Risk-based Maintenance Research Articles

The effect of the risk-based maintenance approach in reducing workplace risks Comparative research in a number of laboratories in Nineveh Governorate

Abstract. The research sought to measure the nature of the role of the risk-based maintenance approach in reducing workplace risks in a number of the researched laboratories. The research problem is represented by the presence of different types of workplace hazards, and according to the common classification, there are seven types in the researched laboratories. The research problem was defined by the following question: Does the methodology contribute Risk-based maintenance reduces workplace risks in the researched laboratories? The questionnaire was relied upon to collect the required data in three production laboratories: the Mishraq Sulfur Plant, the Qayyarah Refinery, and the Mosul Gas Power Station, with a sample size of (33,34.25), respectively. A comparison was made between the results achieved in these researched laboratories with the aim of identifying the strengths and weaknesses of each. A field of supporting and enhancing strengths and developing solutions and treatments for weaknesses . The research concluded that there is a degree of interest in risk-based maintenance procedures according to the response rates achieved by the fields investigated, in addition to the presence of somewhat acceptable correlations and influence between the research variables. The research suggested making more efforts to support and implement the risk-based maintenance methodology. Color: It is one of the modern concepts in the field of maintenance and plays a vital and important role in preventing many types of work risks in the investigated environments.

NO-CONTACT DIAGNOSTICS IN THE FUNCTION OF RISK-BASED MAINTENANCE ON A WIND GENERATOR MODEL

When it comes to risk-based maintenance of technical systems, selecting a diagnostic procedure is the weakest link in the chain of steps that guarantee the system's availability, sustainability, and safe operation while utilizing its working characteristics.The model of a wind generator, which by virtue of its technological structure represents an increasingly widespread system in the geographical and topographical sense, was used to examine diagnostics based on non-contact, remote collection of pertinent information, which is essential for assessing the state of the technical system. In addition, the fact that the system is not physically accessible to the maintenance teams emphasizes the necessity of taking prompt action to rectify work in order to preserve production capacity and the security of the exploitation process.When applied to the wind generator model, proactive maintenance based on risk allows for enhanced interconnected machine efficiency, optimal maintenance process planning, and real-time production process control. These factors contribute to the advancement and refinement of the contemporary 4K industry philosophy.The concept of remote diagnostics in complex technological structures is of interest to the authors. The goal is to process information about the condition through the lens of risk, paying particular attention to the diagnostic method's reliability and improving prediction accuracy in the maintenance of industrial plant machinery.

Increased Availability Risk-Based Maintenance ROV's Hydraulic System

Increased Availability Risk-Based Maintenance ROV's Hydraulic System

Joint risk-based maintenance strategy and integrated monitoring approach for manufacturing system operational process considering workpiece quality

The fluctuation of various parameters in the production process increases the risk of generating unqualified products and suffering from systematic failures and downtimes, and the preventive maintenance and control of potentially faulty machines in manufacturing systems should consider the continuous increase of operational risk. Therefore, to proactive control the manufacturing system operational risk, a joint model of risk-based maintenance (RBM) and integrated monitoring is proposed. The proposed model formulates an appropriate maintenance strategy and appropriate control charts for monitoring the manufacturing process, guaranteeing the stable operation of the manufacturing system. First, considering the co-effect of the production equipment state and workpiece quality, a novel connotation of the manufacturing system mechanism’s operational risk is proposed, and the RBM framework is constructed. Second, considering the manufacturing system operational state, an integrated maintenance decision-making model based on different risk scenarios is established using a dynamic monitoring scheme based on EWMA control charts. Third, a particle swarm optimisation algorithm is adopted to optimise the proposed model to obtain the optimal maintenance strategy for production equipment and monitoring parameter for workpiece quality. Finally, the effectiveness of the proposed approach is illustrated with a case study on a subway current receiver production line.

A spin on active learning analysis for health monitoring.

