The bearing weak fault feature extraction is crucial to mechanical fault diagnosis and machine condition monitoring.Envelope analysis based on Hilbert transform has been widely used in bearing fault feature extraction...The bearing weak fault feature extraction is crucial to mechanical fault diagnosis and machine condition monitoring.Envelope analysis based on Hilbert transform has been widely used in bearing fault feature extraction. A generalization of the Hilbert transform, the fractional Hilbert transform is defined in the frequency domain, it is based upon the modification of spatial filter with a fractional parameter, and it can be used to construct a new kind of fractional analytic signal. By performing spectrum analysis on the fractional envelope signal, the fractional envelope spectrum can be obtained. When weak faults occur in a bearing, some of the characteristic frequencies will clearly appear in the fractional envelope spectrum. These characteristic frequencies can be used for bearing weak fault feature extraction.The effectiveness of the proposed method is verified through simulation signal and experiment data.展开更多
In audio classification applications, features extracted from the frequency domain representation of signals are typically focused on the magnitude spectral content, while the phase spectral content is ignored. The co...In audio classification applications, features extracted from the frequency domain representation of signals are typically focused on the magnitude spectral content, while the phase spectral content is ignored. The conventional Fourier Phase Spectrum is a highly discontinuous function;thus, it is not appropriate for feature extraction for classification applications, where function continuity is required. In this work, the sources of phase spectral discontinuities are detected, categorized and compensated, resulting in a phase spectrum with significantly reduced discontinuities. The Hartley Phase Spectrum, introduced as an alternative to the conventional Fourier Phase Spectrum, encapsulates the phase content of the signal more efficiently compared with its Fourier counterpart because, among its other properties, it does not suffer from the phase ‘wrapping ambiguities’ introduced due to the inverse tangent function employed in the Fourier Phase Spectrum computation. In the proposed feature extraction method, statistical features extracted from the Hartley Phase Spectrum are combined with statistical features extracted from the magnitude related spectrum of the signals. The experimental results show that the classification score is higher in case the magnitude and the phase related features are combined, as compared with the case where only magnitude features are used.展开更多
For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing m...For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing methods often rely on lengthy battery charging/discharging data or extensive training samples,which hinders their implementation in practical scenarios.To address this issue,a rapid health estimation method based on short-time charging data and limited labels for in-service battery packs is proposed in this paper.First,a digital twin of battery pack is established to emulate its dynamic behavior across various aging levels and inconsistency degrees.Then,increment capacity sequences(△Q)within a short voltage span are extracted from charging process to indicate battery health.Furthermore,data-driven models based on deep convolutional neural network(DCNN)are constructed to estimate battery state of health(SOH),where the synthetic data is employed to pre-train the models,and transfer learning strategies by using fine-tuning and domain adaptation are utilized to enhance the model adaptability.Finally,field data of 10 EVs exhibiting different SOHs are used to verify the proposed methods.By using the△Q with 100 m V voltage change,the SOH of battery packs can be accurately estimated with an error around 3.2%.展开更多
薄板自动化焊时产生的光反射、飞溅、粉尘等噪声使焊缝位置信息被遮挡,从而影响特征点的识别与提取。因此,提出了用连通区域的算法对焊缝的特征进行标记,并改进了连通区域算法用于提取焊缝特征点和获取其位置信息。在图像预处理之前,用...薄板自动化焊时产生的光反射、飞溅、粉尘等噪声使焊缝位置信息被遮挡,从而影响特征点的识别与提取。因此,提出了用连通区域的算法对焊缝的特征进行标记,并改进了连通区域算法用于提取焊缝特征点和获取其位置信息。在图像预处理之前,用感兴趣区域(Region of interest,ROI)方法对激光条纹进行图像分割,可滤除大量弧光、飞溅等噪声;在图像预处理的过程中,采用中值滤波和最大类间方差的二值化算法降低激光条纹附近的干扰噪声,将激光条纹与背景分离,使焊缝特征更清晰、明显;在图像预处理后,用连通区域的方法对激光条纹进行标记,通过改进的算法判断出连通区域的位置,从而识别焊缝特征点,获得焊缝特征点的位置信息。该算法不仅保留了焊缝激光条纹的边缘信息,还能在复杂的工作环境中完成焊缝特征的识别。通过对比薄板的实际间隙宽度和试验计算出的间隙宽度,该算法平均误差在0.067 mm以内,满足工业中的精度要求,适合激光视觉的焊缝跟踪过程。展开更多
