Evidential Reasoning Rule Research Articles

Reliability assessment of train control and management system based on evidential reasoning rule and covariance matrix adaptation evolution strategy algorithm

The reliability assessment of train control and management system (TCMS) is essential for the condition monitoring of high-speed train. Different from other general complex systems, the TCMS has the characteristics of multi-system unit, strong coupling and multiple factors. Considering the special system operating environment and high safety requirements of high-speed train. In this paper, for the reliability assessment of TCMS, we propose a new quantitative model based on the evidential reasoning rule and covariance matrix adaptation evolution strategy algorithm, the proposed model offers the following advantages: it has a strong modeling capability for the TCMS reliability, it has an interpretable model assessment process, it can describe the assessment result under probabilistic uncertainty and ignorance uncertainty, and it possesses considerable robustness. To make the model interpretable, an assessment hierarchy is established for the TCMS; to improve model robustness, weights interval is applied to replace the trained weights as the model weights. Several traditional methods are compared with the proposed model to demonstrate its performance, the results show that the proposed model has a better training accuracy. Moreover, a case study is conducted to verify the model’s functional feasibility.

Environmental investment prediction using extended belief rule-based system and evidential reasoning rule

A scientific environmental investment prediction plays a crucial role in controlling environmental pollution and avoiding the blind investment of environmental management. However effective environmental investment prediction usually has to fact three challenges about diversiform indicators, insufficient data, and the reliability of prediction models. In the present study, a new prediction model is proposed using the extended belief rule-based system (EBRBS) and evidential reasoning (ER) rule, called ensemble EBRBS model, with the aim to overcome the above challenges for better environmental investment prediction. The proposed ensemble EBRBS model consists of two components: 1) multiple EBRBSs, which are constructed on the basis of not only using various feature selection methods to select representative indicators but also data increment transformation to enrich the training data; 2) an ER rule-based combination method, which utilizes the ER rule to accommodate the weights and reliabilities of different EBRBSs with the predicted outputs of these EBRBSs to have an integrated environmental investment prediction. A detailed case study is then provided for validating the proposed model via extensive experimental and comparison analysis based on the real-world environmental data about 25 environmental indicators for 31 provinces in China ranged from 2005 to 2018. The results demonstrate that the ensemble EBRBS model can be used as an effective model to accurately predict environmental investments. More importantly, the ensemble EBRBS model not only obtains a high accuracy better than some existing prediction models, but also has an excellent robustness compared with others under the situations of excessive indicators and insufficient data.

Data Reliability Analysis of Wireless Sensor Nodes considering Perturbation

Wireless Sensor Network (WSN) is widely used in precision engineering, which requires strict data accuracy. Therefore, it is of practical value to evaluate the reliability of WSN data. Considering the complexity of the real environment, the sensor is bound to be affected by the disturbance factors. Currently, the research result of WSN data disturbance is not ideal. Because the results of reliability analysis are not necessarily credible under a disturbance environment. Thus, it is necessary to judge the reliability of sensor nodes in the disturbance environment. Therefore, disturbance analysis is introduced. In this paper, the temporal correlation and spatial correlation of measured data of WSN nodes are taken as reliability indicators. Through the disturbance analysis method to simulate the disturbance in the working process of nodes, a data reliability evaluation model of WSN nodes is proposed, which is based on the evidence reasoning (ER) rule in the disturbance environment. Based on the wireless sensor experiment of the Intel Berkeley research laboratory, the rationality of the model is explained in this paper. ER rule considering disturbance provides an effective method to analyze the reliability of WSN data.

Discrete-valued belief structures combination and normalization using evidential reasoning rule

Discrete-valued belief structures (DBSs) (known as discrete belief structures) are universal in real life, differ from precise-valued belief structures, and interval-valued belief structures (IBSs). However, the combination of different discrete belief structures presents a problem that has yet to be solved. Therefore, this study investigated the respective counter-intuitive types of behavior associated with the combination of discrete belief structures within the frameworks of the Dempster-Shafer theory. (DST) evidential reasoning (ER) for the purpose of constructing a more general method for the combination and normalization of discrete evidence. Finally, an experimental application is provided to indicate that the proposed method is suitable for combining and normalizing conflict-free/conflicting discrete evidence, and can effectively solve problems involving group decision-making (GDM) with uncertain preference ordinals, such as in a software selection problem.

A New Evidential Reasoning Rule-Based Safety Assessment Method With Sensor Reliability for Complex Systems.

