Slipping Detection of Electric Locomotive Based on Empirical Wavelet Transform, Fuzzy Entropy Algorithm and Support Vector Machine

Huang, Jingchun; Jiang, Boya; Xu, Congqian; Wang, Naifu

doi:10.1109/tvt.2021.3094872

Cited by 13 publications

(5 citation statements)

References 29 publications

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“…The attribution of an input can then be inferred. The "one to many" method is to construct a SVM subclassifier for class problems [29]. This paper studies the problem of accurate evaluation of bank credit risk.…”

Section: Research On Modeling Methods Of Credit Risk Assessmentmentioning

confidence: 99%

Credit Risk Assessment Modeling Method Based on Fuzzy Integral and SVM

Zhou

2022

Mobile Information Systems

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With the development of financial globalization and financial market volatility, credit risk has become more prominent and serious, and how to establish an effective enterprise credit evaluation system and bank credit risk evaluation model, provide scientific quantitative decision-making basis for bank decision-making, reduce non-performing loans, and improve the quality of credit assets is a common research topic faced by domestic banks. At present, domestic banks have not been effective to establish risk prevention as the core of credit culture and long-term mechanism, the existence of nonperforming loans is still not fully resolved, new risks continue to appear, and there is a lack of a perfect and effective credit risk evaluation system. With the development of the Internet and financial institutions and the fusion, banks and financial institutions drastically increase the recorded data, and this provides a good prerequisite for the application of intelligent algorithms. In view of the shortcomings of BP neural network in the establishment of credit risk assessment model, such as poor promotion ability and long prediction time, and considering that support vector machine (SVM) can deal with some multi-classification problems, this paper introduces SVM method into the field of bank credit risk assessment and establishes an optimization model of credit risk assessment. This paper discusses the structure and algorithm principle of SVM classification method and proposes an integrated SVM based on fuzzy integral to solve this kind of problem. The results show that the algorithm can effectively improve the prediction accuracy, solve the problem of high computation cost, reduce the occupied memory space, improve the operation efficiency, shorten the training time, and provide a more reliable basis for the rapid and effective evaluation of bank credit risk. On the one hand, the research results expand the application of artificial intelligence technology in the field of economic research; the evaluation model can continuously and accurately measure credit risk is obtained, which provides the necessary basis for upgrading and optimizing credit decision-making, so it has high theoretical value and practical value.

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Section: Research On Modeling Methods Of Credit Risk Assessmentmentioning

confidence: 99%

Credit Risk Assessment Modeling Method Based on Fuzzy Integral and SVM

Zhou

2022

Mobile Information Systems

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“…Conversely, the more complex the series, the larger the fuzzy entropy value. Fuzzy entropy possesses a strong capability to resist interference from background noise [ 19 , 20 , 21 ]. For each type of complex defect, 20 sample datapoints of partial discharge signals were randomly selected and subjected to VMD decomposition.…”

Section: Partial Discharge Signal Feature Extraction Based On Vmd And...mentioning

confidence: 99%

Partial Discharge Signal Pattern Recognition of Composite Insulation Defects in Cross-Linked Polyethylene Cables

Qin,

Zhu,

Zhu

et al. 2024

Sensors

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To investigate the pattern recognition of complex defect types in XLPE (cross-linked polyethylene) cable partial discharges and analyze the effectiveness of identifying partial discharge signal patterns, this study employs the variational mode decomposition (VMD) algorithm alongside entropy theories such as power spectrum entropy, fuzzy entropy, and permutation entropy for feature extraction from partial discharge signals of composite insulation defects. The mean power spectrum entropy (PS), mean fuzzy entropy (FU), mean permutation entropy (PE), as well as the permutation entropy values of IMF2 and IMF13 (Pe) are selected as the characteristic quantities for four categories of partial discharge signals associated with composite defects. Six hundred samples are selected from the partial discharge signals of each type of compound defect, amounting to a total of 2400 samples for the four types of compound defects combined. Each sample comprises five feature values, which are compiled into a dataset. A Snake Optimization Algorithm-optimized Support Vector Machine (SO-SVM) model is designed and trained, using the extracted features from cable partial discharge datasets as case examples for recognizing cable partial discharge signals. The identification outcomes from the SO-SVM model are then compared with those from conventional learning models. The results demonstrate that for partial discharge signals of XLPE cable composite insulation defects, the SO-SVM model yields better identification results than traditional learning models. In terms of recognition accuracy, for scratch and water ingress defects, SO-SVM improves by 14.00% over BP (Back Propagation) neural networks, by 5.66% over GA-BP (Genetic Algorithm–Back Propagation), and by 12.50% over SVM (support vector machine). For defects involving metal impurities and scratches, SO-SVM improves by 13.39% over BP, 9.34% over GA-BP, and 12.56% over SVM. For defects with metal impurities and water ingress, SO-SVM shows enhancements of 13.80% over BP, 9.47% over GA-BP, and 13.97% over SVM. Lastly, for defects combining metal impurities, water ingress, and scratches, SO-SVM registers increases of 11.90% over BP, 9.59% over GA-BP, and 12.05% over SVM.

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“…As an important guiding, moving, and loading device of a train, the train wheelset tread will inevitably suffer from different types of defects, such as wear, cracks, and scratches, due to the long-term service of rolling contact [1][2][3]. If such defects are allowed to continue to worsen, not only will additional shock vibrations affecting ride comfort be caused, but serious defects, such as wheelset breakage, in extreme cases, can also eventuate.…”

Section: Introductionmentioning

confidence: 99%

Detection of Train Wheelset Tread Defects with Small Samples Based on Local Inference Constraint Network

Liu,

Jiang,

Wang

et al. 2024

Electronics

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Due to the long-term service through wheel-rail rolling contact, the train wheelset tread will inevitably suffer from different types of defects, such as wear, cracks, and scratches. The effective detection of wheelset tread defects can provide critical support for the operation and maintenance of trains. In this paper, a new method based on a local inference constraint network is proposed to detect wheelset tread defects, and the main purpose is to address the issue of insufficient feature spaces caused by small samples. First, a generative adversarial network is applied to generate diverse samples with semantic consistency. An attention mechanism module is introduced into the feature extraction network to increase the importance of defect features. Then, the residual spine network for local input decisions is constructed to establish an association between sample features and defect types. Furthermore, the network’s activation function is improved to obtain higher learning speed and accuracy with fewer parameters. Finally, the validity and feasibility of the proposed method are verified using experimental data.

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Slipping Detection of Electric Locomotive Based on Empirical Wavelet Transform, Fuzzy Entropy Algorithm and Support Vector Machine

Cited by 13 publications

References 29 publications

Credit Risk Assessment Modeling Method Based on Fuzzy Integral and SVM

Credit Risk Assessment Modeling Method Based on Fuzzy Integral and SVM

Partial Discharge Signal Pattern Recognition of Composite Insulation Defects in Cross-Linked Polyethylene Cables

Detection of Train Wheelset Tread Defects with Small Samples Based on Local Inference Constraint Network

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