. Within the process of scheduling maintenance activities, one of the most significant challenges is estimating the reliability of the involved systems, especially in case of data scarcity. Overestimating the average time between two consecutive failures of an individual component could compromise safety, while an underestimate leads to an increase of operational costs. Thus, a reliable tool able to determine the parameters of failure modelling with high accuracy when few data are available would be welcome. For this purpose, this paper aims at comparing the implementation of three practical estimation frameworks in case of sparse data to point out the most efficient approach. Hierarchical Bayesian modelling (HBM), maximum likelihood estimation (MLE), and least square estimation (LSE) are applied on data generated by a simulated stochastic process of a natural gas regulating and metering station (NGRMS), which was adopted as a case of study. The results identify the Bayesian methodology as the most accurate for predicting the failure rate of the considered devices, especially for the equipment characterized by less data available. The outcomes of this research will assist maintenance engineers and asset managers in choosing the optimal approach to conduct reliability analysis either when sufficient data or limited data are observed.Peer reviewe

Bayesian estimation for reliability engineering

Over the last few decades, reliability analysis has attracted significant interest due to its importance in risk and asset integrity management. Meanwhile, Bayesian inference has proven its advantages over other statistical tools, such as maximum likelihood estimation (MLE) and least square estimation (LSE), in estimating the parameters characterizing failure modelling. Indeed, Bayesian inference can incorporate prior beliefs and information into the analysis, which could partially overcome the lack of data. Accordingly, this paper aims to provide a closed-mathematical representation of Bayesian analysis for reliability assessment of industrial components while investigating the effect of the prior choice on future failures predictions. To this end, hierarchical Bayesian modelling (HBM) was tested on three samples with distinct sizes, while five different prior distributions were considered. Moreover, a beta-binomial distribution was adopted to represent the failure behavior of the considered device. The results show that choosing strong informative priors leads to distinct predictions, even if a larger sample size is considered. The outcome of this research could help maintenance engineers and asset managers in integrating their prior beliefs into the reliability estimation process.Peer reviewe