Iterative learning belief rule-base inference methodology using evidential reasoning for delayed coking unit

Online updating belief rule based system for pipeline leak detection under expert intervention

Remaining useful life (RUL) estimation of aero-engine is a significant part of the aircraft reliability improvement and operational cost reduction. In order to solve the waste of performance data, in this study, a belief rule-based inference methodology using the evidential reasoning (RIMER) based RUL estimation process is proposed. Firstly, we take the principal component analysis (PCA) and the differential evolution (DE) algorithm to optimize the structure and parameter of the belief rule base (BRB), and then estimate the RUL of aero-engine through evidential reasoning (ER) algorithm. The practical case is investigated to validate the feasibility and efficiency of the proposed method. The experimental results show the RIMER based RUL estimation of aero-engine can support decision-making in the fields of aircraft prognostic and health management.

Prediction systems are an important aspect of intelligent decisions. In engineering practice, the complex system structure and the external environment cause many uncertain factors in the model, which influence the modeling accuracy of the model. The belief rule base (BRB) can implement nonlinear modeling and express a variety of uncertain information, including fuzziness, ignorance, randomness, etc. However, the BRB system also has two main problems: Firstly, modeling methods based on expert knowledge make it difficult to guarantee the model’s accuracy. Secondly, interpretability is not considered in the optimization process of current research, resulting in the destruction of the interpretability of BRB. To balance the accuracy and interpretability of the model, a self-growth belief rule base with interpretability constraints (SBRB-I) is proposed. The reasoning process of the SBRB-I model is based on the evidence reasoning (ER) approach. Moreover, the self-growth learning strategy ensures effective cooperation between the data-driven model and the expert system. A case study showed that the accuracy and interpretability of the model could be guaranteed. The SBRB-I model has good application prospects in prediction systems.

Little knowledge exists on the impact and results associated with e‐government projects in many specific‐use domains. Therefore, it is necessary to evaluate the efficiency and effectiveness of e‐government systems. Because the development of e‐government is a continuous process of improvement, it requires continuous evaluation of the overall e‐government system as well as evaluation of its various dimensions such as determinants, characteristics and results. E‐government development is often complex, with multiple stakeholders, large user bases and complex goals. Consequently, even experts have difficulties in evaluating these systems, especially in an integrated and comprehensive way, as well as on an aggregate level. Expert systems are a candidate solution to evaluate such complex e‐government systems. However, it is difficult for expert systems to cope with uncertain evaluation data that are vague, inconsistent, highly subjective or in other ways, challenging to formalize. This paper presents an approach that can handle uncertainty in e‐government evaluation: the combination of Belief Rule Base knowledge representation and Evidential Reasoning. This approach is illustrated with a concrete prototype, known as the Belief Rule Based Expert System (BRBES) and implemented in the local e‐government of Bangladesh. The results have been compared with a recently developed method of evaluating e‐government, and it is demonstrated that the results of the BRBES are more accurate and reliable. The BRBES can be used to identify the factors that need to be improved to achieve the overall aim of an e‐government project. In addition, various ‘what if’ scenarios can be generated, and developers and managers can obtain a foretaste of the outcomes. Thus, the system can be used to facilitate decision‐making processes under uncertainty.

Consumer preference prediction by using a hybrid evidential reasoning and belief rule-based methodology

A new aeronautical relay health state assessment method based on generic belief rule base with attribute reliability

Safety assessment is one of important aspects in health management. In safety assessment for practical systems, three problems exist: lack of observation information, high system complexity and environment interference. Belief rule base with attribute reliability (BRB-r) is an expert system that provides a useful way for dealing with these three problems. In BRB-r, once the input information is unreliable, the reliability of belief rule is influenced, which further influences the accuracy of its output belief degree. On the other hand, when many system characteristics exist, the belief rule combination will explode in BRB-r, and the BRB-r based safety assessment model becomes too complicated to be applied. Thus, in this paper, to balance the complexity and accuracy of the safety assessment model, a new safety assessment model based on BRB-r with considering belief rule reliability is developed for the first time. In the developed model, a new calculation method of the belief rule reliability is proposed with considering both attribute reliability and global ignorance. Moreover, to reduce the influence of uncertainty of expert knowledge, an optimization model for the developed safety assessment model is constructed. A case study of safety assessment of liquefied natural gas (LNG) storage tank is conducted to illustrate the effectiveness of the new developed model.

