Prioritization Of Failure Modes Research Articles

A novel failure mode and effect analysis model based on extended interval-valued q-rung orthopair fuzzy approach for risk analysis

The failure mode and effect analysis (FMEA) is an essential technique for risk assessment that can not only determine the potential failure modes, causes of failure and effects, but also expunge the potential failure modes in a system. Imprecise evaluation outcomes may be achieved due to the inherent imperfections caused by the traditional FMEA approach. Accordingly, a hybrid FMEA model based upon the interval-valued q-rung orthopair fuzzy (IVq-ROF) set and extended weighted aggregated sum product assessment (WASPAS) method is propounded to provide decision support for risk assessment. The main dedications of this investigation are as below. First, an innovative IVq-ROF score function is presented to compare the IVq-ROF numbers and the related properties of the proposed function are also discussed. Second, the IVq-ROF Aczel–Alsina operation laws are investigated and then some novel IVq-ROF Hamy mean operators are propounded on based on the advanced operations. Third, the IVq-ROF logarithmic percentage change-driven objective weighting (LOPCOW) method is advanced based on the presented score function to determine the objective importance of the risk criteria and the fuzzy full consistency method (FUCOM) is utilized to ascertain the subjective importance of the risk criteria. Fourth, a novel FMEA model based on extended WASPAS method is presented to ascertain the prioritization of failure modes. Lastly, an empirical about risk assessment in the shipbuilding process is implemented to verify the applicability and feasibility of the designed FMEA model. The comparison analysis and sensibility study are executed to demonstrate the significant merits and stability of the developed model. The best advantage of the presented model is that it not only obtains a consistent outcome and takes into account the interrelations among the risk criteria, but also addresses more problems where the weights of the criteria and experts are not determined in advance.

Probabilistic Fuzzy System for Evaluation and Classification in Failure Mode and Effect Analysis

Failure Mode and Effect Analysis (FMEA) is an essential risk analysis tool that is widely applicable in various industrial sectors. This structured technique allows us to identify and assign priority levels to potential failures that violate the reliability of a system or process. Failure evaluation occurs in a decision-making environment with uncertainty. This study proposes a probabilistic fuzzy system that integrates linguistic and stochastic uncertainty based on a Mamdani-type model to strengthen the FMEA technique. The system is based on analyzing the frequency of failures and obtaining the parameters to determine the probability of occurrence through the Poisson distribution. In addition, the severity and detection criteria were evaluated by the experts and modeled using the Binomial distribution. The evaluation result is a discrete value analogous to the process of obtaining the success or failure of the expert generating the evaluation of 10 Bernoulli experiments. Three fuzzy inference expert systems were developed to combine multiple experts’ opinions and reduce linguistic subjectivity. The case study was implemented in the knitting area of a textile company in the south of Guanajuato to validate the proposed approach. The potential failure of the knitting machinery, which compromises the top tension subsystem’s performance and the product’s quality, was analyzed. The proposed system, which is based on a robust mathematical model, allows for reliable fault evaluation with a simple scale. The classification performed by the system and the one performed by the experts has similar behavior. The results show that the proposed approach supports decision-making by prioritizing failure modes.

A novel hybrid framework for prioritization of failure modes during forging die-design

A novel hybrid framework for prioritization of failure modes during forging die-design

Reliability analysis of intelligent manufacturing systems based on improved FMEA combined with machine learning

Along with the booming of intelligent manufacturing, the reliability management of intelligent manufacturing systems appears to be becoming more significant. Failure mode and effects analysis (FMEA) is a prospective reliability management instrument extensively utilized to manage failure modes of systems, products, processes, and services in various industries. However, the conventional FMEA method has been criticized for its inherent limitations. Machine learning can handle large amounts of data and has merits in reliability analysis and prediction, which can help in failure mode classification and risk management under limited resources. Therefore, this paper devises a method for complex systems based on an improved FMEA model combined with machine learning and applies it to the reliability management of intelligent manufacturing systems. First, the structured network of failure modes is constructed based on the knowledge graph for intelligent manufacturing systems. Then, the grey relation analysis (GRA) is applied to determine the risk prioritization of failure modes. Hereafter, the k-means algorithm in unsupervised machine learning is employed to cluster failure modes into priority classes. Finally, a case study and further comparative analysis are implemented. The results demonstrate that failure modes in system security, production quality, and information integration are high-risk and require more resources for prevention. In addition, recommendations for risk prevention and monitoring of intelligent manufacturing systems were given based on the clustering results. In comparison to the conventional FMEA method, the proposed method can more precisely capture the coupling relationship between the failure modes compared with. This research provides significant support for the reliability and risk management of complex systems such as intelligent manufacturing systems.

