A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion

Tang, Yongchuan; Zhou, Deyun; Xu, Shuai; He, Zichang

doi:10.3390/s17040928

Cited by 82 publications

(31 citation statements)

References 65 publications

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“…The main purpose of information fusion is to integrate multi-source homogeneous or heterogeneous information to obtain a comprehensive information evaluation [11]. At present, there are a variety of information fusion strategies and methods developed by scholars including Bayesian inferences [12], fuzzy reasoning [13,14], D-S evidence theory [15][16][17], and the neural network method [18].…”

Section: Related Workmentioning

confidence: 99%

Research on Information Fusion for Machine Potential Fault Operation and Maintenance

Wan

Zuo

et al. 2020

Symmetry

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In recent years, the development of sensor technology in industry has profoundly changed the way of operation and management in manufacturing enterprises. Due to the popularization and promotion of sensors, the maintenance of machines on the production line are also changing from the subjective experience-based machine maintenance to objective data-driven maintenance decision-making. Therefore, more and more data decision model has been developed through AI technology and intelligence algorithms. Equally important, the information fusion between decision results, which got by data decision model, has also received widespread attention. Information fusion is performed on symmetric data structures. The asymmetric data under the symmetric structure leads to the difference in information fusion results. Therefore, fully considering the potential differences of asymmetric data under a symmetric structure is an important content of information fusion. In view of the above, this paper studies how to make information fusion between different decision results through the framework of D-S evidence theory and discusses the deficiency of D-S evidence theory in detail. Based on D-S evidence theory, then a comprehensive evidence method for information fusion is proposed in this paper. We illustrate the rationality and effectiveness of our method through analysis of experiment case. And, this method is applied to a real case from industry. Finally, the irrationality of the traditional D-S method in the comprehensive decision-making results of machine operation and maintenance was solved by our novel method.

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Section: Related Workmentioning

confidence: 99%

Research on Information Fusion for Machine Potential Fault Operation and Maintenance

Wan

Zuo

et al. 2020

Symmetry

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“…If there are a small number of particles, the search range of the PSO algorithm is small, which makes it difficult to obtain solutions that meet the expected goals. The eigenvalues extracted when the sliding window size is 360 are used as the input data of the PSO-ANN model, and different particle swarm numbers (10,20,30,40,50, and 60, respectively) are used to initialize the PSO-ANN model for multifeature fusion fault prediction. The relationship between the number of iterations of the PSO algorithm initialized by different numbers of particles and the loss value of the ANN is shown in Figure 9 (the maximum number of iterations of the PSO algorithm was uniformly set to 100).…”

Section: Feature-level Fusion Fault Prediction Experiments Based On a mentioning

confidence: 99%

“…Based on the results of the PSO-ANN multifeature fusion fault prediction experiment in Section 6.3, Table 13 shows that multiple PSO-ANN models trained with different single features are combined with basic DS evidence theory, DS evidence theory and Deng entropy [30], DS evidence theory combined with evidence distance and Deng entropy [31], DS evidence theory combined with cosine similarity and Deng entropy [32], and the proposed method for fault prediction accuracy of decision-level fusion. As shown in Table 13, the method proposed in [30] had a prediction accuracy that was lower than that of the basic DS theory when the sliding window sizes were 120 and 960. When the sliding window sizes were 240 and 360, the prediction accuracies of the methods proposed in [31,32] were also lower than those of the basic DS evidence theory.…”

Section: Decision-level Fusion Fault Prediction Experiments Based On Pmentioning

confidence: 99%

“…According to belief entropy, scholars have proposed a variety of schemes to preprocess the original evidence, which is combined with DS evidence theory for information fusion. For example, Jiang et al [29] and Tang et al [30] used belief entropy to preprocess the original evidence. Wang et al [31] proposed the preprocessing of original evidence using evidence distance and belief entropy.…”

Section: Introductionmentioning

confidence: 99%

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Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Huang

Liu

2019

Sensors

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Vibration sensing data is an important resource for mechanical fault prediction, which is widely used in the industrial sector. Artificial neural networks (ANNs) are important tools for classifying vibration sensing data. However, their basic structures and hyperparameters must be manually adjusted, which results in the prediction accuracy easily falling into the local optimum. For data with high levels of uncertainty, it is difficult for an ANN to obtain correct prediction results. Therefore, we propose a multifeature fusion model based on Dempster-Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The model first used the particle swarm optimization algorithm to optimize the structure and hyperparameters of the ANN, thereby improving its prediction accuracy. Then, the prediction error data of the multifeature fusion using a PSO-ANN is repredicted using multiple PSO-ANNs with different single feature training to obtain new prediction results. Finally, the Dempster-Shafer evidence theory was applied to the decision-level fusion of the new prediction results preprocessed with prediction accuracy and belief entropy, thus improving the model's ability to process uncertain data. The experimental results indicated that compared to the K-nearest neighbor method, support vector machine, and long short-term memory neural networks, the proposed model can effectively improve the accuracy of fault prediction.

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“…However, the information, which may be obtained from a multi-sensor system, is heterogeneous and imprecision [ 18 ]. Therefore, it is essential that the uncertain information is pre-processed before data fusion and decision-making [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Evidential-IOWA Sensor Data Fusion Approach in Fault Diagnosis

Tang

Zhou

Zhuang

et al. 2017

Sensors

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As an important tool of information fusion, Dempster–Shafer evidence theory is widely applied in handling the uncertain information in fault diagnosis. However, an incorrect result may be obtained if the combined evidence is highly conflicting, which may leads to failure in locating the fault. To deal with the problem, an improved evidential-Induced Ordered Weighted Averaging (IOWA) sensor data fusion approach is proposed in the frame of Dempster–Shafer evidence theory. In the new method, the IOWA operator is used to determine the weight of different sensor data source, while determining the parameter of the IOWA, both the distance of evidence and the belief entropy are taken into consideration. First, based on the global distance of evidence and the global belief entropy, the α value of IOWA is obtained. Simultaneously, a weight vector is given based on the maximum entropy method model. Then, according to IOWA operator, the evidence are modified before applying the Dempster’s combination rule. The proposed method has a better performance in conflict management and fault diagnosis due to the fact that the information volume of each evidence is taken into consideration. A numerical example and a case study in fault diagnosis are presented to show the rationality and efficiency of the proposed method.

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A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion

Cited by 82 publications

References 65 publications

Research on Information Fusion for Machine Potential Fault Operation and Maintenance

Research on Information Fusion for Machine Potential Fault Operation and Maintenance

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

An Improved Evidential-IOWA Sensor Data Fusion Approach in Fault Diagnosis

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