Decisions on the operation and maintenance of high-valued structures are critical, collecting and analysing data that can inform these decisions motivates the development of structural health monitoring (SHM) and tool monitoring (TM) systems. Descriptive health-state information labels for measured data points are crucial for successful monitoring systems. However, the labels can be expensive to collect and are often unavailable. Due to this problem, fully-supervised machine learning is limited in it’s application to monitoring systems which gives rise to unsupervised and semi-supervised approaches, one of these approaches is active learning. Active learning aims to highlight which unlabelled data points have the highest value of information and would be most informative to the model if they were labelled. For risk-based active learning in SHM, the model is used to advise some maintenance decision process where querying a data point means an expert inspecting the structure to determine its health-state. The current paper applies an active learning regression technology to a machining tool health case study. The algorithm queries data points that are most important in helping determine the health-state of the tool. Querying the most informative datapoints can drastically reduce the costs of a monitoring system. The regression analysis results are used to estimate tool wear and inform a risk-based maintenance decision process. This addresses the problem of when to replace a tool based on its health status, which has applications for many monitoring system problems.

A data quality management framework for equipment failure risk estimation: Application to the oil and gas industry

In engineering asset management, accurate failure risk estimation is essential for averting equipment breakdowns and optimizing risk-based maintenance strategies. Data quality and model fitting are two critical sources of prediction uncertainty in risk estimation. While much attention has been devoted to model fitting for risk estimation, the critical role of data quality has often been overshadowed. To bridge this research gap, this paper presents a novel data quality management framework tailored for industrial equipment failure risk estimation. The framework covers the steps from data to model. It consists of the following main phases: data development, data quality assessment, data quality requirement decision-making, data quality improvement, and risk estimation model development. The framework provides detailed guidelines that can facilitate data practitioners to build individualized data quality requirement decision-making models for failure risk estimation of their equipment. The decision-making model can measure the adequacy of existing data to build a risk estimation model that meets the specified requirements and further determines the best risk estimation model given the available data. A case study using actual data collected during oil well drilling operations from multiple oil fields demonstrates the practicality and effectiveness of the framework. In this case study, four risk estimation models are compared, including two baseline models (mean time to failure and median time to failure) and two machine-learning models (quantile regression and hidden Markov model). In addition, a decision tree-based decision model is developed to determine whether the data quality meets the requirements and the best risk estimation model in case the data quality meets the requirements.

Risk-based maintenance strategy for oil transfer pipelines using an intuitionistic fuzzy computational approach

The safety and reliability of oil transfer pipelines are crucial, as pipeline failures can severely affect the environment. As pipeline risk management is dynamic and uncertain, traditional maintenance approaches often fall short. This research paper proposes an Intuitive Fuzzy Computational Approach (IFCA) for oil transfer pipeline maintenance (RBM). To develop a targeted and nuanced maintenance plan, Failure Modes and Effects Analysis (FMEA) is employed. Pipeline equipment is hierarchically organised according to their failure mode criticality, which guides maintenance design. This paper leverages the context of an Interval Valued Intuitionistic Fuzzy (IVIF) environment to streamline the hierarchical classification of oil transmission pipeline instruments. The objectives are threefold: (1) to develop an intuitionistic fuzzy risk assessment model, (2) to design a modified algorithm for maintenance prioritisation, and (3) to develop a decision support system. Additionally, the research incorporates the IVIF-SWARA method to enable flexible adjustments in the alignment of the pipeline instruments. By addressing the specific challenges of oil transfer pipelines, this research aims to contribute to advancing the field of pipeline maintenance and risk management. The proposed RBM strategy, underpinned by the IFCA framework, is expected to enhance pipeline reliability and mitigate the risks associated with pipeline failures. Abbreviations: RBM: Risk-Based Maintenance; IFCA: Intuitionistic Fuzzy Computational Approach; FMEA: Failure Modes and Effects Analysis; IVIF: Interval Valued Intuitionistic Fuzzy; SWARA: Stepwise Weight Assessment Ratio Analysis; RCM: Reliability-Centered Maintenance; RBI: Risk-Based Inspection; CBM: Condition-Based Maintenance; IFS: Intuitionistic Fuzzy Set; HFPR: Hesitant Fuzzy Preference Relation; IVIFPHA: Interval-Valued Intuitionistic Fuzzy Power Heronian Aggregation; MCDM: Multi-Criteria Decision-Making; S C O i : Accuracy Function of Occurrence; S C S i : Accuracy Function of Severity; K ( a ~ ) : Failure Probability Index; L W D : Minimum Allowable Weight of The Failure Detection Index; WS, WO, WD: Weight Assigned to The Failure Outcome; RP V j : Relative Priority Value; OREDA: The Offshore Reliable Data Handbook; IVIFPWHA: Interval-Valued Intuitionistic Fuzzy Power Weight Heronian Aggregation

Stochastic finite element-based reliability of corroded pipelines with interacting corrosion clusters

