Fuzzy Evidence Theory Research Articles

Operational risk modeling based on operational data fusion for multi-state manufacturing systems

Predictive health analysis and diagnostics are essential functional elements of “Smart Manufacturing,” but existing studies are mostly limited to discrete manufacturing equipment without a holistic analysis on the entire system health state. In consideration of the enhancement of risk control in production quality management advocated in ISO 9001:2015, modeling and monitoring the risk during operation are the core aspects and bottlenecks for achieving prognostics. Therefore, a novel holistic operational risk modeling approach for discrete manufacturing systems is proposed by fusing the operational data to quantify the risk-oriented operational performance systematically. First, the connotation of operational risk is expounded and associated with system operational mechanism and mission reliability connotation. Second, a modeling foundation is proposed on the basis of quantifying machine, task execution and work-in-process quality from mission reliability framework in accordance with the operational data characteristics and properties. Third, operational risk evaluation is conducted using the above indicators, where reasonable aggregation of three indicators is performed by fuzzy evidence theory and the risk levels are determined. Finally, a case study is conducted to illustrate the effectiveness and advantages of the proposed approach.

Fuzzy evidence theory and Bayesian networks for process systems risk analysis

Quantitative risk assessment (QRA) approaches systematically evaluate the likelihood, impacts, and risk of adverse events. QRA using fault tree analysis (FTA) is based on the assumptions that failure events have crisp probabilities and they are statistically independent. The crisp probabilities of the events are often absent, which leads to data uncertainty. However, the independence assumption leads to model uncertainty. Experts’ knowledge can be utilized to obtain unknown failure data; however, this process itself is subject to different issues such as imprecision, incompleteness, and lack of consensus. For this reason, to minimize the overall uncertainty in QRA, in addition to addressing the uncertainties in the knowledge, it is equally important to combine the opinions of multiple experts and update prior beliefs based on new evidence. In this article, a novel methodology is proposed for QRA by combining fuzzy set theory and evidence theory with Bayesian networks to describe the uncertainties, aggregate experts’ opinions, and update prior probabilities when new evidences become available. Additionally, sensitivity analysis is performed to identify the most critical events in the FTA. The effectiveness of the proposed approach has been demonstrated via application to a practical system.

Low-Velocity Impact Localization on Composites Under Sensor Damage by Interpolation Reference Database and Fuzzy Evidence Theory

Composites are widely used in aeronautical manufacturing. Despite their excellent properties, composites suffer from barely visible impact damage caused by low-velocity objects. Random impacts need to be detected and located to alert pilots and engineers of the need for maintenance. Generally, fiber Bragg grating (FBG) sensors are installed in aerospace composites, and a reference database is established by recording the reference signals from different impact positions. The random impact is located by comparing its signal to the reference signals in the database. The performance of current localization algorithms mainly relies on the repeatability of FBG signals. However, the FBG sensors or their installation structures may be damaged by repeated impacts during the monitoring process, and the localization accuracy will decrease. In this paper, a new algorithm is proposed based on the interpolation reference database and fuzzy evidence theory to realize accurate impact localization under sensor damage. More correlation coefficients are obtained from the basic reference database by interpolation, and the influence of damaged sensors on localization results is reduced by fuzzy evidence theory. The proposed algorithm was tested on a carbon fiber reinforced polymer plate with four surface-attached FBG sensors. A parametric study was conducted to determine the coefficients of the algorithm. The localization performance was analyzed with both properly functioning sensors and damaged sensors. The results showed that the localization accuracy was better than the existing algorithms, especially in the case of sensor damage.

Real-time Resource Positioning System based on Wireless Sensor Network in Manufacturing Workshop

Discrete manufacturing enterprise has a complex and varied production process, which causes manufacturing resources have dynamic characteristics. Aiming at the efficient collect and management of manufacturing resource information, improve the enterprise’ intellectualization, a real-time resource positioning system based on wireless sensor network was proposed. Firstly, a perceptual model for resource positioning was designed, which can collect and analysis real-time resources information in the workshop. Meanwhile, the architectural structure of real-time resources positioning system was designed based on wireless sensor network and the resources positioning flow was illustrated. Aiming at the low positioning accuracy caused by electromagnetic interference and obstacle in manufacturing workshop environment, a multi-sensor positioning data fusion algorithm based on fuzzy evidence theory was proposed. Finally, a prototype system is implemented to demonstrate the validity of the method in practice.

