The Experts below are selected from a list of 177702 Experts worldwide ranked by ideXlab platform
Jinglong Chen - One of the best experts on this subject based on the ideXlab platform.
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mono component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data driven adaptive fourier spectrum segment
Mechanical Systems and Signal Processing, 2016Co-Authors: Jinglong Chen, Yanyang Zi, Yueming Li, Zhengjia HeAbstract:Abstract Due to the multi-modulation feature in most of the vibration Signals, the extraction of embedded fault information from condition monitoring data for mechanical fault diagnosis still is not a relaxed task. Despite the reported achievements, Wavelet transform follows the dyadic partition scheme and would not allow a data-driven frequency partition. And then Empirical Wavelet Transform (EWT) is used to extract inherent modulation information by decomposing Signal into mono-components under an orthogonal basis and non-dyadic partition scheme. However, the pre-defined segment way of Fourier spectrum without dependence on Analyzed Signals may result in inaccurate mono-component identification. In this paper, the modified EWT (MEWT) method via data-driven adaptive Fourier spectrum segment is proposed for mechanical fault identification. First, inner product is calculated between the Fourier spectrum of Analyzed Signal and Gaussian function for scale representation. Then, adaptive spectrum segment is achieved by detecting local minima of the scale representation. Finally, empirical modes can be obtained by adaptively merging mono-components based on their envelope spectrum similarity. The adaptively extracted empirical modes are Analyzed for mechanical fault identification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.
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mono component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data driven adaptive fourier spectrum segment
Mechanical Systems and Signal Processing, 2016Co-Authors: Jun Pan, Jinglong ChenAbstract:Abstract Due to the multi-modulation feature in most of the vibration Signals, the extraction of embedded fault information from condition monitoring data for mechanical fault diagnosis still is not a relaxed task. Despite the reported achievements, Wavelet transform follows the dyadic partition scheme and would not allow a data-driven frequency partition. And then Empirical Wavelet Transform (EWT) is used to extract inherent modulation information by decomposing Signal into mono-components under an orthogonal basis and non-dyadic partition scheme. However, the pre-defined segment way of Fourier spectrum without dependence on Analyzed Signals may result in inaccurate mono-component identification. In this paper, the modified EWT (MEWT) method via data-driven adaptive Fourier spectrum segment is proposed for mechanical fault identification. First, inner product is calculated between the Fourier spectrum of Analyzed Signal and Gaussian function for scale representation. Then, adaptive spectrum segment is achieved by detecting local minima of the scale representation. Finally, empirical modes can be obtained by adaptively merging mono-components based on their envelope spectrum similarity. The adaptively extracted empirical modes are Analyzed for mechanical fault identification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.
Zhengjia He - One of the best experts on this subject based on the ideXlab platform.
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mono component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data driven adaptive fourier spectrum segment
Mechanical Systems and Signal Processing, 2016Co-Authors: Jinglong Chen, Yanyang Zi, Yueming Li, Zhengjia HeAbstract:Abstract Due to the multi-modulation feature in most of the vibration Signals, the extraction of embedded fault information from condition monitoring data for mechanical fault diagnosis still is not a relaxed task. Despite the reported achievements, Wavelet transform follows the dyadic partition scheme and would not allow a data-driven frequency partition. And then Empirical Wavelet Transform (EWT) is used to extract inherent modulation information by decomposing Signal into mono-components under an orthogonal basis and non-dyadic partition scheme. However, the pre-defined segment way of Fourier spectrum without dependence on Analyzed Signals may result in inaccurate mono-component identification. In this paper, the modified EWT (MEWT) method via data-driven adaptive Fourier spectrum segment is proposed for mechanical fault identification. First, inner product is calculated between the Fourier spectrum of Analyzed Signal and Gaussian function for scale representation. Then, adaptive spectrum segment is achieved by detecting local minima of the scale representation. Finally, empirical modes can be obtained by adaptively merging mono-components based on their envelope spectrum similarity. The adaptively extracted empirical modes are Analyzed for mechanical fault identification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.
Jun Pan - One of the best experts on this subject based on the ideXlab platform.