The performance of corroded carbon steel pipelines over the course of their design life is generally assessed by probabilistic variables with explicit limit state functions, rather than the realistic representation with stochastic spatial variability and implicit failure considerations. This could be due to the complexities associated with the uncertainty quantification and performance estimation approaches. The consideration of random process representation of corrosion defect propagation and material properties, along with computationally effective implicit formulation is expected to lead to accurate reliability outcomes. This paper proposes a stochastic-based reliability framework considering suitable failure modes represented with surrogate models, that lead to time-variant reliability estimation. The approach combines surrogate computational model with scalar random variables and random field discretisation of underlying characteristics to generate experimental designs and corresponding surrogate models over a time period, which are used to derive reliability estimates. The outcomes of this approach are compared with the results of explicit time-dependent functions. It was observed that reliability estimates of the corroded pipeline change rapidly after the fifth year, providing a much lesser probability of failure (of 3.08×10−3 at 30th-year) compared to the existing models (of 1.80×10−2 at 30th-year), thereby providing an effective pathway for risk-based maintenance and management.

Ixazomib, Lenalidomide, and Dexamethasone (IRD) Treatment with Cytogenetic Risk-Based Maintenance in Transplant-Eligible Myeloma: A Phase 2 Multicenter Study by the Nordic Myeloma Study Group.

Scarce data exist on double maintenance in transplant-eligible high-risk (HR) newly diagnosed multiple myeloma (NDMM) patients. This prospective phase 2 study enrolled 120 transplant-eligible NDMM patients. The treatment consisted of four cycles of ixazomib-lenalidomide-dexamethasone (IRD) induction plus autologous stem cell transplantation followed by IRD consolidation and cytogenetic risk-based maintenance therapy with lenalidomide + ixazomib (IR) for HR patients and lenalidomide (R) alone for NHR patients. The main endpoint of the study was undetectable minimal residual disease (MRD) with sensitivity of <10-5 by flow cytometry at any time, and other endpoints were progression-free survival (PFS) and overall survival (OS). We present the preplanned analysis after the last patient has been two years on maintenance. At any time during protocol treatment, 28% (34/120) had MRD < 10-5 at least once. At two years on maintenance, 66% of the patients in the HR group and 76% in the NHR group were progression-free (p = 0.395) and 36% (43/120) were CR or better, of which 42% (18/43) had undetectable flow MRD <10-5. Altogether 95% of the patients with sustained MRD <10-5, 82% of the patients who turned MRD-positive, and 61% of those with positive MRD had no disease progression at two years on maintenance (p < 0.001). To conclude, prolonged maintenance with all-oral ixazomib plus lenalidomide might improve PFS in HR patients.

A risk-based maintenance decision model for subsea pipeline considering pitting corrosion growth

Pitting corrosion growth of subsea pipeline can cause pipeline leak, resulting in the huge economic losses. This study presents a risk-based model to implement the optimal maintenance decision-making of subsea pipelines subject to pitting corrosion. Firstly, a pitting corrosion growth prediction model is built using DeWaard 95 model and dynamic Bayesian network (DBN). Subsequently, the probability of failure, i.e., PoF and consequence of failure, i.e., CoF for pipeline are estimated to obtain the risk profile of corroded pipeline. The maintenance cycle of pipeline is estimated based on total utility function and the acceptable risk. Eventually, a Bayesian influence diagram (BID) model and expected utility theory (EEU) are implemented to determine maintenance decision of corroded pipelines. The methodology is illustrated by a case study, which indicates that it can be a useful tool for maintenance decisions for subsea pipeline subject to pitting corrosion growth.

Hydrogen in Glass Sector: A Comparison between Risk-Based Maintenance and Time-Based Maintenance Approaches

Hydrogen can be the key to decarbonisation, even for energy-intensive industries. The glass sector, for instance, burns natural gas to reach high temperatures necessary to melt the raw materials, leading to a considerable amount of CO2 emissions. Introducing hydrogen in a new sector brings many challenges in technological development, but of no less importance are the safety issues due to its hazardous properties. Hydrogen is highly flammable and can interact with many metals. Avoiding hydrogen leaks or, even worse, preventing catastrophic losses should be a priority: adopting proper maintenance planning is an effective means. In this study, a comparison of two different maintenance approaches is proposed in a line supplying hydrogen to the furnace case study. Time-based maintenance is a consolidated technique that programs the operations based on the reliability of the data on the pieces of equipment. On the other hand, the Risk-Based Maintenance approach leads to planning the maintenance activities based on the risk evaluation. Applying this approach to a hydrogen facility for the first time represents the novelty of this study. The advantages of adopting a methodology based on risk evaluation when handling a safety-critical system are highlighted.

