Magnetoacoustic fusion life prediction method for retired components based on D-S evidence theory

2021 ◽  
Vol 63 (8) ◽  
pp. 488-495
Author(s):  
Y T Gao ◽  
Z M Hu ◽  
J C Leng
Keyword(s):  
Probability Distribution ◽  
Life Prediction ◽  
Back Propagation ◽  
Evidence Theory ◽  
Prediction Method ◽  
Magnetic Memory ◽  
Characteristic Parameters ◽  
Fusion Algorithm ◽  
Weighted Fusion ◽  
Basic Probability

Prediction of the remaining life of remanufacturing blanks is crucial to evaluate their remanufacturability. To overcome the deficiency of obtaining insufficient fatigue damage characteristic information using a single non-destructive testing method, a new magnetoacoustic fusion life prediction method based on Dempster-Shafer (D-S) evidence theory optimised by a weighted fusion algorithm is proposed. The characteristic parameters of metal magnetic memory (MMM) and acoustic emission (AE) signals are first extracted on the basis of a fatigue experiment and data layer fusion is carried out to establish the mapping relationship between MMM and AE characteristic parameters and specimen life based on a back-propagation (BP) neural network. The basic probability distribution of the life is assigned in a fuzzy manner according to the normal distribution and the reliability function of each life interval is obtained by data fusion based on D-S evidence theory. Furthermore, the basic probability distribution value is modified based on a weighted fusion algorithm and the corrected data are fused to obtain a more accurate life prediction result.

scholarly journals Health Monitoring and Diagnosis of Equipment Based on Multi-sensor Fusion

2018 ◽  
Vol 14 (04) ◽  
pp. 4
Author(s):  
Xuemei Yao ◽  
Shaobo Li ◽  
Yong Yao ◽  
Xiaoting Xie
Keyword(s):  
Evidence Theory ◽  
Sensor Data ◽  
Uncertain Information ◽  
Fusion Algorithm ◽  
Monitoring And Diagnosis ◽  
Multi Sensor Data Fusion ◽  
Basic Probability ◽  
Optimal Fusion ◽  
Single Sensor ◽  
Mechanical Devices

As the information measured by a single sensor cannot reflect the real situation of mechanical devices completely, a multi-sensor data fusion based on evidence theory is introduced. Evidence theory has the advantage of dealing with uncertain information. However, it produces unreasonable conclusions when the evidence conflicts. An improved fusion method is proposed to solve this problem. Basic probability assignment of evidence is corrected according to evidence and sensor weights, and an optimal fusion algorithm is selected by comparing an introduced threshold and a conflict factor. The effectiveness and practicability of the algorithm are tested by simulating the monitoring and diagnosis of rolling bearings. The result shows that the method has better robustness.

scholarly journals Region-Dot Conversion Fusion Algorithm and Application

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Zhouxing Fu ◽  
Mei Wang ◽  
Jingyi Du ◽  
Hsiung-Cheng Lin ◽  
Ningbo Xu ◽  
...  
Keyword(s):  
Air Quality ◽  
Belief Function ◽  
Pollution Level ◽  
Fusion Algorithm ◽  
Maximum Probability ◽  
Interior Decoration ◽  
Weighted Fusion ◽  
Air Pollutions ◽  
Basic Probability ◽  
Current Measuring

The interior decoration materials and the new furniture using formaldehyde, ammonia, and other poisonous substances are known as the main sources of indoor air pollutions. However, it is still a big challenge to estimate accurately the overall air quality by using the current measuring tools. Accordingly, the region-dot fusion (RDF) algorithm is proposed to evaluate the air quality in this paper. For the conversion from a region to a dot, the region-dot function is firstly defined as the summation of the belief function and the weighted width of the belief interval. In the RDF algorithm, the belief intervals of the two sensors with the basic probability functions are calculated based on the measurements of formaldehyde sensor and ammonia sensor. Then, the belief intervals are converted to the specific values. After the computation of collision degree and combination, the pollution level represented by a belief interval with the maximum probability is selected as the outcome of fusion decision. Compared with the weighted fusion algorithm and D-S evidence reasoning method, it is experimentally proved that the RDF algorithm can improve the separability of the belief intervals of the belief functions. Also, the evidence collision degree is decreased dramatically.

scholarly journals Determining Weights in Multi-Criteria Decision Making Based on Negation of Probability Distribution under Uncertain Environment

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 191 ◽  
Author(s):  
Chao Sun ◽  
Shiying Li ◽  
Yong Deng
Keyword(s):  
Decision Making ◽  
Probability Distribution ◽  
Evidence Theory ◽  
Weight Coefficient ◽  
Multi Criteria Decision Making ◽  
Basic Probability ◽  
Fuzzy Degree ◽  
Infinite Set ◽  
Weight Coefficients ◽  
Selection Of

