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  • Hindawi Limited  (13)
  • 1
    Online Resource
    Online Resource
    Fault Detection of a Wheelset Bearing Based on Appropriately Sparse Impulse Extraction
    Hindawi Limited ; 2017
    In:  Shock and Vibration Vol. 2017 ( 2017), p. 1-17
    Details
    In: Shock and Vibration, Hindawi Limited, Vol. 2017 ( 2017), p. 1-17
    Abstract: Convolution sparse representation (CSR) is a novel compressive sensing technique proposed in 2016 and provides an excellent framework for extracting the impulses induced by bearing faults and the unevenness of wheel tread. However, its sparsity performance on extracting impulses is sensitive to the improper penalty parameter. So, a novel fault detection method, appropriately sparse impulse extraction, is proposed based on the combination of CSR, estimating the number of atom types (ENA), and crest factor. The type of atoms embedded in vibration signals is estimated by ENA. Aiming at the different types of atoms, the impulses with different sparse characteristic are spanned by CSR with different penalty parameters. The appropriately sparse impulses are selected for fault detection based on the maximal crest factor. The simulation validation, experiment verification, and practical application are conducted to validate the effectiveness of the proposed appropriately sparse impulses extraction. These results show that the proposed appropriately sparse impulse extraction not only can obtain fault-characteristic frequency and its harmonics for fault judgment but also describes the dynamic behaviour between elementary defects and their matching surfaces. In addition, the proposed appropriately sparse impulse extraction can isolate the impulses with different types of atoms and is very suitable for detecting the wheelset bearing faults.
    Type of Medium: Online Resource
    ISSN: 1070-9622 , 1875-9203
    URL: Article
    DOI: 10.1155/2017/7853918
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2017
    detail.hit.zdb_id: 2070162-7
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  • 2
    Online Resource
    Online Resource
    Two-Dimensional Impact Reconstruction Method for Rail Defect Inspection
    Hindawi Limited ; 2014
    In:  Mathematical Problems in Engineering Vol. 2014 ( 2014), p. 1-9
    Details
    In: Mathematical Problems in Engineering, Hindawi Limited, Vol. 2014 ( 2014), p. 1-9
    Abstract: The safety of train operating is seriously menaced by the rail defects, so it is of great significance to inspect rail defects dynamically while the train is operating. This paper presents a two-dimensional impact reconstruction method to realize the on-line inspection of rail defects. The proposed method utilizes preprocessing technology to convert time domain vertical vibration signals acquired by wireless sensor network to space signals. The modern time-frequency analysis method is improved to reconstruct the obtained multisensor information. Then, the image fusion processing technology based on spectrum threshold processing and node color labeling is proposed to reduce the noise, and blank the periodic impact signal caused by rail joints and locomotive running gear. This method can convert the aperiodic impact signals caused by rail defects to partial periodic impact signals, and locate the rail defects. An application indicates that the two-dimensional impact reconstruction method could display the impact caused by rail defects obviously, and is an effective on-line rail defects inspection method.
    Type of Medium: Online Resource
    ISSN: 1024-123X , 1563-5147
    URL: Article
    DOI: 10.1155/2014/236574
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2014
    detail.hit.zdb_id: 2014442-8
    SSG: 11
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  • 3
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    Online Resource
    Radioprotective Effect of Grape Seed Proanthocyanidins In Vitro and In Vivo
    Hindawi Limited ; 2016
    In:  Oxidative Medicine and Cellular Longevity Vol. 2016 ( 2016), p. 1-7
    Details
    In: Oxidative Medicine and Cellular Longevity, Hindawi Limited, Vol. 2016 ( 2016), p. 1-7
    Abstract: We have demonstrated that grape seed proanthocyanidins (GSPs) could effectively scavenge hydroxyl radical ( • O H ) in a dose-dependent manner. Since most of the ionizing radiation- (IR-) induced injuries were caused by • O H , this study was to investigate whether GSPs would mitigate IR-induced injuries in vitro and in vivo. We demonstrated that GSPs could significantly reduce IR-induced DNA strand breaks (DSBs) and apoptosis of human lymphocyte AHH-1 cells. This study also showed that GSPs could protect white blood cells (WBC) from IR-induced injuries, speed up the weight of mice back, and decrease plasma malondialdehyde (MDA), thus improving the survival rates of mice after ionizing radiation. It is suggested that GSPs have a potential as an effective and safe radioprotective agent.
