Basic Probability Assignment Function Research Articles

A Three-Zone Identification Method for Coal Mine Area Based on DS Evidence Theory

As coal ore and other resources are continuously mined, a three-zone structure is formed underground consisting of a sagging zone, fault zone, and caving zone. The use of well-logging data to identify the three zones is important for production safety and environmental management. Owing to the scarcity of data that can reflect three zones in normal coal mining, conventional identification and prediction methods face challenges when extracting data features, incurring a degree of uncertainty within prediction results. Accordingly, the accurate identification of the three zones has become a critical objective in daily production. To address this issue, we developed a method called a method called backpropagation neural networks with Dempster–Shafer (DS) evidence theory. Initially, we preprocessed the training data and deployed two backpropagation neural networks (BPNNs) to predict the three zones according to two parameters. According to these prediction results, the local and global credibility of each prediction is calculated and used to obtain the basic probability assignment function required for the DS evidence theory. Finally, the DS evidence theory is used to fuse the two BPNNs prediction results, thereby producing the final prediction results. The proposed method was demonstrated to improve prediction accuracy by 6.4% compared to a conventional neural network.

Belief Inaccuracy Information Measures and Their Extensions

This work introduces belief inaccuracy information (BII) and Jensen–belief inaccuracy information (JBII), grounded in the foundational concept of basic probability assignment. The significance of these novel methodologies lies in their potential to advance the field by providing valuable insights into belief accuracy assessment. The research not only establishes these measures but also highlights their relevance in addressing critical challenges within the realm of probability assignment. Further, some results associated with these proposed information measures were established. In addition, the BII and JBII measures were examined for escort and generalized escort of basic probability assignment functions, where an escort basic probability assignment with order [Formula: see text] is constructed based on the power [Formula: see text] of a baseline basic probability assignment with a normalizing constant. Finally, this paper presents compelling results derived from the application of BII and JBII measures. A detailed simulation of Conway’s game of life cellular automaton is described, showcasing the utility of these measures in analyzing belief inaccuracies within complex dynamic systems. Furthermore, an application to ozone time series is explored, employing belief Kullback–Leibler and belief chi-square divergences. This application specifically focuses on the study of station discrepancies, revealing the versatility of the proposed measures in addressing diverse scenarios.

Large-scale group hierarchical DEMATEL method with automatic consensus reaching

Decision-making trial and evaluation laboratory (DEMATEL) is widely used because of its ability to effectively analyze nonlinear relationships between factors in complex systems. With the increasing complexity of decision-making problems, large-scale group decision-making (LSGDM) has become the norm. Most existing DEMATEL methods are only suitable for small-scale groups and simple systems. This study, therefore, proposes a large-scale group hierarchical DEMATEL method that considers consensus reaching. The DEMATEL method for LSGDM faces three challenges: large differences in knowledge structures, difficulty coordinating expert opinions, and slow group-consensus convergence. To address these challenges, first, we use hierarchical decomposition to decompose the complex system into simple systems with different levels to reduce the difficulty of decision-making in complex systems. Second, considering the limitations of expert knowledge and experience, we use the basic probability assignment function to extract the opinions of experts at different levels of subsystems and factors. Third, we divide experts into different clusters using K-means clustering to solve the problem of difficult expert-opinion coordination. Fourth, we design two types of consensuses (intrasubgroup and intersubgroup consensus) and an efficient new type of opinion autocorrection mechanism to solve the problem of the slow convergence of intragroup consensus and improve the efficiency of consensus reaching. Finally, we demonstrate the superiority of the proposed method through data analysis and method comparison.