Towards a Business Intelligence Application for Evidence-based Maintenance

Maintenance plans are crucial to ensure the ongoing operation of the assets, extending their lifespan, and minimizing costs as much as possible. Various methods, including Reliability-Centered Maintenance and Risk-Based Maintenance, are available in the literature for formulating such plans. However, these approaches may pose significant challenges, particularly for Small and Medium Enterprises (SMEs). In this context, Evidence-based Maintenance (EbM) has emerged as a promising method, aiming to achieve comparable operational outcomes while minimizing initial overhead. Leveraging the tools provided by digital transformation, this work proposes the utilization of Business Intelligence (BI) to support the development of EbM. A methodology is introduced for this purpose and validated through a case study. Nevertheless, its effective implementation requires leadership and commitment to grant consistency through the standardization of vocabulary, formats, and processes within the organization.

Sewage Treatment Facility Maintenance: A Risk-based Approach for High-rise Residential Property

Although fuzzy-failure modes and effects analysis (Fuzzy-FMEA) is one of the risk-based maintenance approaches that has been used to devise or to improve an existing preventive maintenance program in various industries and settings, applying the technique in the context of sewage treatment facility (STF) is lacking. The proponent investigated the cause of failing effluent parameters of a high-rise residential property through the combination of the Ishikawa Diagram and 5-Whys, then the technique was applied and improved using the data gathered from qualified respondents with different exposure, and backgrounds, among others who answered 10-fuzzy scales regarding the severity, occurrence, and detectability of each failure modes, and ranked the importance of each chamber of an SBR-type STF. Results revealed that the Refined FRPN values emphasized the failure modes with relevance to each other by obtaining values within their functional counterpart resulting in a more apt categorization and prioritization than the FRPN. The paper shows that Fuzzy-FMEA is successful in devising a risk-based approach for SBR-type STF for high-rise residential property with an effective means of addressing the aggravating effluent parameters in the maintenance aspect to comply with the Philippine Department of Environment and Natural Resources (DENR) Department Administrative Order (DAO) 2016-08.

Modular asset management framework based on Petri-net formalisations and risk-aware maintenance

Probabilistic risk analysis (PRA) is fundamental in safety assessment. Current PRA tools face notable limitations for complex systems, such as the heavily reliance on historical failure data. Moreover, existing tools cannot replicate complex asset management strategies easily, leading to inefficiency when analysing a multitude of scenarios. This paper addresses these limitations by introducing a Coloured Hybrid Petri Net (CHPN) framework for the PRA of complex systems. The framework integrates a hybrid system to capture the complex nature of degradation. Moreover, unlike other tools, the framework is modular. This provides a flexible approach to scenario modelling and ensures a more accurate understanding of the system. This paper also investigates the effect of maintenance policy on system performance. The paper evaluates condition-based maintenance (CBM) to two-levels of risk-based maintenance (RBM). The paper also presents a risk-aware policy that integrates a system-level RBM and CBM to capture the dynamic between components condition, health and their influence on system performance. This ensures a holistic view of system's safety and reliability. By integrating advanced modelling techniques and maintenance policies, the CHPN framework provides a new dimension to PRA to enable more accurate risk assessments, informed asset management strategies, and enhanced safety assurance for critical infrastructure.

ANALISA PERAWATAN INTAKE PUMP DENGAN MENGGUNAKAN METODE RISK BASED MAINTENANCE (RBM)

Intake Pump is one of the important equipment in the steam power plant of 3x10 MW electrical generator which functions to supply raw water to production of demin water, domestic water and service water. In the process of producing water through the intake pump there are several components of the intake pump that often experience damage, this can be a loss for the company, therefore requiring additional processing. In this study, the Risk Based Maintenance (RBM) method was carried out to achieve optimal maintenance by knowing the impact and risk of failure according to important intake pump components, namely impeller, bearing and shaft. Based on the analysis results of the RBM calculation, the consequences of risks accepted is low to 0.05% by the company that achieved to 221,498,196.507 Indonesian rupiah it’s to 0.13% of production capacity per year. This value exceeds the acceptance risk tolerance of 0.13% which has become a company provision. Therefore, it is necessary to plan the proposed maintenance period from the existing policy, which is carried out 48 times a year for each critical component such to reducing the number of consequences and risks to 198,002,597 Indonesian Rupiah or equal to 0.01%.