In current studies of safety assessment for complex systems with the evidential reasoning (ER) rule, the evidence reliability is generally given by experts, which makes the observation data by sensors ignored. However, sensors are inevitably affected by such various uncertainties as perturbations in engineering practice, which can reduce their quality and tracking ability. As such, the observation data may become unreliable, and the modeling accuracy of the ER rule is decreased. In this article, a new ER rule-based safety assessment method with sensor reliability for complex systems is proposed, where sensor reliability and perturbation are considered. The coefficient of the variation-based weighting (CVBW) method is employed to obtain sensor weight. The sensor reliability is calculated by static reliability and dynamic reliability, which are determined by experts and the distance-based method, respectively. The perturbation is quantified as a bounded parameter defined as the perturbation factor, which is used to describe uncertainties and aggregate static reliability and dynamic reliability. The performance analysis of safety assessment is conducted to demonstrate the rationality of perturbation and position poor sensors, followed by a safety assessment algorithm. A case study is carried out to validate the effectiveness of the proposed method.

Intelligent Sea States Identification Based on Maximum Likelihood Evidential Reasoning Rule

It is necessary to switch the control strategies for propulsion system frequently according to the changes of sea states in order to ensure the stability and safety of the navigation. Therefore, identifying the current sea state timely and effectively is of great significance to ensure ship safety. To this end, a reasoning model that is based on maximum likelihood evidential reasoning (MAKER) rule is developed to identify the propeller ventilation type, and the result is used as the basis for the sea states identification. Firstly, a data-driven MAKER model is constructed, which fully considers the interdependence between the input features. Secondly, the genetic algorithm (GA) is used to optimize the parameters of the MAKER model in order to improve the evaluation accuracy. Finally, a simulation is built to obtain experimental data to train the MAKER model, and the validity of the model is verified. The results show that the intelligent sea state identification model that is based on the MAKER rule can identify the propeller ventilation type more accurately, and finally realize intelligent identification of sea states.

Improved control of propeller ventilation using an evidence reasoning rule based Adaboost.M1 approach

Marine propeller ventilation, which can occur in extreme seas, reduces energy efficiency of the ship, and can result in significant safety hazards during navigation. A major maritime challenge is to predict water levels during extreme seas and manage an appropriate control strategy for the propulsion system with fast response times. Here we report application of an evidence reasoning (ER) rule-based Adaboost.M1 approach to rapidly estimate propeller ventilation levels thereby improving propulsion management. The ER rule was used as the weak learner of Adaboost.M1 to extract torque signal from the propulsion motor and current signal from the propulsion inverter. First, K-means clustering was used to select the reference value sets of each weak learner. Second, the extracted signals were transformed into multiple pieces of identification evidence, which were combined through ER to estimate the ventilation level by present weak learner. Parameters of the weak learner were then updated according to the estimation results. Finally, the weak learners that had completed training were combined into a strong learner to estimate final ventilation levels. Experimental results showed that the estimation accuracy of the weak learner provided by the ER rule for Adaboost.M1 approach was more than 50%, which resulted in the accurate estimation by the strong learner. This approach provides an important technical advance to allow more effective switching control strategies for marine electric propulsion systems under different sea conditions.

Fault Diagnosis of Capacitance Aging in DC Link Capacitors of Voltage Source Inverters Using Evidence Reasoning Rule

Capacitance aging of DC link capacitors in voltage source inverters (VSIs) is a common fault which can lead to instability of the DC voltage. In such a failure state, although the VSI can still work, its performance gradually deteriorates, resulting in a shorter service life of the equipment. Here, an online monitoring and fault diagnosis method for capacitance aging based on evidence reasoning (ER) rule is presented. Features from the DC link voltage data with different levels of capacitance aging are extracted, and data features are generated as pieces of diagnostic evidence, which are then combined according to the ER rule. Finally, capacitance aging fault levels were estimated using the combined results. This method has better diagnostic performance compared to a backpropagation (BP) neural network approach and can be used to flexibly define the relative weighting of each evidence parameter depending on the application. This approach can therefore be widely used for fault diagnosis of an array of different devices.

DAMER: a novel diagnosis aggregation method with evidential reasoning rule for bearing fault diagnosis

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.