Phase prediction of high-entropy alloys based on machine learning and an improved information fusion approach

The phase design of high entropy alloys (HEAs) is an important issue since the phase structure affects the comprehensive properties of HEAs. Accurate prediction of phase classification can accelerate material design. In this paper, a new phase prediction framework is proposed using machine learning (ML) and an improved information fusion approach based on the Dempster-Shafer (DS) evidence theory. Considering that the classification results of different ML algorithms may conflict, and the traditional DS evidence theory cannot solve the problem of high conflict, we propose an improved information fusion approach based on the DS evidence theory. The basic probability assignment function is constructed using the ML algorithms. 761 HEAs samples are collected consisting of amorphous phase (AM), solid solution (SS), intermetallic compound (IM), and a mixture of SS and IM (SS + IM). For the small dataset of HEAs, we use a conditional generative adversarial network (CGAN) for data augmentation. Based on the enhanced dataset, the ML model is optimized by sparrow search algorithm (SSA), which can accelerate searching speed of model hyperparameters and improve the performance of the model. The results show that the proposed information fusion method performs better than several other existing techniques on the test set, and the prediction accuracy reaches 94.78 %. Meanwhile, the prediction accuracy of the proposed method is higher than that of the existing technology (93.17 %). It is proved that the proposed method can solve the high conflict problem effectively. Moreover, we present the interpretability analysis of the features by the Shapley additive explanations (SHAP) and the sensitivity matrix. A smaller atomic size difference δ (<6.6 %) is conducive to the formation of SS phase, while a larger δ (>6.6 %) is conducive to the formation of AM phase. A smaller enthalpy of mixing ΔHmix tends to form AM phase. In binary and ternary alloy systems, IM phase can be extracted by the mixing enthalpy ΔSmix < 10. In addition, we find that mean bulk modulus (K) and standard deviation of melting temperature (σT) are critical features to distinguish between SS and SS + IM.

Underwater acoustic target recognition method based on WA-DS decision fusion

Deep learning methods can recognize the various underwater acoustic targets in complex marine environments. In this study, we apply deep learning methods in decision recognition of underwater acoustic targets. However, decision recognition methods rely on a complex evidence theory that combines the probabilities of various scenarios as evidence sources, and in the process of combination produces conflicting evidence. For this reason, we propose an underwater acoustic target recognition method based on weighted average Dempster -Shafer (WA-DS) decision fusion, which replaces anomalous evidence with weighted average evidence, and solves the problem of synthesizing anomalous evidence. Firstly, the underwater acoustic dataset is preprocessed with downsampling, denoising, and audio segmentation. Then, the Mel-frequency cepstral coefficients (MFCC), Gammatone frequency cepstral coefficients (GFCC), and time–frequency (TF) features are extracted, and the category probabilities are output using the ResNet18 network to learn and train the multi-feature and then output the category probabilities. Finally, the category probabilities of multi-feature are computed as evidence using the Basic Probability Assignment (BPA) function, and the various types of evidence are fused using our decision fusion algorithm. Experimental results show that our decision fusion method is more capable of synthesizing anomalous evidence than traditional decision fusion methods. Our work achieves recognition accuracy of up to 98.34% on the ShipsEar dataset, which is a significant improvement in recognition rate compared to single features.

Multi-fuzzy clustering validity index ensemble: A Dempster-Shafer theory-based parallel and series fusion

Clustering validity evaluation is a key part in clustering process. To adapt the complex data structure, the traditional fuzzy clustering validity index (FCVI) is designed more complex. The weighted combined validity evaluation method (WCVEM) is simple in structure but difficult in weight selection. Therefore, this paper proposed an ensemble method based on multi-fuzzy clustering algorithms and multi-FCVI. Firstly, multi-FCVI are calculated by using the multiple sets of cluster centers and membership degrees that obtained by multi-fuzzy clustering algorithms. This can improve the robustness of the multi-FCVI. Secondly, multi-FCVI are ensembled by Dempster-Shafer (DS) theory. The validity index basic probability assignment function can be obtained by calculating the credibility of each validity index with different clusters number. Finally, the decision module is used to output the optimal clusters number. This paper ensembles multi-fuzzy clustering algorithms, multi-FCVI, and the DS theory by using series and parallel structure to verify performance of the proposed model and the degree of information retention of the FCVI. The proposed method is simple in structure and does not need to be select weighted. 6 artificial datasets and 12 UCI datasets were selected to simulate and verify the method. When facing different data, the simulation results show that the parallel structure has the highest accuracy, and the series structure is even worse than the weighted method in some datasets. In addition, the paper changes the value of fuzzy weighted, and experimental results show that the ensemble method has better stability than other methods in the face of different fuzzy weighted strategy.

Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method

Failure mode and effects analysis (FMEA) is an important risk analysis tool that has been widely used in diverse areas to manage risk factors. However, how to manage the uncertainty in FMEA assessments is still an open issue. In this paper, a novel FMEA model based on the improved pignistic probability transformation function in Dempster–Shafer evidence theory (DST) and grey relational projection method (GRPM) is proposed to improve the accuracy and reliability in risk analysis with FMEA. The basic probability assignment (BPA) function in DST is used to model the assessments of experts with respect to each risk factor. Dempster’s rule of combination is adopted for fusion of assessment information from different experts. The improved pignistic probability function is proposed and used to transform the fusion result of BPA into probability function for getting more accurate decision-making result in risk analysis with FMEA. GRPM is adopted to determine the risk priority order of all the failure modes to overcome the shortcoming in traditional risk priority number in FMEA. Applications in aircraft turbine rotor blades and steel production process are presented to show the rationality and generality of the proposed method.

Nonlinear spectrum feature fusion diagnosis method for RV reducer of industrial robots

RV reducers of industrial robots may be possibly subjected to wear and vibration impact and invalidated for keeping the repeatability due to long-time dynamic load in their life cycle. Any failure of RV reducers may decrease the reliability and lead to incredible loss due to the unexpectedly shutdown of the manufacturing system. Failures can cause nonlinear interference when occurring in RV reducers and pose a challenge to RV reducer fault diagnosis. Therefore, it is important to take proper measures to diagnose the failure in RV reducers and reduce the influence of nonlinear interference. Nonlinear spectrum features fusion method is proposed for diagnosing the faults of RV reducers in industrial robots. For the purpose of extracting fault features more effectively, nonlinear output frequency response function is employed to obtain nonlinear frequency spectrum for exactly describing the nonlinear mechanism of faulty phenomenon. Moreover, enhanced evidence theory with similarity measure is proposed to compute the basic probability assignment functions of evidences according to the similarity between different modes for improving the identification accuracy of different faults. RV reducer experiments with different faults are performed to simulate robot operating conditions for obtaining normal and fault data under a variable speed working condition. Among the 180 sets of test data including normal conditions, the diagnostic accuracy of the proposed nonlinear feature fusion method is 92.22% and greatly preferable to that of the ordinary frequency spectrum method. Experimental results demonstrated that the nonlinear spectrum method can effectively extract nonlinear features and the introduction of similarity can effectively improve the diagnostic accuracy in the proposed feature fusion method.

Deng–Fisher information measure and its extensions: Application to Conway’s Game of Life

The purpose of this work is to introduce Deng–Fisher information (DFI), Deng–Fisher information distance (DFID) and Jensen–Deng–Fisher (JDF) information distance measures based on the basic probability assignment concept. We also present results associated with these proposed information measures and examine DFI and DFID measures for escort of basic probability assignment functions. For illustrative purpose, we examined Conway’s Game of Life cellular automaton and present numerical results in terms of the proposed information measures. Results indicate that JDF information distance measures living cell population dynamics along time.

An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multisource product-fusion approach