Supporting Consensus Reaching on Prioritizing Failure Modes in Reliability Management: The Role of Social-Trust-Driven Rating Modifications Willingness

Supporting Consensus Reaching on Prioritizing Failure Modes in Reliability Management: The Role of Social-Trust-Driven Rating Modifications Willingness

Analyzing the risk of the ammonia storage facility using extended FMEA model based on probabilistic linguistic GLDS method with consensus reaching

Due to the energy crisis, ammonia stands out as an efficient hydrogen energy carrier. However, liquid ammonia's nature of toxicity, corrosiveness, volatility, flammability, etc., make its storage highly risky. It is essential to analyze the risk of the ammonia storage facility for its safety improvement. Failure mode and effect analysis (FMEA) has been a popular risk analysis method due to its ease of implementation. However, traditional FMEA is insufficient to model the uncertainty of risk assessment information, enhance the consensus among experts, aggregate experts' risk evaluation information, and derive reliable risk analysis results. To address these limitations, we introduce a hybrid probabilistic linguistic FMEA framework to analyze failure risk in the ammonia storage facility. First, the probabilistic linguistic terms (PLTs) are introduced to express uncertain risk evaluation information of failure modes. Second, a risk acceptability-based consensus-reaching process is introduced to measure experts' consensus on each failure mode's risk and improve their consensus. Then, a consensus degree-based ordered weighted averaging operator is introduced to fuse experts' risk evaluation information, where experts' weight is determined by their consensus degree. Furthermore, the gained and lost dominance score method is improved on the basis of the expected value of the PLTs to prioritize failure modes' risk. Finally, a case study of the risk analysis for the ammonia storage facility is performed to show the efficiency of the extended FMEA. The case study indicated that condenser cooling water pump failure and failure of level transfer are the top two failure modes and have risk scores of 0.126 and 0.091, respectively.

A method based on q-rung orthopair fuzzy cognitive map and TOPSIS method for failure mode and effect analysis considering risk causal relationships

As a preventive maintenance technique, failure mode and effect analysis (FMEA) is widely applied to determine and eradicate possible failures. In recent years, many FMEA methods have been developed to address the limitations of traditional FMEA methods in failure mode (FM) assessment, weight calculation, and FM prioritization in complex uncertain environments. However, due to the complexity of equipment, systems, services, etc., there is an inevitable influence on causal relationships between FMs, which is rarely considered by existing methods. Therefore, a novel FMEA method, which integrates the technique for order preference by similarity to the ideal solution (TOPSIS) of q-rung orthopair fuzzy set (q-ROFS) and q-rung orthopair fuzzy cognitive map (q-ROFCM), is proposed for reasoning the relationships between FMs. The design of the novel method, named q-ROFCM FMEA, involves three stages. The q-ROFS is applied to evaluate the risk factors for capturing more ambiguous information in the first stage. The q-ROFCM is proposed and the causal relationship between FMs is integrated to describe the impact of the relationships on the evaluation results through the dynamic behavior of FMs in the second stage. In the third stage, the TOPSIS method is applied to determine the priority ranking of FMs based on more reliable similarity measurement results of the projection measure. The proposed new method is applied to the FM risk assessment of power units in the railway turnout system. The competitiveness and effectiveness of the proposed method have been demonstrated by comparison with other classic methods and sensitivity analysis.