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mono component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data driven adaptive fourier spectrum segment
Mechanical Systems and Signal Processing, 2016Co-Authors: Jun Pan, Jinglong ChenAbstract:Abstract Due to the multi-modulation feature in most of the vibration Signals, the extraction of embedded fault information from condition monitoring data for mechanical fault diagnosis still is not a relaxed task. Despite the reported achievements, Wavelet transform follows the dyadic partition scheme and would not allow a data-driven frequency partition. And then Empirical Wavelet Transform (EWT) is used to extract inherent modulation information by decomposing Signal into mono-components under an orthogonal basis and non-dyadic partition scheme. However, the pre-defined segment way of Fourier spectrum without dependence on Analyzed Signals may result in inaccurate mono-component identification. In this paper, the modified EWT (MEWT) method via data-driven adaptive Fourier spectrum segment is proposed for mechanical fault identification. First, inner product is calculated between the Fourier spectrum of Analyzed Signal and Gaussian function for scale representation. Then, adaptive spectrum segment is achieved by detecting local minima of the scale representation. Finally, empirical modes can be obtained by adaptively merging mono-components based on their envelope spectrum similarity. The adaptively extracted empirical modes are Analyzed for mechanical fault identification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.
Lena Claesson-welsh - One of the best experts on this subject based on the ideXlab platform.
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High sensitivity isoelectric focusing to establish a Signaling biomarker for the diagnosis of human colorectal cancer
BMC Cancer, 2016Co-Authors: Narendra Padhan, Torbjörn E. M. Nordling, Magnus Sundström, Peter Åkerud, Helgi Birgisson, Peter Nygren, Sven Nelander, Lena Claesson-welshAbstract:Background The progression of colorectal cancer (CRC) involves recurrent amplifications/mutations in the epidermal growth factor receptor (EGFR) and downstream Signal transducers of the Ras pathway, KRAS and BRAF. Whether genetic events predicted to result in increased and constitutive Signaling indeed lead to enhanced biological activity is often unclear and, due to technical challenges, unexplored. Here, we investigated proliferative Signaling in CRC using a highly sensitive method for protein detection. The aim of the study was to determine whether multiple changes in proliferative Signaling in CRC could be combined and exploited as a “complex biomarker” for diagnostic purposes. Methods We used robotized capillary isoelectric focusing as well as conventional immunoblotting for the comprehensive analysis of epidermal growth factor receptor Signaling pathways converging on extracellular regulated kinase 1/2 (ERK1/2), AKT, phospholipase Cγ1 (PLCγ1) and c-SRC in normal mucosa compared with CRC stage II and IV. Computational analyses were used to test different activity patterns for the Analyzed Signal transducers. Results Signaling pathways implicated in cell proliferation were differently dysregulated in CRC and, unexpectedly, several were downregulated in disease. Thus, levels of activated ERK1 (pERK1), but not pERK2, decreased in stage II and IV while total ERK1/2 expression remained unaffected. In addition, c-SRC expression was lower in CRC compared with normal tissues and phosphorylation on the activating residue Y418 was not detected. In contrast, PLCγ1 and AKT expression levels were elevated in disease. Immunoblotting of the different Signal transducers, run in parallel to capillary isoelectric focusing, showed higher variability and lower sensitivity and resolution. Computational analyses showed that, while individual Signaling changes lacked predictive power, using the combination of changes in three Signaling components to create a “complex biomarker” allowed with very high accuracy, the correct diagnosis of tissues as either normal or cancerous. Conclusions We present techniques that allow rapid and sensitive determination of cancer Signaling that can be used to differentiate colorectal cancer from normal tissue.
Yueming Li - One of the best experts on this subject based on the ideXlab platform.
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mono component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data driven adaptive fourier spectrum segment
Mechanical Systems and Signal Processing, 2016Co-Authors: Jinglong Chen, Yanyang Zi, Yueming Li, Zhengjia HeAbstract:Abstract Due to the multi-modulation feature in most of the vibration Signals, the extraction of embedded fault information from condition monitoring data for mechanical fault diagnosis still is not a relaxed task. Despite the reported achievements, Wavelet transform follows the dyadic partition scheme and would not allow a data-driven frequency partition. And then Empirical Wavelet Transform (EWT) is used to extract inherent modulation information by decomposing Signal into mono-components under an orthogonal basis and non-dyadic partition scheme. However, the pre-defined segment way of Fourier spectrum without dependence on Analyzed Signals may result in inaccurate mono-component identification. In this paper, the modified EWT (MEWT) method via data-driven adaptive Fourier spectrum segment is proposed for mechanical fault identification. First, inner product is calculated between the Fourier spectrum of Analyzed Signal and Gaussian function for scale representation. Then, adaptive spectrum segment is achieved by detecting local minima of the scale representation. Finally, empirical modes can be obtained by adaptively merging mono-components based on their envelope spectrum similarity. The adaptively extracted empirical modes are Analyzed for mechanical fault identification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.