Abstract. Global land cover (GLC) information with fine spatial resolution is a fundamental data input for studies on biogeochemical cycles of the Earth system and global climate change. Although there are several public GLC products with 30 m resolution, considerable inconsistencies were found among them, especially in fragmented regions and transition zones, which brings great uncertainties to various application tasks. In this paper, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing multiple existing land cover (LC) products based on the Dempster–Shafer theory of evidence (DSET). Firstly, we used more than 160 000 global point-based samples to locally evaluate the reliability of the input products for each land cover class within each 4∘ × 4∘ geographical grid for the establishment of the basic probability assignment (BPA) function. Then, Dempster's rule of combination was used for each 30 m pixel to derive the combined probability mass of each possible land cover class from all the candidate maps. Finally, each pixel was determined with a land cover class based on a decision rule. Through this fusing process, each pixel is expected to be assigned the land cover class that contributes to achieving a higher accuracy. We assessed our product separately with 34 711 global point-based samples and 201 global patch-based samples. Results show that the GLC-2015 map achieved the highest mapping performance globally, continentally, and ecoregionally compared with the existing 30 m GLC maps, with an overall accuracy of 79.5 % (83.6 %) and a kappa coefficient of 0.757 (0.566) against the point-based (patch-based) validation samples. Additionally, we found that the GLC-2015 map showed substantial outperformance in the areas of inconsistency, with an accuracy improvement of 19.3 %–28.0 % in areas of moderate inconsistency and 27.5 %–29.7 % in areas of high inconsistency. Hopefully, this improved GLC-2015 product can be applied to reduce uncertainties in the research on global environmental changes, ecosystem service assessments, and hazard damage evaluations. The GLC-2015 map developed in this study is available at https://doi.org/10.6084/m9.figshare.22358143.v2 (Li et al., 2023).

Structural damage identification of subseabed shield tunnels based on distributed fiber optic sensors and information fusion

Subseabed shield tunnels are constantly subjected to erosion induced by seepage and corrosive ions and electrochemical effects of diffuse currents from subway traction locomotives during service, resulting in damage to the outer wall of the segment. However, the outer wall cannot be directly observed, making damage identification a challenge. In this research, an approach based on distributed fiber optic sensors (DFOSs) and information fusion is proposed to solve this issue. To monitor strain, DFOSs were installed along the circumference of the segment inner wall. The strain curvature was adopted to solve the adverse influence of the spatial resolution (SR) averaging effect in DFOSs. Using the strain and strain curvature as information sources, the strain damage probability index (SDPI) and strain curvature damage probability index (SCDPI) were developed. Based on Dempster-Shafer (D-S) evidence theory, the SDPI and SCDPI were further utilized to build basic probability assignment (BPA) functions and perform information fusion for damage identification. To validate the feasibility of this approach, model experiments were conducted. The analysis of the test data indicated that information fusion can suppress the independent features of the two information sources while emphasizing their common features. The SR averaging effect, which can cause data distortion and diminish or even mask the damage anomaly feature in strain, and multiple extrema in the strain curvature that can interfere with damage localization are simultaneously eliminated. The damage identification accuracy is improved, and the damage localization error for the segment outer wall can reach 0.79 cm.

Rainstorm-Induced Emergency Recognition from Citizens’ Communications Based on Spatial Feature Extraction and Transfer Learning

The rapid recognition of rainstorm-induced emergencies (e.g., building and road inundation, facility damage, and trapped citizens) is vital to timely disaster response. One big challenge that limits the performance of emergency recognition is the data imbalance between different emergency domains. The present study aims to develop an effective cross-domain transfer learning framework for rainstorm-induced emergency recognition based on the text reports provided by citizens. The critical component of the framework is the use of joint distribution adaption (JDA) analysis embedded in a discriminative feature mapping procedure, which transfers rich knowledge learned from large-scale datasets to the learning task from small emergence data. Considering the feature incompleteness that is caused by short text length, a basic probability assignment function is constructed and applied to extract important spatial features for rainstorm emergency recognition, with an improved marginal Fisher analysis being adopted to optimize cross-domain text feature representation. The proposed scheme is validated using the empirical data of ten emergency classes from Wuhan City, China. Our experimental results show that the proposed method could significantly address data imbalance and thus help achieve high recognition performance through domain knowledge complementation. Meanwhile, the use of various spatial features is proved to be effective in tackling missing features. This scheme can be further developed into smart systems for rainstorm disaster response with reasonable performance and imbalanced sample sizes.

