Fuzzy Bayesian Belief Network Research Articles

EVALUATION OF THE INFLUENCE OF ENVIRONMENTAL FACTORS AND COGNITIVE PARAMETERS ON THE DECISION-MAKING PROCESS IN HUMAN-MACHINE SYSTEMS OF CRITICAL APPLICATION

Context. A feature of human-machine systems of critical application operating in real time is that they include as elements both technical systems and people interacting with these systems. At the same time, the main difficulties are associated not only with the improvement of hardware and software, but also with the insufficient development of methods for reliably predicting the impact of the production environment on the human factor and, as a result, on the relevance of decisions made by decision makers. As a result, the task of developing methods for determining the mutual influence of environmental factors and cognitive parameters of decision makers on the decision-making process becomes very relevant.
 Objective. The aim of the work is to propose methodological foundations for the development and study of fuzzy hierarchical relational cognitive models to determine the influence of environmental factors and cognitive parameters of decision makers on the DMP.
 Method. When building FHRCM methods of “soft computing”, methodologies of cognitive and fuzzy cognitive modeling were used, providing an acceptable formalization uncertainty of mutual influence of factors on the DMP.
 Results. A fuzzy cognitive model based on a fuzzy Bayesian belief network has been developed, which makes it possible to draw a connection between qualitative and quantitative assessments of mutually influencing factors on the DMP. The proposed model makes it possible to probabilistically predict the influence of factors and choose rational ways of their interaction in the DMP.
 Conclusions. The results of the experiments make it possible to recommend using the developed model, which takes into account the mutual influence of factors of various nature, including cognitive ones, in the DMP in order to improve the efficiency of HMSCA management as a whole.

Capturing and quantifying the aggregate effects of multi-source factors affecting miners' health and well-being: Construction of Bayesian belief networks.

Factors originating at the organizational, work, and individual levels are closely interrelated and intricately intertwined, affecting health rates. There was limited research on the interdependence and aggregate effects between multi-source factors and occupational health and well-being (OHW). It is challenging to achieve management goals. Therefore, considering cross-level factors and across the "work environment-stress-exposure-OHW" chain, individual vulnerability was considered. A Fuzzy Bayesian Belief Network (FBBN) driven by both domain knowledge and data was constructed to carve out the logic between multi-source factors and OHW. Workers from four coal mines were surveyed twice in 6months. 714 valid samples were included in the analysis. The interdependencies among multi-source factors were identified by the Interpretive Structure Modeling method and the visual probability estimation was achieved based on FBBN. It revealed that the work and the organizational level were the root factors. Eight factors involved in work stress were mainly mediating, and actual exposure and individual vulnerability were direct factors. Pathway interventions and joint interventions were proposed. The prediction ability and scheme feasibility of FBBN were verified. The approach developed allows robust assessments of aggregate effects and obtains multi-source factor importance. This study provides vital insights and evaluation tools for understanding workplace stress and OHW management.

Fault Diagnosis of DCV and Heating Systems Based on Causal Relation in Fuzzy Bayesian Belief Networks Using Relation Direction Probabilities

The state-of-the-art provides data-driven and knowledge-driven diagnostic methods. Each category has its strengths and shortcomings. The knowledge-driven methods rely mainly on expert knowledge and resemble the diagnostic thinking of domain experts with a high capacity in the reasoning of uncertainties, diagnostics of different fault severities, and understandability. However, these methods involve higher and more time-consuming effort; they require a deep understanding of the causal relationships between faults and symptoms; and there is still a lack of automatic approaches to improving the efficiency. The data-driven methods rely on similarities and patterns, and they are very sensitive to changes of patterns and have more accuracy than the knowledge-driven methods, but they require massive data for training, cannot inform about the reason behind the result, and represent black boxes with low understandability. The research problem is thus the combination of knowledge-driven and data-driven diagnosis in DCV and heating systems, to benefit from both categories. The diagnostic method presented in this paper involves less effort for experts without requiring deep understanding of the causal relationships between faults and symptoms compared to existing knowledge-driven methods, while offering high understandability and high accuracy. The fault diagnosis uses a data-driven classifier in combination with knowledge-driven inference with both fuzzy logic and a Bayesian Belief Network (BBN). In offline mode, for each fault class, a Relation-Direction Probability (RDP) table is computed and stored in a fault library. In online mode, we determine the similarities between the actual RDP and the offline precomputed RDPs. The combination of BBN and fuzzy logic in our introduced method analyzes the dependencies of the signals using Mutual Information (MI) theory. The results show the performance of the combined classifier is comparable to the data-driven method while maintaining the strengths of the knowledge-driven methods.

