scholarly journals Engine fault diagnosis based on sensor data fusion considering information quality and evidence theory

2018 ◽  
Vol 10 (11) ◽  
pp. 168781401880918 ◽  
Author(s):  
Hepeng Zhang ◽  
Yong Deng
Keyword(s):  
Fault Diagnosis ◽  
Probability Distribution ◽  
Information Quality ◽  
Evidence Theory ◽  
Sensor Data ◽  
Processing Variable ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
The Status ◽  
Basic Probability

Fault diagnosis is a problem processing variable information obtained from different sources in nature. Evidence theory, efficient to deal with information viewed as evidence, is widely used in fault diagnosis. However, a shortcoming of the existing fault diagnosis methods only gets probability distribution rather than the basic probability assignment. A novel method of generating basic probability assignment that takes information quality into account is proposed. The probability distribution is determined by the preliminary matrix and sampling matrix that are constructed by sensor data. And the quality of probability distribution is taken as the discount factor and the rest of belief is assigned to the universal set. Hence, the basic probability assignment is obtained. Then, basic probability assignment can be combined with Dempster and Shafer evidence theory to determine the status of the engine. An application of engine fault is shown to illustrate the practicability of the proposed method. Then by comparing the result of the method which takes information quality into account (the proposed method) and does not do it, the former is better than the latter. Finally, the reliability analysis shows that the proposed method has strong reliability because performance accuracy is 100% when the error rate is less than 10%.

scholarly journals An Improved Expression for Information Quality of Basic Probability Assignment and Its Application in Fault Diagnosis

2020 ◽  
Author(s):  
Hanwen Li ◽  
Rui Cai
Keyword(s):  
Fault Diagnosis ◽  
Information Quality ◽  
Numerical Examples ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Basic Probability ◽  
Open Issue

<div>Information quality is widely used in many applications. However, how to measure information quality in basic probability assignment accurately is still an open issue. Generalized expression for information quality is an effective method to measure information quality in basic probability assignment. Nevertheless, the counter-intuitive results may be obtained when statements are of intersection. To address this issue, a new expression for information quality of basic probability assignment is proposed in this paper considering the frame of discernment and the influence of intersection among statements which can cause changes of uncertainty. Numerical examples are illustrated to demonstrate the effectiveness of the proposed method. In addition, an application in fault diagnosis is used to show the effectiveness of the proposed method. </div>

scholarly journals An Improved Data Fusion Method Based on Weighted Belief Entropy considering the Negation of Basic Probability Assignment

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Yong Chen ◽  
Yongchuan Tang ◽  
Yan Lei
Keyword(s):  
Data Fusion ◽  
Evidence Theory ◽  
Mass Function ◽  
Sensor Data ◽  
Artificial Data ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Fusion Applications ◽  
Basic Probability ◽  
Belief Entropy

Uncertainty in data fusion applications has received great attention. Due to the effectiveness and flexibility in handling uncertainty, Dempster–Shafer evidence theory is widely used in numerous fields of data fusion. However, Dempster–Shafer evidence theory cannot be used directly for conflicting sensor data fusion since counterintuitive results may be attained. In order to handle this issue, a new method for data fusion based on weighted belief entropy and the negation of basic probability assignment (BPA) is proposed. First, the negation of BPA is applied to represent the information in a novel view. Then, by measuring the uncertainty of the evidence, the weighted belief entropy is adopted to indicate the relative importance of evidence. Finally, the ultimate weight of each body of evidence is applied to adjust the mass function before fusing by the Dempster combination rule. The validity of the proposed method is demonstrated in accordance with an experiment on artificial data and an application on fault diagnosis.

scholarly journals An Improved Expression for Information Quality of Basic Probability Assignment and Its Application in Fault Diagnosis

2020 ◽  
Author(s):  
Hanwen Li
Keyword(s):  
Fault Diagnosis ◽  
Information Quality ◽  
Numerical Examples ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Basic Probability ◽  
Open Issue

<div>Information quality is widely used in many applications. However, how to measure infor </div><div>mation quality in basic probability assignment accurately is still an open issue. Generalized expression for </div><div>information quality is an effective method to measure information quality in basic probability assignment. </div><div>Nevertheless, the counter-intuitive results may be obtained when statements are of intersection. To address </div><div>this issue, a new expression for information quality of basic probability assignment is proposed in this paper </div><div>considering the frame of discernment and the inflfluence of intersection among statements which can cause </div><div>changes of uncertainty. Numerical examples are illustrated to demonstrate the effectiveness of the proposed </div><div>method. In addition, an application in fault diagnosis is used to show the effectiveness of the proposed </div><div>method. </div>

scholarly journals An Improved Expression for Information Quality of Basic Probability Assignment and Its Application in Fault Diagnosis

