Noisy OR-gate Research Articles

Risk assessment of belt conveyor belt in thermal power plant based on Bow-tie model and uncertainty Fuzzy Dynamic Bayesian Network

Abstract In thermal power plants, coal conveyor belts pose significant risks that jeopardize the stability of the energy supply, underscoring the need for effective risk management. To address the complexity, uncertainty, and polymorphism issues in belt conveyor systems, we introduce a BT-UFDBN risk analysis method specifically for coal conveyor belts. This method develops a typical Bow-tie model, identifies potential risk factors for unplanned stoppages, and utilizes fuzzy evaluation methods and an improved SAM method to determine prior probabilities. The Bow-tie (BT) model is then mapped into a Dynamic Bayesian Network (DBN). To manage uncertainties within the DBN, the Leaky Noisy-OR gate model, stationarity, and first-order Markov assumptions are employed to ensure the model’s validity and practical relevance. This paper uses a belt conveyor system from a thermal power plant as a case study to validate the model’s effectiveness in predicting accident consequences, diagnosing fault causes, and proposing targeted preventive measures for identified weak points. The study provides theoretical guidance for risk management of coal conveyor belts in power plants.

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks

This study aims to address quality issues in the production of prefabricated steel structural components for buildings by investigating challenges in quality risk assessment. It identifies key factors contributing to quality problems and establishes an evaluation index system. Traditional methods encounter limitations in handling uncertainty and conducting quantitative analysis. Therefore, the fuzzy Bayesian network (FBN) theory is utilized to perform a probabilistic analysis of quality risks during the production phase. This research achieves a more accurate and dynamic risk assessment by integrating the strengths of fuzzy logic and Bayesian networks (BNs) and by utilizing expert knowledge, the similarity aggregation method (SAM), and the noisy-OR gate model. The study reveals that factors such as the “low professional level of designers”, “poor production refinement”, and “poor storage conditions for finished products” have a significant impact on quality risks. This study offers a scientific risk assessment tool designed to address the quality control challenges commonly experienced in the manufacturing of steel structural components. Identifying the critical risk factors that influence quality empowers actual production enterprises to develop risk management strategies and improvement measures in a more focused manner, thereby facilitating more effective resource allocation and risk prevention and control. Consequently, this approach has a significant impact on enhancing the overall production level and quality within the industry.

Incorporating ergonomic and psychosocial stressors: A comprehensive model for assessing miners’ work-related musculoskeletal disorders

Although the influence of ergonomic and psychosocial stressors on work-related musculoskeletal disorders (WMSDs) has been widely recognized, a risk assessment method that comprehensively considers these stressors is still lacking. As such, this study developed a fuzzy Bayesian network (FBN) model that incorporated a comprehensive set of ergonomic and psychosocial stressors to investigate how they interacted to affect frontline miners’ WMSDs risk. A methodology combining fuzzy set theory, Leaky Noisy-OR gate, and an expert weight dynamic adjustment algorithm was employed to deal with fuzziness, uncertainty, limited data, and conflicting judgments and to provide a probabilistic assessment. Meanwhile, the FBN was validated using 10-fold cross-validation through data collected from 542 frontline miners. Results indicated that the probability of frontline miners suffering from WMSDs is 79.7%. Vibration, awkward posture, inadequate organizational support, inadequate organizational justice, and bad organizational culture were the most influential stressors to frontline miners’ WMSDs. The proposed FBN model can be a powerful decision-support tool to improve frontline miners’ musculoskeletal health by managing a more targeted subset of ergonomic and psychosocial stressors.

Bayesian network modelling for navigation status control of cargo ships in the Three Gorges Waterway

This paper proposes a Bayesian network-based navigation status control model to estimate the probability of cargo ships being allowed to navigate in the waterway between the Three Gorges Dam and the Gezhouba Dam. The objective of the study is to develop a model to decide the navigation status of cargo ships by considering their attribute characteristics and the navigation risk factors related to the channel flow. Therefore, environmental condition, ship condition, and traffic complexity three modules are comprehensively considered in the proposed model. Specifically, the historical navigation data of cargo ships collected from the Three Gorges Navigation Administration, together with the navigation regulations of this waterway, are used to analyze the influencing factors of the navigation status and to construct the graphical structure of the Bayesian network. Moreover, the conditional probabilities of some nodes are determined using the Noisy-OR gate and the IF-THEN method. Finally, the navigation records are used to verify the feasibility of the navigation status control model for cargo ships, and the estimated average navigable probability of cargo ships is 89 %. The results indicate that the proposed model can reasonably estimate the navigation probability of cargo ships and assist maritime supervisors in controlling the navigation of cargo ships in the Three Gorges Waterway.