This paper establishes a novel reliability assessment method for industrial control system (ICS). Firstly, the qualitative and quantitative information were integrated by evidential reasoning(ER) rule. Then, an ICS reliability assessment model was constructed based on belief rule base (BRB). In this way, both expert experience and historical data were fully utilized in the assessment. The model consists of two parts, a fault assessment model and a security assessment model. In addition, the initial parameters were optimized by covariance matrix adaptation evolution strategy (CMA-ES) algorithm, making the proposed model in line with the actual situation. Finally, the proposed model was compared with two other popular prediction methods through case study. The results show that the proposed method is reliable, efficient and accurate, laying a solid basis for reliability assessment of complex ICSs.

Wireless sensor network (WSN) is inevitably subject to node failures due to their harsh operating environments and extra-long working hours. In order to ensure reliable and correct data collection, WSN node fault diagnosis is necessary. Fault diagnosis of sensor nodes usually requires the extraction of data features from the original collected data. However, the data features of different types of faults sometimes have similarities, making it difficult to distinguish and represent the types of faults in the diagnosis results, these indistinguishable types of faults are called ambiguous information. Therefore, a belief rule base with power set (PBRB) fault diagnosis method is proposed. In this method, the power set identification framework is used to represent the fuzzy information, the evidential reasoning (ER) method is used as the reasoning process, and the projection covariance matrix adaptive evolution strategy (P-CMA-ES) is used as the parameter optimization algorithm. The results of the case study show that PBRB method has higher accuracy and better stability compared to other commonly used fault diagnosis methods. According to the research results, PBRB can not only represent the fault types that are difficult to distinguish, but also has the advantage of small sample training. This makes the model obtain high fault diagnosis accuracy and stability.

In order to achieve optimal maintenance and Prediction and Health Management (PHM) of CNC (Computer Numerical Control) machine tool, a belief rule base (BRB) is proposed to predict the fault of the servo system by studying the key techniques of fault prediction. Based on the analysis of fault mechanism in CNC machine tool servo system, a BRB based fault prognosis model of the servo system is established by combing expert knowledge with quantitative information. Moreover, the evidential reasoning (ER) algorithm is used to achieve fault on-line prediction. Experimental results show that the proposed method can accurately reflect the behavior of the system and take full use of uncertain information to improve the accuracy of fault prediction.

Evaluation of user satisfaction using evidential reasoning-based methodology

RIMER and SA based thermal efficiency optimization for fired heaters

SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis is one of the most widely used strategic planning and decision making tool. However, it still has some structural problems such as the lack of prioritization of the alternatives, and too many extractable strategies. In this paper, we suggest a new approach to ranking the strategy alternatives via the SWOT analysis by making use of the Axiomatic Fuzzy Set (AFS) theory to find the best description of the alternatives and use the Evidential Reasoning (ER) approach in the light of AFS to rank the alternatives. The advantages of using the hybrid method (AFS theory and ER approach) to rank the alternatives are that: arbitrary “values in between” of the Expected Utilities are avoided; Decision Makers (DMs) do not need to give subjective judgments to the values of the degree of belief but instead, degrees of belief are directly obtained from the database via AFS theory. A comparison analysis with previous methodologies is made, and it is shown than the proposed hybrid methodology provides a far higher flexibility and effectiveness.

Increases in usage of websites as communication tools has lead to reductions in costs of product and service delivery for companies. Incorrect design of websites leads to redesigning them to fit customer needs. Redesign costs are particularly relevant for resource constrained SMEs. Modeling highly subjective, uncertain and repetitive decisions requires methods that include the benefits of both expert knowledge as well as machine learning. This paper reports a comparison on the application of two such hybrid approaches: Belief Rule-Base Inference Methodology Using the Evidential Reasoning Approach (RIMER) and Artificial Neural Fuzzy Inference System (ANFIS). A numerical example and a case study are reported in order to analyze the potential of the two methods. The results show that both methods can be used to accurately represent and predict the complicated and nonlinear relationships between website characteristics and overall satisfaction. The RIMER approach is found to outperform ANFIS when considering explanatory power.

Wireless sensor networks (WSNs) has been developed rapidly in recent years, but with the number of nodes increasing, it is a difficult task to maintain their normal working condition. Maintainability estimation is the core issue in product design and quality improvement. To improve the maintainability of WSNs, it is important to design a maintainability estimation method. In this paper, we propose a maintainability estimation method for WSNs using belief rule base (BRB) system. The conception and connotation of maintainability are discussed and a process of design for maintainability (DFM) is proposed. Taking account of whole WSNs lifetime, the antecedent attributes influencing maintainability are investigated, which include design factor and external factor. We also establish the BRB base on expert knowledge, which contains three sub-rule-bases. Finally, using the evidential reasoning approach, the nonlinear relationship between maintainability and the antecedent attributes is deduced. By predicting maintainability of a new product using the nonlinear relationship, this maintainability estimation method can provide a theoretical guidance in the design of WSNs.