A Combined Method for Line Selection of Single Phase to Ground Fault in Compensated Distributions Based on Evidence Theory

In order to improve reliability of distributions, the neutrals are usually insulated or compensated with Peterson coil. Thus, the currents of single-phase to ground fault are weak, leading to difficulty in fault line selection problem. This paper investigates an evidence theory based integrated fault line selection method which employs multiple fault components to improve the performances of selection for neutral compensated distributions. Aim of this method is through the fusion strategy of the multi-fold unreliable fault components to arrive at more reliable solution. Three kinds of fault components including the transient, the abrupt changing, and the damped DC are developed. Fault measures are introduced for each line to quantitatively describe the likelihood of the line being the fault line. The fault components are extracted by dual-tree complex wavelet and atom decomposition. And the algorithms of calculating fault measures for the three components are developed according to the principle of the fault. The basic probability assignment (BPA) functions of evidence theory are established with the cloud model algorithm of data driven. Examples validate that through fusion algorithm of the multifold fault components, the line selection is more robust and reliable to varied circumstance than utilizing individual component.

Research on the construction safety risk assessment of prefabricated subway stations in China

PurposePrefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.Design/methodology/approachA construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.FindingsThe results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.Originality/valueThis research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.

Improved Dempster–Shafer Evidence Theory for Tunnel Water Inrush Risk Analysis Based on Fuzzy Identification Factors of Multi-Source Geophysical Data

Water inrush is one of the most important risk factors in tunnel construction because of its abruptness and timeliness. Various geophysical data used in actual construction contain useful information related to groundwater development. However, the existing approaches with such data from multiple sources and sensors are generally independent and cannot integrate this information, leading to inaccurate projections. In addition, existing tunnel advanced geological forecast reports for risk projections interpreted by human operators generally contain no quantitative observations or measurements, but only consist of ambiguous and uncertain qualitative descriptions. To surmount the problems above, this paper proposes a tunnel water inrush risk analysis method by fusing multi-source geophysical observations with fuzzy identification factors. Specifically, the membership function of the fuzzy set is used to solve the difficulty in determining the basic probability assignment function in the improved Dempster–Shafer evidence theory. The prediction model of effluent conditions fuses seismic wave reflection data, ground penetrating radar data, and transient electromagnetic data. Therefore, quantitative evaluations of the effluent conditions are achieved, including the strand water, linear water, seepage and dripping water, and anhydrous. Experimental evaluations with a typical tunnel section were conducted, in which the state of the groundwater from a series of geological sketch reports in this sectionpaper were used as ground truth for verification. The experimental results revealed that the proposed method not only has high accuracy and robustness but also aligns well with different evidence effectively that generally contradicts manual interpretation reports. The results from 12 randomly selected tunnel sections also demonstrate the generalization abilities of the proposed method.

A Possibility-Based Method for Urban Land Cover Classification Using Airborne Lidar Data

Airborne light detection and ranging (LiDAR) has been recognized as a reliable and accurate measurement tool in forest volume estimation, urban scene reconstruction and land cover classification, where LiDAR data provide crucial and efficient features such as intensity, elevation and coordinates. Due to the complex urban environment, it is difficult to classify land cover accurately and quickly from remotely sensed data. Methods based on the Dempster–Shafer evidence theory (DS theory) offer a possible solution to this problem. However, the inconsistency in the correspondence between classification features and land cover attributes constrains the improvement of classification accuracy. Under the original DS evidence theory classification framework, we propose a novel method for constructing a basic probability assignment (BPA) function based on possibility distributions and apply it to airborne LiDAR land cover classification. The proposed approach begins with a feature classification subset selected by single-feature classification results. Secondly, the possibility distribution of the four features was established, and the uncertainty relationship between feature values and land cover attributes was obtained. Then, we selected suitable interval cut-off points and constructed a BPA function. Finally, DS evidence theory was used for land cover classification. LiDAR and its co-registration data acquired by Toposys Falcon II were used in the performance tests of the proposed method. The experimental results revealed that it can significantly improve the classification accuracy compared to the basic DS method.