A new model for threat assessment in data fusion based on fuzzy evidence theory

In this paper a new method for threat assessment is proposed based on Fuzzy Evidence Theory. The most widely classical and intelligent methods used for threat assessment systems will be Evidence or Dempster Shafer and Fuzzy Sets Theories. The disadvantage of both methods is failing to calculate of uncertainty in the data from the sensors and the poor reliability of system. To fix this flaw in the system of dynamic targets threat assessment is proposed fuzzy evidence theory as a combination of both Dempster- Shafer and Fuzzy Sets Theories. In this model, the uncertainty in input data from the sensors and the whole system is measured using the best measure of the uncertainty. Also, a comprehensive comparison is done between the uncertainty of fuzzy model and fuzzy- evidence model (proposed method). This method applied to a real time scenario for air threat assessment. The simulation results show that this method is reasonable, effective, accuracy and reliability.

A distance measure between intuitionistic fuzzy belief functions

Intuitionistic fuzzy (IF) evidence theory, as an extension of Dempster–Shafer theory of evidence to the intuitionistic fuzzy environment, is exploited to process imprecise and vague information. Since its inception, much interest has been concentrated on IF evidence theory. Many works on the belief functions in IF information systems have appeared. However, there is little research on the distance measure between IF belief functions despite the fact that distance measure in classical belief functions has received close attention. In this paper we mainly investigated the distance measure between IF belief functions based on the Euclidean distance between two column vectors. The similarity between focal elements is also taken into account. The distance and similarity measures between IF sets are investigated firstly. A new similarity measure between IF sets along with its properties and proofs is proposed. The positive definiteness of similarity matrix is investigated to guarantee the metric properties of the distance measure. Then a distance measure between IF belief functions is proposed. It is proved that the proposed distance measure is a metric distance. As is illustrated by examples, the distance measure is sensitive to the change of focal elements. Moreover, its applicability for classical belief functions is also demonstrated.

A practical model for ensemble estimation of QoS and QoE in VoIP services via fuzzy inference systems and fuzzy evidence theory

Nowadays, there is an increasing number of Voices over IP (VoIP) services offered in telecommunication networks with specific quality requirements. Such requirements impact upon both the objective quality of service (QoS) of the end-to end connection as well as the subjective quality of experience (QoE) as perceived by the end user. In this study, we have proposed an integrated QoS and QoE evaluation system based on the combination of fuzzy inference systems and fuzzy evidence theory. For quality criteria, we have used QoS as a technical source from the viewpoint of service provider and QoE as the end user approach in front of service. We have divided the positive and negative variables of QoE into two distinct systems. At the same time, a parameter has been proposed as quality, which is the total quality of a service according to the objective and subjective viewpoints. To calculate the parameters mentioned above, a fuzzy inference system has been utilized. In addition, to obtain all data sources as an evidence of quality, Dempster---Shafer evidence theory has been employed. Finally, the proposed approach evaluated through the quality of VoIP services in three real cases and the results are discussed.

Probability Estimation in the Framework of Intuitionistic Fuzzy Evidence Theory

Intuitionistic fuzzy (IF) evidence theory, as an extension of Dempster-Shafer theory of evidence to the intuitionistic fuzzy environment, is exploited to process imprecise and vague information. Since its inception, much interest has been concentrated on IF evidence theory. Many works on the belief functions in IF information systems have appeared. Although belief functions on the IF sets can deal with uncertainty and vagueness well, it is not convenient for decision making. This paper addresses the issue of probability estimation in the framework of IF evidence theory with the hope of making rational decision. Background knowledge about evidence theory, fuzzy set, and IF set is firstly reviewed, followed by introduction of IF evidence theory. Axiomatic properties of probability distribution are then proposed to assist our interpretation. Finally, probability estimations based on fuzzy and IF belief functions together with their proofs are presented. It is verified that the probability estimation method based on IF belief functions is also potentially applicable to classical evidence theory and fuzzy evidence theory. Moreover, IF belief functions can be combined in a convenient way once they are transformed to interval-valued possibilities.

Complex Equipment FMECA Method and Application Based on the Product Family

The Failure Mode, Effects and Criticality Analysis of the complicated equipment is difficult to implement. Therefore, this paper puts forward a product family FMECA method based on the analysis process and analysis. At the same time, the application of the fuzzy set and evidence theory are applied in the product family partition algorithm, which considering the complex equipment development early RMS attribute value fuzzy, uncertainty and the non-quantitative and other characters. Besides the method of effective application support complicated equipment based on the product platform of fault information database building methods is given in this paper.