Evidential Reasoning Rule-Based Decision Support System for Predicting ICU Admission and In-Hospital Death of Trauma

We propose to employ evidential reasoning (ER) rule to construct a clinical decision support system (CDSS) to aid physicians to predict the probability of intensive care unit (ICU) admission and in-hospital death for trauma patients once they arrive at a hospital. A generalized Bayesian rule is used to mine evidence from historical data. Evidence is profiled using a format of belief distribution, where the belief degrees of different trauma outcomes are assigned with derived probabilities linked to the corresponding outcomes. Inputs to the CDSS are clinical data of a patient, and output from the system is predicted belief degree of severe trauma, including ICU admission and in-hospital death. The inner logic of the CDSS is that pieces of evidence that match the clinical data of a patient are identified from the evidence base first, and then the ER rule-based evidence aggregation mechanism is utilized to combine the matched evidences to arrive at a prediction. The reliability, weight, and interdependence of clinical evidence are taken into account. Moreover, an evidence weight training module is constructed. The ER rule-based prediction model has superior performance compared with logistic regression and artificial neural network models. An innovative and pragmatic ER rule-based CDSS for trauma outcome prediction is contributed by this article. In the era of big data, this CDSS helps predict patient outcomes based on historical data and helps physicians in emergency departments make proper trauma management decisions.

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

For the sake of risks management, losses reduction, and costs saving, financial distress prediction (FDP) has attracted extensive attention from various communities including academic researchers, industrial practitioners, and government regulators. In addition to the conventional financial information, the textual disclosures regarding companies have received especial concern nowadays and are demonstrated to be effective for FDP. Ensemble methods have become a prevalent research line in the field of FDP incorporating financial and non-financial features. Feature quality is an important factor determining the accuracy in ensemble, however, traditional ensemble methods integrate these different types of features directly and ignore their grouping structures, hence weakening the feature quality and ultimately deteriorating the prediction accuracy. Moreover, although diversity can be obtained by virtue of the randomness of feature sampling in ensemble, the problem is that such randomness leads to the ambiguities among base classifiers, resulting in that the prediction accuracy of each classifier could not be ensured. Having noted these deficiencies, we propose a novel and robust meta FDP framework, which incorporates the feature regularizing module for identifying discriminatory predictive power of multiple features and the probabilistic fusion module for enhancing the aggregation over base classifiers. To validate our proposed regularized sparse-based Random Subspace with Evidential Reasoning rule (RS2_ER), we conducted extensive experiments on the datasets collected from the China Security Market Accounting Research Database (CSMARD), and the experimental results indicate that the proposed RS2_ER method enables the prediction effectiveness on FDP to be significantly facilitated by dealing with the features grouping property and the ambiguities among base classifiers.

Evidence reasoning rule-based classifier with uncertainty quantification

In Dempster–Shafer evidence theory (DST)-based classifier design, the newly proposed evidence reasoning (ER) rule can be used as a multi-attribute classifier to combine multiple pieces of evidence generated from quantitative samples or qualitative knowledge of many attributes. Different from the classical Dempster's combination (DC) rule and its improved forms, ER rule definitely distinguishes the reliability and importance weight of evidence. The former reflects the ability of a single attribute or the corresponding evidence to give correct classification results whereas the latter clarifies the relative importance of evidence when it is combined with other pieces of evidence. Here how to determine the reliability factor is a key problem because it is the connection between the preceding evidence acquisition and the following evidence combination with the importance weights. Therefore, the main aim of this paper is to present a universal method for obtaining the reliability factor by quantifying the uncertainties of samples and the generated evidence. Experiential results on five popular benchmark databases taken from University of California Irvine (UCI) machine learning database show the improved classifier can give higher classification accuracy than the original ER-based classifier without considering uncertainty quantification and other classical or mainstream classifiers.

Multi-objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy

Accurately predicting treatment outcome is crucial for creating personalized treatment plans and follow-up schedules. Electronic health records (EHRs) contain valuable patient-specific information that can be leveraged to improve outcome prediction. We propose a reliable multi-objective ensemble deep learning (MoEDL) method that uses features extracted from EHRs to predict high risk of treatment failure after radiotherapy in patients with lung cancer. The dataset used in this study contains EHRs of 814 patients who had not achieved disease-free status and 193 patients who were disease-free with at least one year follow-up time after lung cancer radiation therapy. The proposed MoEDL consists of three phases: (1) training with dynamic ensemble deep learning; (2) model selection with adaptive multi-objective optimization; and (3) testing with evidential reasoning (ER) fusion. Specifically, in the training phase, we employ deep perceptron networks as base learners to handle various issues with EHR data. The architecture and key hyper-parameters of each base learner are dynamically adjusted to increase the diversity of learners while reducing the time spent tuning hyper-parameters. Furthermore, we integrate the snapshot ensembles (SE) restarting strategy, multi-objective optimization, and ER fusion to improve the prediction robustness and accuracy of individual networks. The SE restarting strategy can yield multiple candidate models at no additional training cost in the training stage. The multi-objective model simultaneously considers sensitivity, specificity, and AUC as objective functions, overcoming the limitations of single-objective–based model selection. For the testing stage, we utilized an analytic ER rule to fuse the output scores from each optimal model to obtain reliable and robust predictive results. Our experimental results demonstrate that MoEDL can perform better than other conventional methods.