2020 ◽  
Author(s):  
Hanwen Li ◽  
Rui Cai
Keyword(s):  
Fault Diagnosis ◽  
Information Quality ◽  
Numerical Examples ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Basic Probability ◽  
Open Issue

<div>Information quality is widely used in many applications. However, how to measure information quality in basic probability assignment accurately is still an open issue. Generalized expression for information quality is an effective method to measure information quality in basic probability assignment. Nevertheless, the counter-intuitive results may be obtained when statements are of intersection. To address this issue, a new expression for information quality of basic probability assignment is proposed in this paper considering the frame of discernment and the influence of intersection among statements which can cause changes of uncertainty. Numerical examples are illustrated to demonstrate the effectiveness of the proposed method. In addition, an application in fault diagnosis is used to show the effectiveness of the proposed method. </div>

scholarly journals An improved conflicting-evidence combination method based on the redistribution of the basic probability assignment

2021 ◽  
Author(s):  
Zezheng Yan ◽  
Hanping Zhao ◽  
Xiaowen Mei
Keyword(s):  
Evidence Theory ◽  
Combination Method ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Combination Methods ◽  
Conflicting Evidence ◽  
Basic Probability ◽  
The Right ◽  
Conflict Intensity ◽  
Dempster’S Rule Of Combination

AbstractDempster–Shafer evidence theory is widely applied in various fields related to information fusion. However, the results are counterintuitive when highly conflicting evidence is fused with Dempster’s rule of combination. Many improved combination methods have been developed to address conflicting evidence. Nevertheless, all of these approaches have inherent flaws. To solve the existing counterintuitive problem more effectively and less conservatively, an improved combination method for conflicting evidence based on the redistribution of the basic probability assignment is proposed. First, the conflict intensity and the unreliability of the evidence are calculated based on the consistency degree, conflict degree and similarity coefficient among the evidence. Second, the redistribution equation of the basic probability assignment is constructed based on the unreliability and conflict intensity, which realizes the redistribution of the basic probability assignment. Third, to avoid excessive redistribution of the basic probability assignment, the precision degree of the evidence obtained by information entropy is used as the correction factor to modify the basic probability assignment for the second time. Finally, Dempster’s rule of combination is used to fuse the modified basic probability assignment. Several different types of examples and actual data sets are given to illustrate the effectiveness and potential of the proposed method. Furthermore, the comparative analysis reveals the proposed method to be better at obtaining the right results than other related methods.

scholarly journals Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 820
Author(s):  
Jingyu Liu ◽  
Yongchuan Tang
Keyword(s):  
Data Fusion ◽  
Evidence Theory ◽  
Multi Agent System ◽  
Agent System ◽  
Combination Rules ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Reasonable Evidence ◽  
Basic Probability ◽  
Multi Agent

The multi-agent information fusion (MAIF) system can alleviate the limitations of a single expert system in dealing with complex situations, as it allows multiple agents to cooperate in order to solve problems in complex environments. Dempster–Shafer (D-S) evidence theory has important applications in multi-source data fusion, pattern recognition, and other fields. However, the traditional Dempster combination rules may produce counterintuitive results when dealing with highly conflicting data. A conflict data fusion method in a multi-agent system based on the base basic probability assignment (bBPA) and evidence distance is proposed in this paper. Firstly, the new bBPA and reconstructed BPA are used to construct the initial belief degree of each agent. Then, the information volume of each evidence group is obtained by calculating the evidence distance so as to modify the reliability and obtain more reasonable evidence. Lastly, the final evidence is fused with the Dempster combination rule to obtain the result. Numerical examples show the effectiveness and availability of the proposed method, which improves the accuracy of the identification process of the MAIF system.

Determination of Basic Probability Assignment Based on Recognition Sequence

2014 ◽  
Vol 1049-1050 ◽  
pp. 1171-1175
Author(s):  
Yan Fei Chen ◽  
Xue Zhi Xia ◽  
Kui Tu
Keyword(s):  
Target Recognition ◽  
Evidence Theory ◽  
Practical Application ◽  
Recognition Sequence ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Recognition Result ◽  
Basic Probability ◽  
Simulation Results

In some practical application of target recognition with sensors, the sensors will give the recognition sequence of targets, which is more detailed than the single recognition result. How to properly construct the basic probability assignment by the recognition sequence becomes the key to successful application of evidence theory. For the recognition sequence of the target recognition results of general sensor is incomplete, and the importance of the types in the recognition sequence is in descending order, this paper proposes a method to construct weights of recognition sequence, the basic probability assignments constructed by the weights are closer to the real recognition results. Simulation results show that this method is more reasonable and effective than the method of contrast.

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