A Comparison of the Classification Performances of the DINO Model, Artificial Neural Networks and Non-Parametric Cognitive Diagnosis

The purpose of this study was to compare the attribute (ACR) and pattern-level (PCR) classification rates of the Deterministic-Input, Noisy-Or Gate (DINO) model, Artificial Neural Networks (ANNs), and Non-parametric Cognitive Diagnosis (NPCD) from datasets of simulation in various conditions such as the number of attributes, sample size, the number of items and missing data rate. A further purpose was to examine the similarities between the classification rates of the DINO model ANNs, and NPCD on the PISA 2015 collaborative problem-solving (CPS) datasets in various numbers of attributes and sample sizes. For the purpose of the study, simulation datasets were generated on the basis of the complex Q matrix structures and the DINO model. The conditions for the sample size factor for the real datasets were determined by simple random selection among the participants in the PISA 2015 administration. As a result, it was found that there was a similarity between the DINO model and NPCD classification rates in both simulation and real datasets. In addition, regarding the increase in sample size in both simulation and real datasets, no consistency was found in the increase or decrease of the classification rates of ANNs and NPCD and the similarities of these rates.

A probabilistic analysis method based on Noisy-OR gate Bayesian network for hydrogen leakage of proton exchange membrane fuel cell

The proton exchange membrane fuel cell (PEMFC) is one of the crucial power units of the hydrogen vehicle. This work proposes a method based on the Noisy-OR gate Bayesian network to evaluate the hydrogen leakage probability of PEMFC. The generation mechanism of hydrogen leakage in fuel cells was analyzed using the fault tree theory. The conditional probability was determined using the Noisy-OR gate model in the Bayesian network, and the probability of hydrogen leakage in the fuel cell was 0.04. Through sensitivity analysis, the key factors that can cause hydrogen leaks in fuel cell were identified. The method proposed in this study can aid in PEMFC accident prevention and control strategies.

Bayesian-Based Traffic Safety Evaluation Study for Driverless Infiltration

Although driverless technology belongs to the frontier of science and technology, there is no sufficient actual data. From the lack of a comprehensive systematic evaluation method of traffic safety under driverless penetration, considering the impact of the three core systems of driverless perception, decision-making, control, and the complex road factors on the safety of driving, we review the main risk causal factors through the analysis of the accident causal model STAMP and put forward the fusion of the Leaky Noisy-OR Gate and Bayesian network model. The Bayesian network professional analysis tool GeNIe 2.0 was used to simulate, analyze, and evaluate the driverless traffic risk Bayesian network model, which accurately assessed the traffic safety risk under driverless penetration and diagnosed and identified the sensitive risk factors. The results of this study concluded that, in order to effectively deal with the future traffic safety risks of driverless vehicles, vehicle enterprises, research institutions, software and hardware suppliers in the field of driverless driving should strengthen the research and development and manufacturing of key components such as perception, and enhance the depth of research and development of AI decision-making software, which provides a new way of thinking about the management of the safety risk of driverless traffic and a theoretical basis for the development and implementation of risk control measures.

Leakage failure probability assessment of submarine pipelines using a novel pythagorean fuzzy bayesian network methodology

Obtaining precise and adequate failure data can be challenging in the probabilistic risk assessment of process industries, like submarine pipelines. This study proposes a novel approach, the Pythagorean fuzzy Bayesian network methodology, to tackle the challenge of obtaining prior data in the assessment of leakage risk in submarine pipelines. First, the qualitative evaluation of experts is converted by Pythagorean fuzzy sets. Next, the enhanced Pythagorean trapezoidal fuzzy Einstein hybrid geometric operator is integrated with the subject-objective weighting approach to consolidate the expert opinions in order to obtain prior probabilities. Following this, the leaky Noisy-OR gate in the Bayesian network is utilized to access the conditional probabilities, which depict the uncertain logical connection of events. Ultimately, the Bayesian network is utilized for inference and analysis to anticipate the probability of system failure and detect any vulnerabilities. Furthermore, a case study is performed to demonstrate the practicality of the approach. The reliability of the methodology is verified by results comparison and analysis. The suggested approach and evaluation findings can offer valuable guidance for the safety supervision of the submarine pipeline network.