Machine learning-based wear fault diagnosis for marine diesel engine by fusing multiple data-driven models

Wear fault is one of the dominant causes for marine diesel engine damage which significantly influences ship safety. By taking full advantage of the data generated in engine operation, machine learning-based wear fault diagnostic model can help engineers to determine fault modes correctly and take quick action to avoid severe accidents. To identify wear faults more accurately, a multi-model fusion system based on evidential reasoning (ER) rule is proposed in this paper. The outputs of three data-driven models including an artificial neural network (ANN) model, a belief rule-based inference (BRB) model, and an ER rule model are used as pieces of evidence to be fused in decision level. In this paper, the fusion system defines reliability and importance weight of every single model respectively. A novel method is presented to determine the reliability of evidence by considering the accuracy and stability of every single model. The importance weight is optimized by genetic algorithm to improve the performance of the fusion system. The proposed machine learning-based diagnostic system is validated by a set of real samples acquired from marine diesel engines in operation. The test results show that the system is more accurate and robust, and the fault tolerant ability is improved remarkably compared with every single data-driven diagnostic model.

Evidential reasoning rule for interval-valued belief structures combination

Evidential reasoning rule for interval-valued belief structures combination

On the combination and normalization of conflicting interval-valued belief structures

Dempster-Shafer theory (DST) or evidence theory has significant advantages in the fields of information aggregation and decision analysis. In this paper, in order to overcome the counter-intuitive behavior or specificity changes caused by evidence theory, the evidential reasoning (ER) rule which handles the weight and reliability of evidence in an appropriate way, is generalized to deal with the combination of conflicting interval-valued belief structures (IBSs). Specifically, an optimization model of pignistic probability distance is established from the global perspective to provide the relative weights for interval evidence so that the modified interval evidence can be reasonably combined, and then a modified interval evidence combination approach is proposed which is based on ER rule. The method can lead to a rational combination of conflicting interval evidence, which is also a development of Yang’s ER rule. Numerical examples are provided to indicate that the proposed method is not only suitable for combining conflict-free interval evidence, but can also suitably combine conflicting interval evidence. At last, a case study is conducted on the actual pattern recognition problem to illustrate the applicability of the proposed method and the potential in dealing with the combination of conflicting interval evidence.

Reliability Assessment Model for Industrial Control System Based on Belief Rule Base

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.

New evidential reasoning rule with both weight and reliability for evidence combination

New evidential reasoning rule with both weight and reliability for evidence combination

Multiple-attribute group decision making for interval-valued intuitionistic fuzzy sets based on expert reliability and the evidential reasoning rule

This study proposes a novel fuzzy multiple-attribute group decision-making approach based on expert reliability and the evidential reasoning (ER) rule in an interval-valued intuitionistic fuzzy environment. First, to determine the reliabilities of experts, an objective method is developed by combining the similarity between the assessments provided before and after group discussion. Second, the proposed approach extends the ER rule to the case where belief degrees are intervals and employs it to combine experts’ assessments. Hereinto, several optimization models are established to produce the aggregated assessments of the alternatives. Then, the overall priority degree of each alternative can be obtained according to the aggregated assessments and further utilized to yield a ranking of alternatives. Finally, a shopping center site selection problem is analyzed by the proposed approach to demonstrate its validity and applicability.

Fault Prediction Method for Wireless Sensor Network Based on Evidential Reasoning and Belief-Rule-Base

Wireless sensor network (WSN) is a distributed intelligent network, which can independently achieve the information collection task of monitoring targets. However, the WSN is susceptible to faults due to various factors, such as sensor resources, network bandwidth, and work environment. The WSN fault prediction technology can estimate the fault trend of the WSN, which can provide the basis for the formulation and implementation of emergency strategies. In this paper, a new WSN fault prediction method is proposed based on evidential reasoning (ER) and belief rule base (BRB). First, the process of the WSN fault prediction is described, which mainly includes the fault assessment of the current WSN and the fault prediction of future WSN. Second, the WSN fault prediction model is constructed, including the ER-based fault assessment model and BRB-based fault prediction model. The projection covariance matrix adaptation evolutionary strategies (P-CMA-ESs) are used to optimize model parameters. Finally, a case study is constructed to verify the validity of the WSN fault prediction model. The experimental results show that the model can adequately estimate the fault state of the current WSN and then predict the fault status of future WSN.