Study of the Impact of sanitary decisions over water quality using Bayesian Belief Networks in Upper Pantanal Wetland Basin – Brazil

Applications of Bayesian belief networks in water resource management: A systematic review

During the last decades there has been a rise of awareness regarding the necessity to increase energy systems efficiency and reduce carbon emissions. These goals could be partially achieved through a greater use of gas turbine - solid oxide fuel cell hybrid systems to generate both electric power and heat. However, this kind of systems are known to be delicate, especially due to the fragility of the cell, which could be permanently damaged if its temperature and pressure levels exceed their operative limits. This could be caused by degradation of a component in the system (e.g. the turbomachinery), but also by some sensor fault which leads to a wrong control action. To be considered commercially competitive, these systems must guarantee high reliability and their maintenance costs must be minimized. Thus, it is necessary to integrate these plants with an automated diagnosis system capable to detect degradation levels of the many components (e.g. turbomachinery and fuel cell stack) in order to plan properly the maintenance operations, and also to recognize a sensor fault. This task can be very challenging due to the high complexity of the system and the interactions between its components. Another difficulty is related to the lack of sensors, which is common on commercial power plants, and makes harder the identification of faults in the system. This paper aims to develop and test Bayesian belief network based diagnosis methods, which can be used to predict the most likely degradation levels of turbine, compressor and fuel cell in a hybrid system on the basis of different sensors measurements. The capability of the diagnosis systems to understand if an abnormal measurement is caused by a component degradation or by a sensor fault is also investigated. The data used both to train and to test the networks is generated from a deterministic model and later modified to consider noise or bias in the sensors. The application of Bayesian belief networks to fuel cell - gas turbine hybrid systems is novel, thus the results obtained from this analysis could be a significant starting point to understand their potential. The diagnosis systems developed for this work provide essential information regarding levels of degradation and presence of faults in gas turbine, fuel cell and sensors in a fuel cell – gas turbine hybrid system. The Bayesian belief networks proved to have a good level of accuracy for all the scenarios considered, regarding both steady state and transient operations. This analysis also suggests that in the future a Bayesian belief network could be integrated with the control system to achieve safer and more efficient operations of these plants.

The application of Bayesian belief networks in the analysis of reliability is considered. An algorithm for constructing a mathematical model in Bayesian belief networks is developed. An example of reliability analysis of a radioelectronic system by using Bayesian belief networks is given. It is shown that the developed model of Bayesian networks allows estimating the probability of failure-free operation, identifying possible failures and modeling failure states. Keywords Bayesian networks, reliability, reliability analysis, electronic system, fault tree analysis

Bayesian belief networks (BBN) and fuzzy cognitive maps (FCM) are two major causal knowledge frameworks that are frequently used in various domains for cause and effect analysis. However, most researchers use these as separate approaches to analyse the cause(s) and effect(s) of an event. In practice, both methods have their own strengths and weaknesses in both causal modelling and causal analysis. In this paper, a combination of BBN and FCM is used in order to model and analyse network intrusions. First, the BBN is learnt from network intrusion data; following this, an FCM is generated from the BBN, using a migration method. A data-mining approach is suitable for use in the construction of a BBN for network intrusion since this is a data-rich domain, while an FCM is appropriate for the intuitive representation of complex domains. The proposed method of network intrusion analysis using both BBN and FCM consists of several stages, in order to leverage the capabilities of each approach in building the causal model and performing causal analysis. Both the intuitive representation of the causal model in FCM and the wide variety of reasoning methods supported by BBN are exploited in this research to facilitate network intrusion analysis.

This paper aims to develop and test Bayesian belief network-based diagnosis methods, which can be used to predict the most likely degradation levels of turbine, compressor, and fuel cell (FC) in a hybrid system based on different sensors measurements. The capability of the diagnosis systems to understand if an abnormal measurement is caused by a component degradation or by a sensor fault is also investigated. The data used both to train and to test the networks are generated from a deterministic model and later modified to consider noise or bias in the sensors. The application of Bayesian belief networks (BBNs) to fuel cell—gas turbine hybrid systems is novel, thus the results obtained from this analysis could be a significant starting point to understand their potential. The diagnosis systems developed for this work provide essential information regarding levels of degradation and presence of faults in a gas turbine, fuel cell and sensors in a fuel cell—gas turbine hybrid system. The Bayesian belief networks proved to have a good level of accuracy for all the scenarios considered, regarding both steady-state and transient operations. This analysis also suggests that in the future a Bayesian belief network could be integrated with the control system to achieve safer and more efficient operations of these plants.