Dynamic risk evaluation of hydrogen station leakage based on fuzzy dynamic Bayesian network

This paper introduces a dynamic risk assessment method for hydrogen leakage at hydrogen stations, employing fuzzy dynamic Bayesian networks. To begin, we utilize the Bow-Tie model for an in-depth analysis and consolidation of the primary risk factors contributing to hydrogen leak explosions. Subsequently, we establish a dynamic Bayesian network model for hydrogen leakage at hydrogen stations, adhering to the mapping rules derived from the Bow-Tie model. In order to reduce the impact of subjectivity, our model derives event prior probabilities through expert scoring and fuzzy set theory. Furthermore, the inclusion of a time factor is followed by the application of the Leaky Noisy-or gate model to enhance and calculate conditional probabilities, leading to the generation of time-series change curves for hydrogen leak probability and the probabilities associated with each accident consequence. The research findings yield valuable insights for risk assessment, accident prevention, and emergency management at hydrogen stations.

Dynamic Probabilistic Risk Assessment of Overpressure Burst of Pipeline with Pressure-Protection Systems in Case of Blockage

Although overpressure burst of pipeline with pressure-protection systems occurs infrequently in case of blockage, it endangers personnel safety and causes serious economic loss when it happens. Therefore, assessing the dynamic probabilistic risk of overpressure burst of pipeline with pressure-protection systems in case of blockage is necessary to prevent accidents. This study proposes a method based on a dynamic Bayesian network (DBN) for assessing the dynamic probabilistic risk of overpressure burst of pipeline with pressure-protection systems in case of blockage. First, a fault tree (FT) model of pipeline overpressure burst is constructed. The constructed FT model is then mapped into the DBN model to solve the uncertainty of the model. Second, the leaky Noisy-OR gate is used to define the uncertain logical relationships of relevant nodes in the DBN model. The effect of common cause failures on a pipeline’s redundant pressure-protection system is analyzed using the multiple error shock model. Finally, a natural gas export pipeline is presented as an example to demonstrate the proposed method. Results show that the method can effectively assess the dynamic probabilistic risk of overpressure burst of pipeline with pressure-protection systems in case of blockage.

Brake-by-wire architecture design and analysis in accordance with functional safety standard

The brake-by-wire (BBW) system is one of the safety-critical components of intelligent vehicle chassis, and ensuring its reliability requires a comprehensive functional safety design process. Although many studies have been conducted on electro-mechanical braking (EMB), there is a lack of relevant content on electro-hydraulic braking (EHB), another scheme of BBW system. And the key components affecting EHB system reliability need to be further explored. To address these issues, a system-architecture for EHB with fail-operational capabilities based on ISO 26262 is proposed. Additionally, Fault tree analysis (FTA) and Bayesian network (BN) are used for assessing its reliability. Fault tree (FT) is established to quantitatively calculate the Automotive Safety Integration Level (ASIL). Then FT is mapped into BN, and the conditional probability table is modified to express the polymorphic and uncertain logical relationship of events. To mitigate the dimensional disaster caused by numerous risk factors in the joint probability distribution, a Noisy-or gate method is applied in the BN to address this problem. Finally, the crucial events of system reliability are analyzed. The results indicate that the proposed EHB architecture can meet the safety and reliability requirements of ASIL D and can provide essential reference for the design of EHB related fail-operational system.

Integration of fuzzy reliability analysis and consequence simulation to conduct risk assessment

Losses of containment within the natural gas network, located in most populated areas, could cause environmental damage, injuries, or even death. Accordingly, it is pivotal to adopt proper approaches to assess and mitigate the risk arising from potential losses. Within this context, it is required to exploit solid reliability and consequence analysis techniques. To this end, this paper presents a methodology established on the integration of a Fuzzy Bayesian Network and consequence simulation. The Bayesian Network is more flexible and realistic than classic approaches because it can consider conditional probabilities and prior information. Furthermore, Leaky Noisy-OR Gates are exploited to allow an easier filling of the Conditional Probability Tables. This task is performed through expert elicitation, adopting Intuitionistic Fuzzy Set Theory and Similarity Aggregation Method. Finally, the severity analysis is performed via a software, named Safeti, which provides an accurate evaluation of the consequences. To show the applicability of the framework, a pressure regulator of a Natural Gas Regulating and Metering Station is considered as case study. The proposed approach can assist asset managers in evaluating the risk arising from the operations, and, accordingly, it can guide them in making maintenance-related decisions to assure the safety of the operations.