Enhancing System Safety and Reliability through Integrated FMEA and Game Theory: A Multi-Factor Approach

This study aims to address the limitations of traditional Failure Mode and Effect Analysis (FMEA) in managing safety and reliability within complex systems characterized by interdependent critical factors. We propose an integrated framework that combines FMEA with the strategic decision-making principles of Game Theory, thereby enhancing the assessment and mitigation of risks in intricate environments. The novel inclusion of the Best Worst Method (BWM) and Pythagorean fuzzy uncertain linguistic variables refines the accuracy of risk evaluation by overcoming the inherent deficiencies of conventional FMEA approaches. Through sensitivity analysis, the framework’s efficacy in identifying and prioritizing failure modes is empirically validated, guiding the development of targeted interventions. The practical application of our methodology is demonstrated in a comprehensive healthcare system analysis, showcasing its versatility and significant potential to improve operational safety and reliability across various sectors. This research is particularly beneficial for systems engineers, risk managers, and decision-makers seeking to fortify complex systems against failures and their effects.

A hybrid integrated multi-criteria decision-making approach for risk assessment: a study of automotive parts industry

PurposeThis paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events.Design/methodology/approachTo address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis.FindingsThis work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes.Originality/valueThis research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts.

Possibilistic chance-constrained data envelopment analysis framework for failure modes and effects analysis

Failure modes and effects analysis (FMEA) is an intensively used risk analysis technique for prioritizing failure modes of products, systems, and services. Traditionally, FMEA assumes deterministic and precise values for risk factors. In engineering scenarios, the observed values of decision-makers on risk factors are, however, potentially stochastic and imprecise in nature due to the lack of adequate knowledge and experience. On the other hand, the structural correlation between risks (or failure modes) and induced factors (e.g., the loss cost and recovery time of products, systems, and services) is rarely reported in FMEA. To this end, we develop a possibilistic chance-constrained data envelopment analysis (DEA) framework to overcome the above deficiencies. In this framework, three differentiated categories of DEA approaches (namely, series, parallel, and network structure DEA) are devised to characterize such structural correlation. More specially, the Me measure is introduced to handle imprecise parameters associated with risk factors. Simultaneously, decision-makers are endowed to express flexible attitudes on these imprecise parameters. Moreover, in order to reveal the mechanism of risk priority of failure modes, the proposed framework enables decision-makers to offer optimistic and pessimistic attitudes on risk input and propagation, respectively. A case study of a production system, along with some comparisons and discussions, is given to demonstrate the effectiveness and merits of the proposed framework. As indicated from this case study, no matter which DEA approach is conducted, the risk priority of failure modes is stable in a stochastic environment.

Assessing the impact of healthcare service risks on healthcare demand under evolving economic and social structures: An improved GLDS decision making method considering risk attitudes

Faced with backward and uneven economic conditions and an irrational and increasingly inequitable social structure, healthcare disparities are widening and the healthcare inequalities are expanding. This can lead to an increasing public demand for healthcare services, posing unprecedented risk, such as resource shortages, quality deterioration and rising healthcare costs. By identifying, managing, and optimizing the aforementioned risks within the healthcare system, it helps the healthcare system better address the challenges of expanding healthcare demands under evolving economic and social structure. To achieve this, this paper employs failure mode and effects analysis (FMEA) to identify severe failure modes in healthcare services. However, traditional FMEA methods possess limitations in assessing the dynamics of trust relationships and interaction effects among experts within social networks. Therefore, this paper proposes an improved gained and lost dominance score (GLDS) based social network decision-making method considering risk attitudes for FMEA to prioritize failure modes in healthcare services. First, transform linguistic risk assessment information from an interdisciplinary team of experts based on probabilistic linguistic term sets handle uncertain efficiently. Meanwhile, experts’ influence is reflected by an extended PageRank algorithm with trust degrees based on social networks analysis. Next, an improved GLDS method considering the differentiated risk attitudes of individuals and groups is developed to prioritize failure modes. Especially, to solve the problems of soft preference and incomparability relation among failure modes, which are ignored by the traditional GLDS method, a conflict analysis is established to determine the preference, indifference, and incomparability relations. Finally, the practicality of the proposed method is illustrated by the healthcare service risk case in the emergency department and the effectiveness of the developed method is validated by comparison and sensitivity experiments. The results suggest that failure to update labels showing patient severity promptly is the most dangerous. In addition, it would be more economically efficient to analyze the non-comparable failure modes facing different risk factors.

