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Abstract

For the optimal location of an additional surplus measurements in the design of redundant measurements system, from data reconciliation point of view, of thermal processes, an information entropy has been applied. The relative entropy - Kullback-Leibler divergence, has been used. As a criterion of the optimal location of an additional surplus measurements in a system of measurements data, the minimum of the entropy information of reconciled measurements data has been assumed. Hence, the objective function in the described optimization task is maximum of the relative entropy - Kullback-Leibler divergence concerning sets of raw and reconciled measurements data. Simulation calculation with application of data reconciliation algorithm and Monte Carlo method concerning the influence of installation of the additional surplus measurements on decrease of entropy information of measurements after data validation have been carried out. The example calculations concerned the cross high-pressure heat regeneration system with cascade flow of condensate installed in 153 MW power unit equipped with cooler of steam are presented. Calculations for all variants of configurations of an additional surplus measurements in the analyzed thermal system have been done. Usefulness of the proposed Kullback-Leibler divergence as a objective function has been demonstrated.
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Authors and Affiliations

Marcin Szega
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Abstract

In order to overcome the shortcomings of the dolphin algorithm, which is prone to falling into local optimum and premature convergence, an improved dolphin swarm algorithm, based on the standard dolphin algorithm, was proposed. As a measure of uncertainty, information entropy was used to measure the search stage in the dolphin swarm algorithm. Adaptive step size parameters and dynamic balance factors were introduced to correlate the search step size with the number of iterations and fitness, and to perform adaptive adjustment of the algorithm. Simulation experiments show that, comparing with the basic algorithm and other algorithms, the improved dolphin swarm algorithm is feasible and effective.

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Authors and Affiliations

Y. Li
X. Wang
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Abstract

Buzz, squeak and rattle (BSR) noise has become apparent in vehicles due to the significant reductions in engine noise and road noise. The BSR often occurs in driving condition with many interference signals. Thus, the automatic BSR detection remains a challenge for vehicle engineers. In this paper, a rattle signal denoising and enhancing method is proposed to extract the rattle components from in-vehicle background noise. The proposed method combines the advantages of wavelet packet decomposition and mathematical morphology filter. The critical frequency band and the information entropy are introduced to improve the wavelet packet threshold denoising method. A rattle component enhancing method based on multi-scale compound morphological filter is proposed, and the kurtosis values are introduced to determine the best parameters of the filter. To examine the feasibility of the proposed algorithm, synthetic brake caliper rattle signals with various SNR ratios are prepared to verify the algorithm. In the validation analysis, the proposed method can well remove the disturbance background noise in the signal and extract the rattle components with well SNR ratios. It is believed that the algorithm discussed in this paper can be further applied to facilitate the detection of the vehicle rattle noise in industry.
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Authors and Affiliations

Linyuan Liang
1 2
Shuming Chen
1 2
Peiran Li
1

  1. State Key Laboratory of Vehicle NVH and Safety Technology, Chongqing 401122, China
  2. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
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Abstract

In order to solve the problem of misjudgment caused by the traditional power grid fault diagnosis methods, a new fusion diagnosis method is proposed based on the theory of multisource information fusion. In this method, the fault degree of the power element is deduced by using the Bayesian network. Then, the time-domain singular spectrum entropy, frequencydomain power spectrum entropy and wavelet packet energy spectrum entropy of the electrical signals of each circuit after the failure are extracted, and these three characteristic quantities are taken as the fault support degree of the power components. Finally, the four fault degrees are normalized and classified as four evidence bodies in the D-S evidence theory for multifeature fusion, which reduces the uncertainty brought by a single feature body. Simulation results show that the proposed method can obtain more reliable diagnosis results compared with the traditional methods.
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Bibliography

[1] Yao Yuantao, Wang Jin, Xie Min, Hu Liqin and Wang Jianye, ”A new approach for fault diagnosis with full-scope simulator based on state information imaging in nuclear power plant”, Annals of Nuclear Energy, 2020, 141, 1-9.
[2] Lei Koua, Chuang Liua, Guo-wei Caia, Zhe Zhangb, ”Fault Diagnosis for Power Electronics Converters based on Deep Feedforward Network and Wavelet Compression”, Electric Power Systems Research, 2020, 185, 1-9.
[3] Haibo Zhang, Kai Jia, Weijin Shi, Jianzhao Guo, Weizhi Su and Li Zhang, ”Power Grid Fault Diagnosis Based on Information Theory and Expert System”, Proceedings of the CSU-EPSA,, 2017, 29(8), 111-118.
[4] Jianfeng Zhou, Genserik Reniers and Laobing Zhang, ”A weighted fuzzy Petri-net based approach for security risk assessment in the chemical industry”, Chemical Engineering Science, 2017, 174, 136-145.
[5] Sen Wang and Xiaorun Li, ”Circuit Breaker Fault Detection Method Based on Bayesian Approach”, Industrial Control Computer, 2018, 31(4), 147-151.
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[9] Libo Liu, Tingting Zhao, Yancang Li and Bin Wang, ”An Improved Whale Algorithm Based on Information Entry”, Mathematics in practice and theory, 2020, 50(2), 211-219.
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Authors and Affiliations

Xin Zeng
1 2
Xingzhong Xiong
1 3
Zhongqiang Luo
1 3

  1. School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, China
  2. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, China
  3. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan Universityof Science and Engineering, Yibin, China

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