Risk analysis methodology using STPA-based Bayesian network- applied to remote pilotage operation

The maritime industry is currently ongoing into a digital transformation to develop cleaner, safer and smarter transport services. Establishing such services requires identifying and assessing new emerging risks such as software and design flaws. Thus, suitable hazard identification and risk analysis methods must be developed and implemented for these complex services. This study aims to develop a novel risk analysis methodology by integrating Systems Theoretic Process Analysis, Bayesian Network, Noisy-OR gates, Parent divorcing technique and Sub-modelling. The effectiveness of the proposed methodology is demonstrated through a case study of Remote pilotage operation. The results show that the methodology can be applied to complex operations to assess the propagation of risks from a single fault or failure in a system to the hazards, accidents and incidents, and ultimately the losses. Furthermore, it is demonstrated how the remote pilotage risk model can support pilots and pilotage companies in real-time decision-making by estimating the likelihood of losses in case of a single fault or failure.

Is the drill pipe safe during drilling process?—A new method for evaluating drill pipe failure risk based on the Noisy-OR gate and bayesian network

To evaluate and prevent the failure of drill pipes, this paper proposed a new method for evaluating drill pipe failure risk based on the Noisy-OR gate model and Bayesian network (BN). Different from conventional drill pipe failure analysis, this paper considers the environment-material-human factors as a whole rather than separate. Firstly, the fault tree and the BN model were constructed considering “environment-material-human factors”. Secondly, the influence degree evaluation and drill pipe environmental sensitivity test were investigated. Finally, a case study and basic events sensitivity analysis was carried out with a real well-X as an example. Our results indicated that the top 5 sensitive basic events of drill pipe failure were corrosive medium (0.281), overload (0.178), improper processing technology (0.137), mismanagement of drill pipe (0.102), and structural design defect (0.091) respectively. The mitigation measures were put forward and the risk of drill pipe failure was reduced from 65% to 24%. The real case study showed the applicability and feasibility of the proposed model in this paper.

Characteristics of Chemical Accidents and Risk Assessment Method for Petrochemical Enterprises Based on Improved FBN

Refining and chemical integration is the major trend in the development of the world petrochemical industry, showing intensive and large-scale development. The accident risks caused by this integration are complex and diverse, and pose new challenges to petrochemical industry safety. In order to clarify the characteristics of the accident and the risk root contained in the production process of the enterprise, avoid the risk reasonably and improve the overall safety level of the petrochemical industry, in this paper, 159 accident cases of dangerous chemicals in China from 2017–2021 were statistically analyzed. A Bayesian network (BN)-based risk analysis model was proposed to clarify the characteristics and root causes of accident risks in large refining enterprises. The prior probability parameter in the Bayesian network was replaced by the comprehensive weight, which combined subjective and objective weights. A hybrid method of fuzzy set theory and a noisy-OR gate model was employed to eliminate the problem of the conditional probability parameters being difficult to obtain and the evaluation results not being accurate in traditional BN networks. Finally, the feasibility of the methods was verified by a case study of a petrochemical enterprise in Zhoushan. The results indicated that leakage, fire and explosion were the main types of accidents in petrochemical enterprises. The human factor was the main influencing factors of the top six most critical risk root causes in the enterprise. The coupling risk has a relatively large impact on enterprise security. The research results are in line with reality and can provide a reference for the safety risk management and control of petrochemical enterprises.

Use of fuzzy fault tree analysis and Bayesian network for occurrence likelihood estimation of navigational accidents in the Qinzhou Port

This paper utilizes a fuzzy fault tree analysis and Noisy-OR gate Bayesian network to estimate the occurrence likelihood of navigational accidents. The kernel of this proposed method is first to construct the fault tree from investigation reports of navigational accidents, to calculate the occurrence probability of basic events using fuzzy set, to transform the fault tree to Bayesian network using the Noisy-OR gate. The merit of the developed model can overcome the problem of absolute description of the relationships between basic events and intermediate events. Finally, the model is applied to Qinzhou Port, the results are reasonable by comparing the results in other waterways. Moreover, the key influencing factors are identified from minimum cut set analysis and sensitive influencing factors are quantified sensitivity analysis. Consequently, the findings are beneficial for the maritime authorities to take countermeasures for navigational accidents prevention.