Fuzzy-Based Failure Modes, Effects, and Criticality Analysis Applied to Cyber-Power Grids

Failure modes, effects, and criticality analysis (FMECA) is a qualitative risk analysis method widely used in various industrial and service applications. Despite its popularity, the method suffers from several shortcomings analyzed in the literature over the years. The classical approach to obtain the failure modes’ risk level does not consider any relative importance between the risk factors and may not necessarily represent the real risk perception of the FMECA team members, usually expressed by natural language. This paper introduces the application of Type-I fuzzy inference systems (FIS) as an alternative to improve the failure modes’ risk level computation in the classic FMECA analysis and its use in cyber-power grids. Our fuzzy-based FMECA considers first a set of fuzzy variables defined by FMECA experts to embody the uncertainty associated with the human language. Second, the “seven plus or minus two” criterion is used to set the number of fuzzy sets to each variable, forming a rule base consisting of 125 fuzzy rules to represent the risk perception of the experts. In the electrical power systems framework, the new fuzzy-based FMECA is utilized for reliability analysis of cyber-power grid systems, assessing its benefits relative to a classic FMECA. The paper provides the following three key contributions: (1) representing the uncertainty associated with the FMECA experts using fuzzy sets, (2) representing the FMECA experts’ reasoning and risk perception through fuzzy-rule-based reasoning, and (3) applying the proposed fuzzy approach, which is a promissory method to accurately define the prioritization of failure modes in the context of reliability analysis of cyber-power grid systems.

An ELICIT information-based ORESTE method for failure mode and effect analysis considering risk correlation with GRA-DEMATEL

Failure mode and effect analysis (FMEA) is one of the most powerful reliability analysis techniques for identifying and preventing potential risks across various fields. Current FMEA methods, while effective, still present several shortcomings. For example, using experts’ subjective pairwise comparisons of risk factors to determine their weights reduces the stability of the result; different relationships among failure modes are often ignored. To improve the performance of FMEA, multi-criteria decision-making (MCDM) methods have been employed to support risk evaluation and prioritization in recent years. This paper proposes a novel FMEA method by exploring several MCDM techniques. First, the Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) are utilized to generate group risk assessments under uncertainty. Then, grey relation analysis (GRA) is incorporated into the decision-making trial and evaluation laboratory (DEMATEL) method to objectively determine the weight of risk factors. Afterward, the traditional ORESTE (organísation, rangement et Synthèse de données relarionnelles (in French)) method is generalized to the ELICIT environment to prioritize the failure modes, in which the Besson’s ranking is replaced by deviation measure for more accurate ranking results. Finally, a case study of FMEA for electro-mechanical actuators is presented to illustrate the effectiveness of the proposed method. The results indicate that our approach can express risk information more flexibly, determine the weight of risk factors more objectively, and prioritize failure modes more reasonably.

New risk assessment and prioritization failure modes based approach in a gas turbine system

The dependability occupies a strong place in the performance achievement of the system. It describes the mechanisms that lead to failures of systems. Failure mode and effects, analysis (FMEA) is a classical safety technique widely used in several safety critical industries. This method uses the risk priority number (RPN) to assess the criticality value and prioritize failure modes. However, it suffers from some drawbacks regarding the situation where the in-formation provided is ambiguous or uncertain. Thus, in this work, a fuzzy criticality assessment based approach is carried out to evaluate the failure modes of the relevant system and gives an alternate prioritizing to that obtained by the conventional method. In addition, a novel hybrid approach is proposed that combines the grey relational approach (GRA) and fuzzy analytic hierarchy process. This approach offers a new ranking of failure modes by solving the shortcoming concerning the lack of established rules of inference system which necessitate a lot of experience and shows the weightage or importance to the three parameters severity, detection, and frequency, which are considered to have equal importance in the traditional method. A real case study from a gas turbine system provides encouraging results regarding the risk evaluation and prioritizing failures mode with handling different forms of ambiguity, uncertainty, and divergent judgments of experts.