Multi-criteria decision making (MCDM) refers to the decision making in the limited or infinite set of conflicting schemes. At present, the general method is to obtain the weight coefficients of each scheme based on different criteria through the expert questionnaire survey, and then use the Dempster–Shafer Evidence Theory (D-S theory) to model all schemes into a complete identification framework to generate the corresponding basic probability assignment (BPA). The scheme with the highest belief value is then chosen. In the above process, using different methods to determine the weight coefficient will have different effects on the final selection of alternatives. To reduce the uncertainty caused by subjectively determining the weight coefficients of different criteria and further improve the level of multi-criteria decision-making, this paper combines negation of probability distribution with evidence theory and proposes a weights-determining method in MCDM based on negation of probability distribution. Through the quantitative evaluation of the fuzzy degree of the criterion, the uncertainty caused by human subjective factors is reduced, and the subjective error is corrected to a certain extent.

Information Fusion Algorithm for Electromechanical Equipment Based on DS Evidence Theory

2013 ◽  
Vol 380-384 ◽  
pp. 1125-1128 ◽  
Author(s):  
Yao Hui Zhang ◽  
Jun Xu ◽  
Kang Du
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Prediction Method ◽  
Electrical Equipment ◽  
Fusion Algorithm ◽  
State Prediction ◽  
Level Information ◽  
The Difference ◽  
Equipment State ◽  
Electromechanical Equipment

According to the problem that the difference of test mode, mixed quantitative and qualitative information of electromechanical equipment state prediction, a state prediction method based on information fusion was proposed in this paper. It was used DS evidence theory to fuse decision level information of electromechanical equipments at this method. Simulation results showed that it is feasible and effective that information fusion technology is applied on the state prediction for mechanical and electrical equipment. Information for decision-making integrated repeatedly by different forecasting methods, can greatly reduce the blindness of judgment and improve the accuracy of state prediction.

scholarly journals Bayesian Update with Information Quality under the Framework of Evidence Theory

Entropy ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 5 ◽  
Author(s):  
Yuting Li ◽  
Fuyuan Xiao
Keyword(s):  
Probability Distribution ◽  
Posterior Probability ◽  
Information Quality ◽  
Prior Probability ◽  
Evidence Theory ◽  
Combination Rule ◽  
Posterior Probability Distribution ◽  
Basic Probability ◽  
Bayesian Update ◽  
Different Sources

Bayesian update is widely used in data fusion. However, the information quality is not taken into consideration in classical Bayesian update method. In this paper, a new Bayesian update with information quality under the framework of evidence theory is proposed. First, the discounting coefficient is determined by information quality. Second, the prior probability distribution is discounted as basic probability assignment. Third, the basic probability assignments from different sources can be combined with Dempster’s combination rule to obtain the fusion result. Finally, with the aid of pignistic probability transformation, the combination result is converted to posterior probability distribution. A numerical example and a real application in target recognition show the efficiency of the proposed method. The proposed method can be seen as the generalized Bayesian update. If the information quality is not considered, the proposed method degenerates to the classical Bayesian update.

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%.

Research on Improved Gamma Transform Face Image Preprocessing Fusion Algorithm under Complex Lighting Conditions

2020 ◽  
Vol 13 ◽  
Author(s):  
Xiaolin Tang ◽  
Xiaogang Wang ◽  
Jin Hou ◽  
Huafeng Wu ◽  
Ping He
Keyword(s):  
Edge Detection ◽  
Face Image ◽  
Light Sources ◽  
Fusion Algorithm ◽  
Edge Information ◽  
Image Preprocessing ◽  
Lighting Conditions ◽  
Weighted Fusion ◽  
The Face ◽  
Sobel Edge Detection

Introduction: Under complex illumination conditions such as poor light sources and light changes rapidly, there are two disadvantages of current gamma transform in preprocessing face image: one is that the parameters of transformation need to be set based on experience; the other is the details of the transformed image are not obvious enough. Objective: Improve the current gamma transform. Methods: This paper proposes a weighted fusion algorithm of adaptive gamma transform and edge feature extraction. First, this paper proposes an adaptive gamma transform algorithm for face image preprocessing, that is, the parameter of transformation generated by calculation according to the specific gray value of the input face image. Secondly, this paper uses Sobel edge detection operator to extract the edge information of the transformed image to get the edge detection image. Finally, this paper uses the adaptively transformed image and the edge detection image to obtain the final processing result through a weighted fusion algorithm. Results: The contrast of the face image after preprocessing is appropriate, and the details of the image are obvious. Conclusion: The method proposed in this paper can enhance the face image while retaining more face details, without human-computer interaction, and has lower computational complexity degree.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

Hybrid remaining useful life prediction method. A case study on railway D-cables

2021 ◽  
pp. 107746
Author(s):  
Yu Zang ◽  
Wei Shangguan ◽  
Baigen Cai ◽  
Huasheng Wang ◽  
Michael. G. Pecht
Keyword(s):  
Life Prediction ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Useful Life
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