Seepage behavior assessment of earth-rock dams based on Bayesian network

Seepage behavior assessment is an important part of the safety operation assessment of earth-rock dams, because of insufficient intelligent analysis of monitoring information, abnormal phenomena or measured values are often ignored or improperly processed. To improve the intelligent performance of the monitoring system, this article has established an assessment framework covering project quality, maintenance status, monitoring data analysis, and on-site inspection based on the relevant norms of seepage safety assessment of earth-rock dams and the expert survey scoring method, and the Leaky Noisy-OR Gate extended model were used to determine the probability of events, and the dynamic and static Bayesian networks used to assess the possibility of seepage failure of earth-rock dams and diagnose the most likely cause of failure. The function of static and dynamic Bayesian networks to assess the seepage behavior of earth-rock dams, abnormal measured values, and causes of anomalies can make up for the limitations of reservoir management personnel and monitoring system in seepage failure experience and seepage knowledge of earth-rock dams and enable better handling of abnormal phenomena and monitoring information, making the monitoring system more intelligent.

Probability Analysis of Construction Risk based on Noisy-or Gate Bayesian Networks

During construction risks’ probability assessment, it is challenging to obtain the joint probability distribution (JPD) of target risk systems, because every risk element's probability needs to be determined, known as the curse of dimensionality. This paper introduces a Noisy-or Gate Bayesian Network (NG-BN) model that integrates the Noisy-or Gate (NG) model and the Naive Bayesian Network (NBN) to address the problem. The NBN and the NG model's conditional independence assumptions’ gap is bridged by the Markov property. The proposed model requires only connection probabilities with high availability and reliability as the prior knowledge, thus substantially reduces the dimensionality of risk factors while retaining the ability of JPD reasoning. The model is illustrated and tested by a data analysis of the Zijingang Station construction project of Hangzhou Metro Line 5. The result demonstrates that the NG-BN can effectively accomplish the practical occurrence probability evaluation of construction risks. This study has a theoretical contribution as this model establishes a qualitative examination criterion of the Markov property. The proposed NG-BN performs better than the NBN on dimensionality reduction without diminishing the effectiveness of practical risk probability assessment. Its potential for reliability analysis in other engineering fields awaits further study.

Управление рисками портфеля сетевой взаимосвязи проектов (на примере агрохолдинга)

The article is devoted to the problem of portfolio risk management of network project interdependencies in an agricultural company, as the lack of existing methods of project portfolio risk assessment and proper consideration of interdependencies are traced. During the study, we conducted an in-depth interview with the CFO of the company to identify the pool of ongoing projects of the company, determine the types of interproject dependencies, the likelihood of the corresponding risk in the project and the level of losses in monetary terms. Using the method of expert assessments and the noisy-OR gate tool, calculations were made in the GeNIe software product to determine the overall risk of the project portfolio after identifying high-risk projects and then recommendations were given for the application of a dynamic portfolio risk management model based on the software product used in the company.

A Noisy-OR gate based fuzzy fault tree approach for micro-leakage evaluation of bolt-gasket-flange connection (BGFC)

In the natural gas gathering and transportation station, bolt-gasket-flange connection (BGFC) is the main connection mode, but due to its large number, the micro-leakage may result in serious consequences. For this reason, early BGFC micro-leakage evaluation can effectively assist decision-making and prevent the occurrence of accidents. To solve the problem of insufficient field data collection, we proposed a Noisy-OR gate based fuzzy fault tree approach, and calculated the corresponding minimum cut sets and the highest risk factors. Moreover, sensitivity analysis (SA) was used to determine that material problems (wear and corrosion of the sealing surface), man-made operation problems (bolts and flanges) and working conditions (pipe vibration and temperature fluctuations) are the key factors that contributing to the BGFC micro-leakage. In brief, the combination of the fuzzy fault tree analysis and Noisy-OR gate is an effective approach for BGFC micro-leakage evaluation, which is of great significance to the micro-leakage analysis, safe operation, and risk assessment of the gas station.