An evidence theory-based large group FMEA framework incorporating bounded confidence and its application in supercritical water gasification system

Supercritical water gasification (SCWG) is an advanced technology for sewage sludge treatment, which can effectively remove hazardous substances in it and realize the resource utilization of sludge. Given the harsh operating environment and complex operating process, it is particularly critical for SCWG system to adopt failure mode and effects analysis (FMEA) to ensure its reliability and security. In particular, the risk management process of SCWG system needs to include two considerations: the evaluation process requires the participation of a large number of team members (TMs) with different professional knowledge and operational skills, and the cross-correspondence between the factors within the system cannot be ignored in the analysis process. Thus, an FMEA framework is established to meet practical needs, which can model TMs’ personality traits and reflect the interdependencies between factors. First, assessments in the form of probability linguistic term sets (PLTSs) are converted into mass functions due to the excellence of evidence theory in information aggregation. Second, a bounded confidence-based clustering method is developed to consider TMs’ willingness to interact during clustering. Additionally, evidence conflicts are managed using a new discounting method that captures TMs’ stubbornness. Besides, the cross-correspondence between factors is analyzed by an evidence theory-based DEMATEL method. Further considering TMs’ bounded rationality, regret theory (RT) is used to distinguish and prioritize failure modes (FMs). Finally, a case study of an SCWG system is successfully solved by applying the proposed framework. The effectiveness and superiority are subsequently demonstrated by a series of analyses and discussions.

Fuzzy analytic hierarchy process‐based risk priority number for risk assessments of commissioning process of a ring gantry LINAC

We propose a fuzzy analytic hierarchy process (AHP)-based risk priority number (RPN) method in failure modes and effects analysis (FMEA) to overcome the shortcomings of traditional RPN-based FMEA. Our research group has previously published the FMEA to mitigate the failure modes (FMs) for the commissioning process of a ring gantry LINAC. However, inter-relationships among FMs were observed in high ranked FMs due to a heavy reliance on imaging system. Fuzzy AHP was applied to determine relative weights of risk impacts based on inter-relationships among FMs. Since the time sequence dependency is a major factor for risk factors, a hierarchical structure of AHP was used to reflect the directional impacts such as causal influence and feedback loop. Two fuzzy weighted RPNs, called (RPNW and FRPNW , were calculated depending on the input values of severity (S), occurrence (O), and probability of not being detected (D) from the evaluators. The RPNW used numerical values, whereas the fuzzy values were used for FRPNW . Both RPNs were calculated by multiplying the weighted O, S, and D using the fuzzy AHP method. The differences between the two fuzzy RPN rankings are due to inherent fuzzy uncertainty and deviations in O, S, and D values submitted by the evaluators. Considering all results of traditional and fuzzy-based FMEA, the two most highly ranked FMs were identified: errors in determining the non-isocentric SSD and SSD from MV images because of the unique features of the ring gantry LINAC. This study has demonstrated the feasibility of the use of a fuzzy AHP-based RPN to perform comprehensive analysis and prioritization of FMs. The risk analysis using fuzzy AHP can be improved and/or refined based on the department's specific workflow and clinical preferences taking various priority weighting approaches into account.

A new linguistic preference relation-based approach for failure mode and effect analysis with dynamic consensus reaching process

In the literature, a lot of alternative failure mode and effect analysis (FMEA) models have been developed to overcome the deficiencies and enhance the performance of traditional FMEA method. However, majority of them evaluated the risk of failure modes directly and cannot take the consensus levels among experts into account. Thus, this article aims to propose a new approach for FMEA via integrating hesitant linguistic preference relations (HLPRs) and an extended dynamic consensus model. Based on the HLPRs, the risk evaluations of experts are described via pairwise comparison of the identified failure modes. An extended dynamic consensus model is proposed to measure the weights of FMEA experts and revise the individual preference information until reaching an acceptable consensus. Finally, the applicability and effectiveness of the proposed FMEA are verified with a practical case study on the risk analysis of supercritical water gasification systems. It was shown that the new approach being proposed provides a practical and flexible way for the risk assessment and prioritization of failure modes in FMEA.

Use of failure mode and effect analysis to reduce patient safety risks in purchasing prescription drugs from online pharmacies in China.