Developing best-practice Bayesian Belief Networks in ecological risk assessments for freshwater and estuarine ecosystems: A quantitative review

Bayesian Belief Networks as a tool for evidence-based conservation management

Application of Bayesian Belief Networks to quantify and map areas at risk to soil threats: Using soil compaction as an example

Fires become one of the common challenges faced by smart cities. As one of the most efficient ways in the safety science field, risk assessment could determine the risk in a quantitative or qualitative way and recognize the threat. And Bayesian Belief Networks (BBNs) has gained a reputation for being powerful techniques for modeling complex systems where the variables are highly interlinked and have been widely used for quantitative risk assessment in different fields in recent years. This work is aimed at further exploring the application of Bayesian Belief Networks for smart city fire risk assessment using history statistics and sensor data. The dynamic urban fire risk assessment method, Bayesian Belief Networks (BBNs), is described. Besides, fire risk associated factors are identified, thus a BBN model is constructed. Then a case study is presented to expound the calculation model. Both the results and discussion are given.

Using Bayesian belief networks for reliability management : construction and evaluation: a step by step approach

The problem of the safe interaction between a Nuclear Power Plant (NPP) and a Power Grid (PG), considering the Fukushima nuclear accident, is becoming topical. There are a lot different types of influences between NPPs and PG, which stipulate NPPs' safety levels. To evaluate the influences, two metrics are proposed: linguistic and numerical. The approach to the NPP-PG safety assessment is based on the application of Bayesian Belief Network (BBN), where nodes represent different PG systems and links are stipulated by different types of influences (physical, informational, geographic, etc). It is suggested to evaluate criticality of the PG system considering the change of criticalities of all connected systems. The total criticality of each node in BBN is assessed considering particular criticalities caused by different types of influence. The complex nature of NPP and PG mutual interaction calls for the need for integration of different methods that use input data of different qualimetric nature (deterministic, stochastic, linguistic). Application of one specified group of risk methods might lead to loss and/or disregard of a part of safety-related information. BBN and Fuzzy Logic (FL) represent a basis for development of the hybrid approach to capture all information required for safety assessment of NPP – PG under uncertainties. Integration of FL-based methods and BBNs allows decreasing the amount of input information (measurements) required for safety assessment, when these methods are used independently outside from the proposed integration framework. An illustrative example for the NPP reactor safety assessment is considered in this chapter.

The problem of the safe interaction between a Nuclear Power Plant (NPP) and a Power Grid (PG), considering the Fukushima nuclear accident, is becoming topical. There are a lot different types of influences between NPPs and PG, which stipulate NPPs’ safety levels. To evaluate the influences, two metrics are proposed: linguistic and numerical. The approach to the NPP-PG safety assessment is based on the application of Bayesian Belief Network (BBN), where nodes represent different PG systems and links are stipulated by different types of influences (physical, informational, geographic, etc). It is suggested to evaluate criticality of the PG system considering the change of criticalities of all connected systems. The total criticality of each node in BBN is assessed considering particular criticalities caused by different types of influence. The complex nature of NPP and PG mutual interaction calls for the need for integration of different methods that use input data of different qualimetric nature (deterministic, stochastic, linguistic). Application of one specified group of risk methods might lead to loss and/or disregard of a part of safety-related information. BBN and Fuzzy Logic (FL) represent a basis for development of the hybrid approach to capture all information required for safety assessment of NPP – PG under uncertainties. Integration of FL-based methods and BBNs allows decreasing the amount of input information (measurements) required for safety assessment, when these methods are used independently outside from the proposed integration framework. An illustrative example for the NPP reactor safety assessment is considered in this chapter.

Designers of dependable systems need to present assurance cases that support the claims made about the system’s dependability. Building this assurance case, incorporating different types of evidence and reasoning, can be daunting. In this paper we argue that, thanks to their flexibility and expressive capabilities, Bayesian Belief Networks are particularly suitable for building such assurance cases. Drawing on our experience preparing and presenting an assurance case to certify a software product to IEC 61508 Safety Integrity Level 3, we describe how Bayesian Belief Networks can be used to simplify both the engineer’s work in preparing the case, and the auditor’s or customer’s work in checking this case for coherence and completeness

Bayesian belief networks for IR

This study investigates the application of Bayesian Belief Networks (BBNs) to optimize thermoforming, an energy-intensive manufacturing process that rapidly shapes thermoplastic composite sheets into precise three-dimensional forms. Thermoforming involves complex interactions among process parameters, presenting ongoing challenges in achieving optimal energy efficiency, temperature stability, and consistent product quality. Further, the process can become highly non-linear with multiple sources of noise, due to the nature of the factory and logistical factors, adding further challenges to effectively predicting the process outcomes. To address this challenge, in this case study a Bayesian Belief Network was developed to capture the inherent uncertainties and intricate relationships among critical process variables. This probabilistic model integrated experimental observations, computational modeling results, and expert-derived insights, enabling a robust and adaptive decision-making support for the process designers. Each of the select key process parameters such as convective Heat Transfer Coefficient (HTC), and heating power, were prioritized based on their influence on the process performance indicators such as the end product quality, energy usage, temperature distribution error, settling time, and stability. Results demonstrated that the BBN framework provides an effective interactive decision support tool capable of continuous model refinements through updating of probabilities as new data became available, while achieving enhanced energy efficiency and process control, thereby also reducing the operational cost and material wastage.