Risk induced contingency cost modeling for power plant projects

The current practice of expert judgment-based contingency cost allocation by owners lacks a holistic understanding of project risks, their causal relationships, and impact on project costs. This study presents an integrated fuzzy set theory and fuzzy Bayesian belief network model for a rational, realistic determination of contingency costs for infrastructure projects. The application of the model is demonstrated for real-world power plant projects in Bangladesh. The model has promising results for its ability to establish the amount of contingency costs with a maximum error of 20% between the contingency cost predicted with the model and the actual contingency cost. It has the potential to assist both the owner and contractor to set aside a realistic amount of contingency cost in the preliminary phase of a project. The approach is also equally useful for monitoring and controlling project risks, and dynamically updates the contingency cost amount during project execution.

A Framework Proposal for Evaluating the Organizational Reliability and Capability of a Liner Shipping Operator in a Fuzzy Context

Reliability and capability have become significant concerns for Liner Shipping Operators (LSOs) to distinguish themselves from their competitors in the liner shipping industry. Many organizations including LSOs have accepted that having a highly reliable and capable performance at the organizational level is an important element in the drive for better overall performance, and commitment to achieve a better competitive advantage. However, to understand how far LSOs are reliable, internal and external factors (i.e. exogenous influential factors) that determine the Organizational Reliability and Capability (ORC) should be evaluated. As a result, the aim of this study is to evaluate the value of the ORC of an LSO by considering five main criteria, namely operational reliability, financial capability, knowledge management, compliance with regulations and service quality capability. Furthermore, the influence of the business environment on the organizational functions is investigated. This study makes uses of a Fuzzy Bayesian Belief Network (FBBN) for evaluating the value of ORC of an LSO. The results have shown that the financial capability is a strong indicator for the internal ORC of the LSOs. In addition, the sensitivity analysis has resulted the model output is more sensitive respectively to the security and safety compliances than the other 28 input variables. This method of assessment can help LSOs to conduct self-evaluation of the ORC for enhancing their business sustainability in the liner shipping industry. In addition, maritime researchers will benefit from the proposed methodology for evaluating the value of the ORC of an LSO.

Current research trends and application areas of fuzzy and hybrid methods to the risk assessment of construction projects

Fuzzy and hybrid methods have been increasingly used in construction risk management research and this study aims to compile and analyse the basic concepts and methods applied in this field to date. A content analysis is made of a comprehensive literature review of publications during 2005–2017. It is found that the nature of complex projects is such that most risks are interdependent of each other. Therefore, a fuzzy structured method such as the fuzzy analytical network process (FANP) has frequently been used for different complex projects. However, the application of FANP is limited because of the tedious and lengthy calculations required for the pairwise comparisons needed and an inability to incorporate new information into the risk structure. To overcome this constraint, a fuzzy Bayesian belief network (FBBN) has been increasingly used for risk assessment. Further project-specific studies based on FBBN are recommended to justify its broader application. Beyond fuzzy methods, the Credal network – an extended form of Bayesian network- is found to have potential for risk assessment under uncertainty.

A fuzzy Bayesian belief network for safety assessment of oil and gas pipelines

Safety assessment of oil and gas (O&G) pipelines is necessary to prevent unwanted events that may cause catastrophic accidents and heavy financial losses. This study develops a safety assessment model for O&G pipeline failure by incorporating fuzzy logic into Bayesian belief network. Proposed fuzzy Bayesian belief network (FBBN) model explicitly represents dependencies of events, updating probabilities and representation of uncertain knowledge (such as randomness, vagueness and ignorance). The study highlights the utility of FBBN in safety analysis of O&G pipeline because of its flexible structure, allowing it to fit a wide variety of accident scenarios. The sensitivity analysis of the proposed model indicates that construction defect, overload, mechanical damage, bad installation and quality of worker are the most significant causes for the O&G pipeline failures. The research results can help owners of transmission and distribution pipeline companies and professionals to prepare preventive safety measures and allocate proper resources.

Probability propagation in fuzzy Bayesian belief networks with nondeterministic states

The conceptual basis of fuzzy Bayesian belief networks with nondeterministic states is considered. The concept of a fuzzy probability estimate as a fuzzy relation of special type is introduced and its geometrical interpretation is given. Functional transformations of fuzzy probability estimates are defined and a multidimensional linear interpolation procedure is developed. Fundamental aspects of information distribution in fuzzy Bayesian belief networks with nondeterministic states are considered.