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.

Information Fusion and Data Augmentation for Risk-based Maintenance Optimization of Hydrogen Gas Pipelines

Demand for energy is increasing every year and hydrogen is being seen as a good alternative to conventional natural gas. The current focus is on the use of existing pipeline infrastructure for the transport of hydrogen gas, and it is necessary for us to ensure the safe and efficient operation of the pipeline infrastructure given the risks posed by hydrogen. Pipeline integrity management is critical for hydrogen transport and there are knowledge gaps for the impact of hydrogen on the pipeline integrity and operational considerations, thus hindering the pipeline operators from adopting hydrogen into their networks. To realize the concept of transporting hydrogen through existing pipeline systems, it is necessary to have reliable risk assessment and maintenance optimization frameworks in place. A Bayesian network methodology is proposed to fuse information from multiple sources obtained by multimodality diagnosis of pipe materials and Bayesian updating will be incorporated to reduce the uncertainty arising from different random variables. Risk assessment of the pipeline systems will be carried out based on the posterior distributions of the random variables. Given the predicted risk level, we then propose a risk-based maintenance optimization framework to minimize the maintenance costs while ensuring the safe operation of the pipeline systems.

Reliability and Risk Centered Maintenance: A Novel Method for Supporting Maintenance Management

Proper maintenance planning is critical for maintenance management to contribute to increasing availability, ensuring quality requirements, and controlling the safety and environmental risks associated with physical assets. As supporting tools for developing maintenance strategies, Reliability-Centered Maintenance (RCM) and Risk-Based Maintenance (RBM) methods are currently used in several organizations. Nevertheless, these strategies are often approached separately although they are complementary. In this context, this paper proposes a novel method that effectively integrates RCM and RBM by adapting the traditional RCM method to incorporate risk management into maintenance planning decision-making to support maintenance management. The proposed Reliability and Risk Centered Maintenance (RRCM) method allows organizations to determine maintenance plans that ensure the reliability of the physical assets while considering and prioritizing the risks associated with their potential functional failures. The proposed method was demonstrated through a case study considering the operational context of a hydroelectric power plant. The results show the ability of RRCM to assist in the development and implementation of maintenance plans oriented to reliability, risk, and cost.

Data-driven model to predict burst pressure in the presence of interacting corrosion pits

This paper presents a data-driven approach to predict the pipelines’ corrosion-induced Burst failure. In this approach, different aspects of pit growth progression and spatial distribution of pits are simulated. The proposed approach takes advantage of population characteristics to model these aspects of the degradation paths for each pipe section down to the size of single joints. The insights obtained from simulations are used to project the degradation of each pipe section. Understanding corrosion behavior and field data are used to model the corrosion-related parameters such as corrosion pit dimensions, probability and time of initiation, and location. The failure is modeled using the probabilistic simulation considering degradation rate, interactions among pits, and material properties as stochastic variables. The proposed approach and included models are tested using multiple real-life inline inspection datasets. Validation of predicted properties shows prediction errors ranging from 3%–10% depending on the three remaining strength calculation approaches. This work aimed to serve as an important tool for risk-based maintenance prioritization, inspection interval assessment, and the fitness of service assessment of pipelines.

INTEGRASI METODE FUZZY ANALYTICAL HIERARCHY PROCESS (FAHP) DALAM PEMILIHAN KEBIJAKAN RISK BASED MAINTENANCE PADA MESIN DH2100

This study discusses the integration of Fuzzy Analytical Hierarchy Process (FAHP) method in the selection of Risk Based Maintenance (RBM) policy for DH2100 Machine at PT Fajar Putra Plasindo. The objective of this research is to enhance the effectiveness of maintenance strategies and optimize decision-making in RBM policy selection. The FAHP method is employed to address the uncertainties and complexities in maintenance decision-making. The results showed that a risk value of Rp. 83,365,100 was obtained or with a risk percentage of 0.1%. the percentage of the risk exceeds the established tolerance limit. So then the selection of maintenance actions is carried out using the FAHP, from the calculation results it is found that Condition Based Maintenance maintenance actions are the right maintenance actions for the DH2100 Machine.