BackgroundOnline pharmacies have gradually penetrated the market, but pose risks to patients' health. Failure Mode and Effect Analysis (FMEA) is an effective and reliable method for reducing pharmacy and medication risks. The purpose of this study was to conduct a prospective risk analysis of the process of purchasing prescription drugs from online pharmacies in China to guarantee drug quality and patient safety.MethodsThe FMEA was performed at Sichuan University, China. A multidisciplinary team was assembled comprising a leader, four regulators, four pharmacists, two experts, etc. The process was composed of eight subprocesses: searching for prescription drugs, submitting medication requirements, completing patient information forms, dispensing, delivering, etc. Brainstorming was used to identify and prioritize failure modes, propose corrective actions, and reduce risks. Risk priority numbers were the main criterion and were obtained by multiplying three scores: severity, occurrence and detectability, which were scored by the team The team proposed corrective actions for each selected failure mode.ResultsA total of forty-one potential failure modes were identified, and the causes, effects, and corrective actions of the 30 top failure modes were analyzed. The highest risk value was assigned to “photocopies of paper prescriptions uploaded were reused by patients.” Three failure modes for the S value of 5 were: “drugs are eroded and polluted by moisture or insects in the process of transportation,” “the qualification information of the pharmacies were absent or fake,” and “pharmacists fail to check prescriptions in accordance with Prescription Administrative Regulation.” Of the top failure modes, 36.67% were from Step 5, delivering the drug. After taking corrective measures to control risks, the risks reduced by 69.26%.ConclusionThe results of this study proves that the FMEA is a valuable tool for identifying and prioritizing the risks inherent in online pharmacies. This study shows that there are many potential risks in the process of purchasing prescription drugs from online pharmacies, especially in the drug delivery stage. Enhanced training and the introduction of smart devices may minimize risks. Online pharmacies and Chinese regulators should consider these findings for risk mitigation and the improvement of regulations pertaining to online pharmacies.

Quality Tools and Strategy for Critical Alerts Process Improvements to Ensure Patient Safety.

Objectives A number of regulatory and accrediting bodies require the reporting of critical results on a timely basis (immediately or within the time frame established by the laboratory) to "the responsible, licensed caregiver" as timely notification of critical laboratory results can pivotally affect patient outcome. The aim of the study was to decrease the turnaround time (TAT) of critical result notification along with assurance of notification to the concerned caregiver or clinicians. The objectives was 30% reduction in the critical value notification TAT and identify factors associated with delayed reporting and root cause analysis for these factors by application of quality tools. Materials and Methods The study was conducted at the Institute of Human Behavior and Allied Sciences, Delhi, a tertiary center teaching Hospital, from April 2019 to June 2021. A value streamed Process Map of critical alert was prepared. The incidents related to failure were presented through Pareto chart. The possible causes were analyzed through the fishbone model. The failure mode prioritization was executed with Failure Mode and Effect Analysis (FMEA). Through extensive brainstorming, appropriate and feasible corrective actions were implemented. The effectiveness of the implemented plan was analyzed by reassessing the TAT of critical alert and feedback received by clinical caregivers. Results After implementation of corrective action plan using quality tools for 3 months, the average critical alert TAT was reduced to 21 minutes from 30 minutes (30% reduction). The median critical alert TAT for ICU, emergency, and IPD were reduced to 3 minutes (IQR: 1-7). During the pilot project, 156 critical value data were sent for feedback with treatment plan but was received only for 88 patients (56%). Conclusion Comprehensive utilization of quality tools has a potential role in patient safety by reducing the critical alert TAT as well as establishing an effective communication between laboratory personnel and clinicians.

D-S evidence based FMECA approach to assess potential risks in ballast water system (BWS) on-board tanker ship

Ballast water is essential for cargo ships since it stabilizes vessels at sea. Most ships are equipped with a ballast water system (BWS) to maintain safe operating conditions. This paper attempts to perform a risk assessment for the BWS on-board tanker ship as it poses a major threat to the operational safety of the ship, marine environment, and cargo. To achieve this purpose, the paper utilizes a robust methodology integrating D-S evidence (Dempster-Shafer) theory and FMECA (Failure mode effects and criticality analysis). In the methodology, while the D-S evidence theory introduces a proper mathematical framework to handle epistemic uncertainty in the assessment of risk parameters and to prioritize failure modes as intended, the FMECA is capable of evaluating system potential failures and their causes. Hence, the risk priority number (RPN) can be calculated to assess potential hazards and their consequences in BWS on-board ships. Besides its theoretical insight, the paper contributes to marine safety inspectors, safety researchers, and HSEQ (Health, Safety, Environment, and Quality) managers to identify potential hazards, effects, and consequences in case of BWS failures on-board tanker ships.