    Type of Medium: Online Resource
    ISSN: 1942-0900 , 1942-0994
    URL: Article
    DOI: 10.1155/2016/5706751
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2016
    detail.hit.zdb_id: 2455981-7
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  • 4
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    Morphological Neuroimaging Biomarkers for Tinnitus: Evidence Obtained by Applying Machine Learning
    Hindawi Limited ; 2019
    In:  Neural Plasticity Vol. 2019 ( 2019-12-13), p. 1-11
    Details
    In: Neural Plasticity, Hindawi Limited, Vol. 2019 ( 2019-12-13), p. 1-11
    Abstract: According to previous studies, many neuroanatomical alterations have been detected in patients with tinnitus. However, the results of these studies have been inconsistent. The objective of this study was to explore the cortical/subcortical morphological neuroimaging biomarkers that may characterize idiopathic tinnitus using machine learning methods. Forty-six patients with idiopathic tinnitus and fifty-six healthy subjects were included in this study. For each subject, the gray matter volume of 61 brain regions was extracted as an original feature pool. From this feature pool, a hybrid feature selection algorithm combining the F -score and sequential forward floating selection (SFFS) methods was performed to select features. Then, the selected features were used to train a support vector machine (SVM) model. The area under the curve (AUC) and accuracy were used to assess the performance of the classification model. As a result, a combination of 13 cortical/subcortical brain regions was found to have the highest classification accuracy for effectively differentiating patients with tinnitus from healthy subjects. These brain regions include the bilateral hypothalamus, right insula, bilateral superior temporal gyrus, left rostral middle frontal gyrus, bilateral inferior temporal gyrus, right inferior parietal lobule, right transverse temporal gyrus, right middle temporal gyrus, right cingulate gyrus, and left superior frontal gyrus. The accuracy in the training and test datasets was 80.49% and 80.00%, respectively, and the AUC was 0.8586. To the best of our knowledge, this is the first study to elucidate brain morphological changes in patients with tinnitus by applying an SVM classifier. This study provides validated cortical/subcortical morphological neuroimaging biomarkers to differentiate patients with tinnitus from healthy subjects and contributes to the understanding of neuroanatomical alterations in patients with tinnitus.
    Type of Medium: Online Resource
    ISSN: 2090-5904 , 1687-5443
    URL: Article
    DOI: 10.1155/2019/1712342
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2019
    detail.hit.zdb_id: 2236872-3
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  • 5
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    Online Resource
    Normality Criteria of Meromorphic Functions That Share a Holomorphic Function
    Hindawi Limited ; 2012
    In:  Abstract and Applied Analysis Vol. 2012 ( 2012), p. 1-11
    Details
    In: Abstract and Applied Analysis, Hindawi Limited, Vol. 2012 ( 2012), p. 1-11
    Abstract: Let F be a family of meromorphic functions defined in D , let ψ ( ≢ 0 ) , a 0 , a 1 , . . . , a k - 1 be holomorphic functions in D , and let k be a positive integer. Suppose that, for every function f ∈ F , f ≠ 0 , P ( f ) = f ( k ) + a k - 1 f ( k - 1 ) + ⋯ + a 1 f ' + a 0 f ≠ 0 and, for every pair functions ( f , g ) ∈ F , P ( f ) , P ( g ) share ψ , then F is normal in D .
    Type of Medium: Online Resource
    ISSN: 1085-3375 , 1687-0409
    URL: Article
    DOI: 10.1155/2012/582854
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2012
    detail.hit.zdb_id: 2064801-7
    SSG: 17,1
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  • 6
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    Bäcklund Transformation and New Exact Solutions of the Sharma-Tasso-Olver Equation
    Hindawi Limited ; 2011
    In:  Abstract and Applied Analysis Vol. 2011 ( 2011), p. 1-8
    Details
    In: Abstract and Applied Analysis, Hindawi Limited, Vol. 2011 ( 2011), p. 1-8
    Abstract: The Sharma-Tasso-Olver (STO) equation is investigated. The Painlevé analysis is efficiently used for analytic study of this equation. The Bäcklund transformations and some new exact solutions are formally derived.
    Type of Medium: Online Resource
    ISSN: 1085-3375 , 1687-0409
    URL: Article
    DOI: 10.1155/2011/935710
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2011
    detail.hit.zdb_id: 2064801-7
    SSG: 17,1
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  • 7
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    On Properties of Differences Polynomials about Meromorphic Functions
    Hindawi Limited ; 2012
    In:  Discrete Dynamics in Nature and Society Vol. 2012 ( 2012), p. 1-11
    Details
    In: Discrete Dynamics in Nature and Society, Hindawi Limited, Vol. 2012 ( 2012), p. 1-11
    Abstract: We study the value distribution of a special class difference polynomial about finite order meromorphic function. Our methods of the proof are also different from ones in the previous results by Chen (2011), Liu and Laine (2010), and Liu and Yang (2009).
