A Linguistic Z-Number Rule-Based Modeling Framework Considering Knowledge Reliability Based on Evidential Reasoning Rule

In current research of complex system health assessment with evidential reasoning (ER) rule, the relationship between the indicators reference grades and pre-defined assessment result grades is regarded as a one to one correspondence. However, in engineering practice, this strict mapping relationship is difficult to meet, and it may degrease the accuracy of the assessment. Therefore, a new ER rule-based health assessment model for a complex system with a transformation matrix is adopted. First, on the basis of the rule-based transformation technique, expert knowledge is embedded on the transformation matrix to solve the inconsistent problems between the input and the output, which keeps completeness and consistency of information transformation. Second, a complete health assessment model is established via the calculation and optimization of the model parameters. Finally, the effectiveness of the proposed model can be validated in contrast with other methods.

Performance evaluation of aerospace relay based on evidential reasoning rule with distributed referential points

This paper presents a new approach to dynamic system state estimation under bounded noises via the Evidential Reasoning(£K) rule. This method regards the dynamic system equations and the actual observations of the system states as two information sources. The random set description of evidence and the extension principle of random set are used to recursively generate state evidence and observation evidence respectively from the two information sources and to propagate them in system equations. At each time step, the ER rule is used to fuse the two pieces of evidence in observation domain and then the fused result is transformed to state domain by the extension principles. Pignistic expectation of the fused result is calculated as state estimation value. Compared with the estimation method using interval analysis and evidence theory given by Nassreddine, the proposed approach makes estimation results more accurate by using fusion mechanism of the ER rule considering weight and reliability of evidence. The method is shown to have better performances in an application to liquid level estimation of industrial level apparatus than does the Nassreddine's method.

It is extremely important to monitor the health status of welding robots for the safe and stable operation of a body-in-white (BIW) welding production line. In the actual production process, the robot degradation rate is slow and the effective data are poor, which can reflect a degradation state in the large amount of obtained monitoring data, which causes difficulties in health status evaluation. In order to realize the accurate evaluation of the health status of welding robots, this paper proposes a health status evaluation method based on the evidential reasoning (ER) rule, which reflects the health status of welding robots by using the running state data monitored in actual engineering and through the qualitative knowledge of experts, which makes up for the lack of effective data. In the ER rule evaluation model, the covariance matrix adaptive evolutionary strategy (CMA-ES) algorithm is used to optimize the initial parameters of the evaluation model, which improved the accuracy of health status evaluations. Finally, a BIW welding robot was taken as an example for verification. The results show that the proposed model is able to accurately estimate the health status of the welding robot by using the monitored degradation data.

Lithium-ion batteries are widely used in energy storage, small electronic devices and other fields due to their advantages of high energy density and long life cycles, as well as causing less damage to the environment than alternatives. For safety, it is essential to propose reasonable methods to assess batteries’ health statuses. Therefore, a health assessment model based on the evidential reasoning (ER) rule is proposed in this article. Firstly, the voltage rise time and the current fall time are taken as observation indicators, which contain information about the health status of lithium-ion batteries. Secondly, the information of various indicators is integrated into a belief structure, and the indicator reliability and indicator weights are adequately considered in the assessment model. Thirdly, there are some perturbations that will affect the operating status of batteries and cause the batteries’ reliability to fluctuate, so we use perturbation analysis to determine the adaptability of batteries to perturbations. We set two bounded parameters, the perturbation coefficient and the maximum perturbation error, to assess the reliability of lithium-ion batteries when experiencing perturbations. Finally, on the basis of the whole-life open data set of lithium-ion batteries from the National Aeronautics and Space Administration’s Prognostics Center of Excellence, the validity of the health assessment model and perturbation analysis is demonstrated.

A data-driven industrial alarm decision method via evidence reasoning rule

In this paper, we prove from a new angle that Dempster’s rule is inherently probabilistic, extends Bayes’ rule and reduces to Bayes’ rule when precise probabilities are available, regardless of whether prior is uniform. We use examples to demonstrate this equivalence. Additionally, we explain that the Evidential Reasoning (ER) rule is also probabilistic and includes Bayes’ and Dempster’s rules as special cases. Furthermore, we address some criticisms of the behaviour of Dempster’s rule from a probabilistic perspective and explain the rationality of the behaviour. We also identify instances where such critiques were misapplied. Finally, we clarify vital differences between belief degrees in belief functions and basic probabilities and highlight the critical differences between Shafer’s discounting method and the ER rule. These differences make the latter probabilistic, while the former is not. Our motivation is to show that evidence theory has a probabilistic foundation and is possible to become probabilistic again.