Reliability Assessment Using Fuzzy Evidence Theory

Aiming at many uncertain factors in the reliability assessment of aircraft, a new approach to reliability assessment combining D-S theory of evidence and fuzzy theory was discussed. The concepts of inclusion degree and intersection degree of fuzzy sets were redefined, and fuzzy evidence theory was extended to the circumstance when the framework of discrimination is a continuous and in finite set. Finally, the practical assessment process was given in an illustrative example of aircraft system. The results show that the method is effective and reliable.

Application of the Data Fusion Method Based on Fuzzy Theory and Evidence Theory in Vehicle Recognition

A data fusion method which based on fuzzy theory and evidence theory was discussed in this paper， and it is applied to the vehicle type recognition simulation research using some multi-sensor characteristics such as the appearance and the length of the vehicle. The experimental results show that this method can avoid the limitation of single sensor, reduce the sensors uncertainty, and improve the identification accuracy.

Target Identification Method for Single-Ship Air-Defense Based on Kinematics Characteristics

In this paper, a target identification method is proposed based on kinematics characteristics for single-ship air-defense. After seriously analyzing the characteristics of modern air strike targets, extracting methods of the features of height, velocity, acceleration, detection distance, continuous radial motion etc. is given. Then, three-layer identification structure is established: layer 1 can identify tactical ballistic missile (TBM) and armed helicopter (AH) according to height, velocity; layer 2 can identify anti-radiation missile (ARM) and precision guided bomb (PGB) according to the features of horizontal continuous decelerated motion and continuous radial motion; layer 3 combines Fuzzy set and DS evidence theory to identify anti-ship missile (ASM) and bomb-attacker (BA) according to the features of height and detection distance. Verified by environment of typical targets, this method proposed in this paper has fine effect of identification.

Attribute reduction based on generalized fuzzy evidence theory in fuzzy decision systems

Attribute reduction is viewed as an important issue in data mining and knowledge representation. This paper studies attribute reduction in fuzzy decision systems based on generalized fuzzy evidence theory. The definitions of several kinds of attribute reducts are introduced. The relationships among these reducts are then investigated. In a fuzzy decision system, it is proved that the concepts of fuzzy positive region reduct, lower approximation reduct and generalized fuzzy belief reduct are all equivalent, the concepts of fuzzy upper approximation reduct and generalized fuzzy plausibility reduct are equivalent, and a generalized fuzzy plausibility consistent set must be a generalized fuzzy belief consistent set. In a consistent fuzzy decision system, an attribute set is a generalized fuzzy belief reduct if and only if it is a generalized fuzzy plausibility reduct. But in an inconsistent fuzzy decision system, a generalized fuzzy belief reduct is not a generalized fuzzy plausibility reduct in general.

基于模糊证据理论的多特征目标融合检测算法

基于模糊证据理论的多特征目标融合检测算法

Applications of Possibility and Evidence Theory in Civil Engineering

This article is devoted to applications of fuzzy set theory, possibility theory and evidence theory in civil engineering, presenting current work of a group or researchers at the University of Innsbruck. We argue that these methods are well suited for analyzing and processing the parameter uncertainties arising in soil mechanics and construction management. We address two specific applications here: finite element computations in foundation engineering and a queueing model in earth work.

FACTOR SELECTIONS FOR A SYSTEM DEVELOPMENT PROCESS

In this study, methods of investigating influential factors is proposed for a system development process. According to different phases of a system development process, multiobjective programming (MOP) models are developed from fuzzy set theory, evidence theory, and probability theory. Based on the proposed MOP models, an integrated solution algorithm is designed to aid researchers in acquiring complete influential factors of a system. An example of mobile phone service (MPS) is provided to illustrate the proposed algorithm.

Treatment of Uncertainty in Study of Transportation: Fuzzy Set Theory and Evidence Theory

This paper examines the nature of uncertainty present in transport planning and explores appropriate mathematical treatment. Two types of uncertainty are dealt with: vagueness and ambiguity. The former refers to the uncertainty caused by the lack of definition of words, and the latter refers to the uncertainty caused by the lack of information about the subject matter. The mathematical framework that can deal with vagueness is fuzzy set theory, and that for ambiguity is evidence theory. Differences in the nature of problems that these two types of uncertainty present are examined. How to apply the appropriate mathematical technique to model these types of uncertainty is discussed, along with the basic properties of fuzzy set theory and evidence theory. Domains of transport problems that are conducive to these theories are explained, and the issues that need to be examined when using these theories are discussed.