    Type of Medium: Online Resource
    ISSN: 1026-0226 , 1607-887X
    URL: Article
    DOI: 10.1155/2012/569305
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2012
    detail.hit.zdb_id: 2033014-5
    SSG: 11
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  • 8
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    Fault Detection of High-Speed Train Wheelset Bearing Based on Impulse-Envelope Manifold
    Hindawi Limited ; 2017
    In:  Shock and Vibration Vol. 2017 ( 2017), p. 1-17
    Details
    In: Shock and Vibration, Hindawi Limited, Vol. 2017 ( 2017), p. 1-17
    Abstract: A novel fault detection method employing the impulse-envelope manifold is proposed in this paper which is based on the combination of convolution sparse representation (CSR) and Hilbert transform manifold learning. The impulses with different sparse characteristics are extracted by the CSR with different penalty parameters. The impulse-envelope space is constructed through Hilbert transform on the extracted impulses. The manifold based on impulse-envelope space (impulse-envelope manifold) is executed to learn the low-dimensionality intrinsic envelope of vibration signals for fault detection. The analyzed results based on simulations, experimental tests, and practical applications show that ( 1 ) the impulse-envelope manifold with both isometric mapping (Isomap) and locally linear coordination (LLC) can be successfully used to extract the intrinsic envelope of the impulses where local tangent space analysis (LTSA) fails to perform and ( 2 ) the impulse-envelope manifold with Isomap outperforms those with LLC in terms of strengthening envelopes and the number of extracted harmonics. The proposed impulse-envelope manifold with Isomap is superior in extracting the intrinsic envelope, strengthening the amplitude of intrinsic envelope spectra, and enlarging the harmonic number of fault-characteristic frequency. The proposed technique is highly suitable for extracting intrinsic envelopes for bearing fault detection.
    Type of Medium: Online Resource
    ISSN: 1070-9622 , 1875-9203
    URL: Article
    DOI: 10.1155/2017/2104720
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2017
    detail.hit.zdb_id: 2070162-7
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  • 9
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    A Novel Wheelset Bearing Fault Diagnosis Method Integrated CEEMDAN, Periodic Segment Matrix, and SVD
    Hindawi Limited ; 2018
    In:  Shock and Vibration Vol. 2018 ( 2018-11-05), p. 1-18
    Details
    In: Shock and Vibration, Hindawi Limited, Vol. 2018 ( 2018-11-05), p. 1-18
    Abstract: A novel fault diagnosis method, named CPS, is proposed based on the combination of CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), PSM (periodic segment matrix), and SVD (singular value decomposition). Firstly, the collected vibration signals are decomposed into a set of IMFs using CEEMDAN. Secondly, the PSM of the selected IMFs is constructed. Thirdly, singular values are obtained by SVD conducted on the space of PSM. Fourthly, the impulse components are enhanced by the singular value reconstruction with the first maximal singular value. Finally, the squared envelope spectra of the reconstructed signals are used to diagnose the wheelset bearing faults. The effectiveness of the proposed CPS has been verified by simulations and experiments. Compared to the well-known Hankel-based SVD, the proposed CPS performs better at extracting the weak periodic impulse responses from the measured signals with strong noise and interferences.
    Type of Medium: Online Resource
    ISSN: 1070-9622 , 1875-9203
    URL: Article
    DOI: 10.1155/2018/1382726
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2018
    detail.hit.zdb_id: 2070162-7
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  • 10
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    Online Resource
    Wheelset-Bearing Fault Detection Using Adaptive Convolution Sparse Representation
    Hindawi Limited ; 2019
    In:  Shock and Vibration Vol. 2019 ( 2019-11-14), p. 1-26
    Details
    In: Shock and Vibration, Hindawi Limited, Vol. 2019 ( 2019-11-14), p. 1-26
    Abstract: Wheelset bearings are crucial mechanical components of high-speed trains. Wheelset-bearing fault detection is of great significance to ensure the safety of high-speed train service. Convolution sparse representations (CSRs) provide an excellent framework for extracting impulse responses induced by bearing faults. However, the performance of CSR on extracting impulse responses is fairly sensitive to inappropriate selection of method-related parameters, and a convolution model for representing the impulse responses has not been discussed. In view of these two unsolved problems, a convolutional representation model of the impulse response series is developed. A novel fault detection method, named adaptive CSR (ACSR), is then proposed based on combinations of CSR and methods for estimating three parameters related to CSR. Finally, the effectiveness of the proposed ACSR method is validated via simulation, bench testing, and a real-life running test employing a high-speed train.
    Type of Medium: Online Resource
    ISSN: 1070-9622 , 1875-9203
    URL: Article
    DOI: 10.1155/2019/7198693
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2019
    detail.hit.zdb_id: 2070162-7
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