Ensemble learning method has shown its superiority in bearing fault diagnosis based on the condition based monitoring. Nevertheless, features extracted from the monitoring signals of bearing systems often contain interrelated and redundant components, leading to poor performances of the base classifiers in the ensemble. Moreover, the current ensemble methods rely on voting strategies to aggregate the diagnostic predictions of these base classifiers without considering their reliabilities and weights simultaneously. To address the aforementioned issues, we propose a novel Diagnosis Aggregation Method with Evidential Reasoning rule, i.e., DAMER, for bearing fault diagnosis. In this method, a semi-random subspace approach using a structured sparsity learning model is developed to decrease the negative effect of interrelated and redundant features, and in the meanwhile to generate accurate and diverse base classifiers. Furthermore, an adaptive evidential reasoning rule (ER rule) incorporating with ensemble learning theory is utilized to aggregate the diagnostic predictions of the base classifiers by taking both their weights and reliabilities into account. To validate the proposed DAMER, an empirical study is conducted on Case Western Reserve University bearing vibration dataset, and the experimental results verify the effectiveness of the proposed DAMER as well as its superiority over commonly used ensemble methods. The performances of feature subsets from multiple domains and the aggregation capability of the adaptive ER rule were also investigated. Results illustrate that DAMER can be utilized as an effective method for bearing fault diagnosis.

An ensemble method with DenseNet and evidential reasoning rule for machinery fault diagnosis under imbalanced condition

An evidential reasoning rule based feature selection for improving trauma outcome prediction

It is of great significance to obtain the performance state of complex equipment to protect equipment and maintain its normal operation. The majority of the performance evaluation methods are based on test data, but resume information is not considered. With its wide applicability and completeness, the resume information can be used in the comprehensive evaluation of equipment in various non-testing situations. By incorporating resume information into the performance evaluation of complex equipment, the flexible use of test data and resume information can result in a more comprehensive and accurate evaluation. Therefore, this paper focuses on the evaluation method of complex equipment performance based on evidential reasoning (ER) considering resume information. In order to unify the test data and resume information in the same framework, a novel method is proposed to transform them into the ER-based performance evaluation. On this basis, according to the index types, different reliability calculation methods are put forward, with one being based on the first-order fitting coefficient of variation, and the other being based on average time to failure; the index weight is analyzed based on the method of expert weight construction. Then, the transformed information with reliability and weight are fused by the ER rule. Finally, a performance evaluation case of a certain inertial measurement unit (IMU) is conducted to verify the effectiveness of the proposed method.

To ensure the safety of the power supply for an in-orbit satellite, it is of great significance to accurately predict the end-of-discharge time of lithium-ion batteries for making a reasonable flight plan. Constrained by development time and experimental environment, it is usually difficult to obtain many full discharge voltage curves of satellite batteries from ground experiments as historical data. Because of insufficient data, the prediction accuracy of the single time series prediction method is low. To solve this problem, this paper takes the voltage of the discharge process as the time series and uses the evidential reasoning rule algorithm to fuse the outputs of three typical prediction models to improve the prediction accuracy. The result can be expressed as a form of belief degree distribution with the ability to express uncertainty. Using the NASA battery dataset, the effectiveness of the proposed method is verified, and the end-of-discharge of an in-orbit satellite battery is predicted by the telemetry data.

Two aspects of problems such as weight over-bounding and reliability-dependence cannot be well solved in the evidential reasoning (ER) approach with both weight and reliability. In order to solve the above problems, the characteristics of weight and reliability are investigated and summarized, i.e., the reliability of evidence is objective and absolute to reflect information quality, while the weight of evidence is subjective and relative to reflect information importance. Then a new discounting method is defined to generate probability masses for the evidence by assigning residual support of weight to empty set and that of reliability to power set. A new ER rule<br> is established for recursively combining the evidence with both reliability and weight by the orthogonal sum operation and a series of theorems and corollaries are introduced and proved. Finally numerical comparison and illustrative example are provided to demonstrate the performances and the applicabilities of the proposed rule and algorithm.

Financial distress prediction: Regularized sparse-based Random Subspace with ER aggregation rule incorporating textual disclosures

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.