A new Bayesian network model for risk assessment based on cloud model, interval type-2 fuzzy sets and improved D-S evidence theory

Traditional Bayesian network (BN) model is established by crisp sets and probabilities, and its effectiveness and applicability are restricted. In order to solve this problem, a new BN model for risk assessment based on cloud model (CM), interval type-2 fuzzy sets (IT2FSs) and the improved Dempster-Shafer (D-S) evidence theory was developed in the following route. Firstly, CM and IT2FSs were used to define a new solution for the digital characteristics, and the standard and index cloud membership functions for the IT2FSs-CM were established. Then, the index cloud membership function for the IT2FSs-CM was defined as the basic probability assignment (BPA) function, a new evidence fusion rule algorithm was defined, and the improved D-S evidence theory was proposed. Furthermore, the improved D-S evidence theory was used to transform the BPA function into the IT2FSs based prior probability, and a new BN model for risk assessment was developed. Finally, the BN model was applied to a nuclear power plant (NPP) construction project, its risk was assessed at level I (very low risk), and the results by IT2FSs-CM and Bayesian reasoning were compared with those by common methods. The findings show that the new BN model is effective and applicable in risk assessment.

Development of Network Security Based on the Neural Network PSD Algorithm.

The more frequent occurrence of network security incidents has an impact on network security. Through the research on network security situational awareness, this paper constructs a multilevel network security situation evaluation index system from various aspects and uses the Elman neural network optimized by the genetic algorithm to evaluate network security situation. Aiming at the disadvantage of subjective dependence in the traditional assignment method of basic probability assignment function, Elman neural network is used to obtain the basic probability assignment function to increase its objectivity, and it is optimized with the PSD algorithm. In addition, the neural network is further improved by the genetic algorithm. In the traditional D-S evidence theory, an evidence correction step is added to optimize the situation that the final judgment result is incorrect due to evidence conflict. Finally, the fusion rules of the D-S evidence theory are used to fuse the support degrees of the four first-level situations to different security levels to obtain the final network security situation assessment result. The results show that the prediction accuracy of the GA-Elman neural network model is as high as 80%, which is significantly higher than that of the traditional D-S model, indicating that the model proposed in this paper has improved the accuracy of the assessment and prediction results. In conclusion, this study provides feasible theoretical prediction guidance for the accurate assessment of network security posture, reveals the improvement ideas for network security development, and is of great significance for the maintenance of network environment security.

A Multi-Sensor Data-Fusion Method Based on Cloud Model and Improved Evidence Theory.

The essential factors of information-aware systems are heterogeneous multi-sensory devices. Because of the ambiguity and contradicting nature of multi-sensor data, a data-fusion method based on the cloud model and improved evidence theory is proposed. To complete the conversion from quantitative to qualitative data, the cloud model is employed to construct the basic probability assignment (BPA) function of the evidence corresponding to each data source. To address the issue that traditional evidence theory produces results that do not correspond to the facts when fusing conflicting evidence, the three measures of the Jousselme distance, cosine similarity, and the Jaccard coefficient are combined to measure the similarity of the evidence. The Hellinger distance of the interval is used to calculate the credibility of the evidence. The similarity and credibility are combined to improve the evidence, and the fusion is performed according to Dempster’s rule to finally obtain the results. The numerical example results show that the proposed improved evidence theory method has better convergence and focus, and the confidence in the correct proposition is up to 100%. Applying the proposed multi-sensor data-fusion method to early indoor fire detection, the method improves the accuracy by 0.9–6.4% and reduces the false alarm rate by 0.7–10.2% compared with traditional and other improved evidence theories, proving its validity and feasibility, which provides a certain reference value for multi-sensor information fusion.

Research on risk assessment method of steel catenary riser based on information fusion

The Steel Catenary Riser (SCR) is an essential component connecting the subsea production system and the offshore floating platform and SCR malfunction leads to disastrous consequences. In order to ensure operation safety, the operating company monitors SCR motion response in real time by installing sensors. This paper aims to develop a multi-sensor information fusion algorithm for SCR risk assessment. The algorithm generates basic probability assignment (BPA) function based on known information. Then, the intuitionistic fuzzy multi-attribute decision matrix is constructed by BPA to evaluate the dynamic reliability of the sensor. Finally, the focal element credibility is calculated to reduce the conflict between evidences, and the improved Desert-Smarandache (DSm) theory fusion rule is utilized to evaluate the SCR risk through decision fusion. The feasibility of this method is verified by the case study. The corresponding risk control strategy is also provided.