针对目前空管特情处置过程中案例记录利用不足的问题,提出了空管特情案例利用框架,并重点研究了其中的案例特征提取方法。基于TextRank算法提出了融合空管特情领域知识与数据分析的特情案例特征提取算法(Special Situation Case TextRan...针对目前空管特情处置过程中案例记录利用不足的问题,提出了空管特情案例利用框架,并重点研究了其中的案例特征提取方法。基于TextRank算法提出了融合空管特情领域知识与数据分析的特情案例特征提取算法(Special Situation Case TextRank,SSC TextRank)。所提方法利用空管特情领域知识构建领域词典,以提升分词效果,依据风险知识及文本数据分析结果,同时结合层次分析法赋权原理对文本中的特征词进行赋权,以优化各词的初始重要度以及词语重要度权重的计算方法。利用某地区空管局提供的2000年—2019年特情案例验证算法的有效性。结果表明:模型较传统自然语言处理中的关键词提取算法准确率提高了约40%,体现了所提方法在特情案例特征提取方面的有效性和优越性。展开更多
基金supported by National Natural Science Foundation of China(61074161,61273103,61374061)Nantong Science and Technology Plan Project(MS22016051)
文摘The bearing weak fault feature extraction is crucial to mechanical fault diagnosis and machine condition monitoring.Envelope analysis based on Hilbert transform has been widely used in bearing fault feature extraction. A generalization of the Hilbert transform, the fractional Hilbert transform is defined in the frequency domain, it is based upon the modification of spatial filter with a fractional parameter, and it can be used to construct a new kind of fractional analytic signal. By performing spectrum analysis on the fractional envelope signal, the fractional envelope spectrum can be obtained. When weak faults occur in a bearing, some of the characteristic frequencies will clearly appear in the fractional envelope spectrum. These characteristic frequencies can be used for bearing weak fault feature extraction.The effectiveness of the proposed method is verified through simulation signal and experiment data.
文摘In audio classification applications, features extracted from the frequency domain representation of signals are typically focused on the magnitude spectral content, while the phase spectral content is ignored. The conventional Fourier Phase Spectrum is a highly discontinuous function;thus, it is not appropriate for feature extraction for classification applications, where function continuity is required. In this work, the sources of phase spectral discontinuities are detected, categorized and compensated, resulting in a phase spectrum with significantly reduced discontinuities. The Hartley Phase Spectrum, introduced as an alternative to the conventional Fourier Phase Spectrum, encapsulates the phase content of the signal more efficiently compared with its Fourier counterpart because, among its other properties, it does not suffer from the phase ‘wrapping ambiguities’ introduced due to the inverse tangent function employed in the Fourier Phase Spectrum computation. In the proposed feature extraction method, statistical features extracted from the Hartley Phase Spectrum are combined with statistical features extracted from the magnitude related spectrum of the signals. The experimental results show that the classification score is higher in case the magnitude and the phase related features are combined, as compared with the case where only magnitude features are used.
基金supported in part by the National Natural Science Foundation of China,China(Grant No.52102420)the National Key Research and Development Program of China,China(Grant No.2022YFE0102700)the China Postdoctoral Science Foundation,China(Grant No.2023T160085)。
文摘For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery packs.However,existing methods often rely on lengthy battery charging/discharging data or extensive training samples,which hinders their implementation in practical scenarios.To address this issue,a rapid health estimation method based on short-time charging data and limited labels for in-service battery packs is proposed in this paper.First,a digital twin of battery pack is established to emulate its dynamic behavior across various aging levels and inconsistency degrees.Then,increment capacity sequences(△Q)within a short voltage span are extracted from charging process to indicate battery health.Furthermore,data-driven models based on deep convolutional neural network(DCNN)are constructed to estimate battery state of health(SOH),where the synthetic data is employed to pre-train the models,and transfer learning strategies by using fine-tuning and domain adaptation are utilized to enhance the model adaptability.Finally,field data of 10 EVs exhibiting different SOHs are used to verify the proposed methods.By using the△Q with 100 m V voltage change,the SOH of battery packs can be accurately estimated with an error around 3.2%.
文摘薄板自动化焊时产生的光反射、飞溅、粉尘等噪声使焊缝位置信息被遮挡,从而影响特征点的识别与提取。因此,提出了用连通区域的算法对焊缝的特征进行标记,并改进了连通区域算法用于提取焊缝特征点和获取其位置信息。在图像预处理之前,用感兴趣区域(Region of interest,ROI)方法对激光条纹进行图像分割,可滤除大量弧光、飞溅等噪声;在图像预处理的过程中,采用中值滤波和最大类间方差的二值化算法降低激光条纹附近的干扰噪声,将激光条纹与背景分离,使焊缝特征更清晰、明显;在图像预处理后,用连通区域的方法对激光条纹进行标记,通过改进的算法判断出连通区域的位置,从而识别焊缝特征点,获得焊缝特征点的位置信息。该算法不仅保留了焊缝激光条纹的边缘信息,还能在复杂的工作环境中完成焊缝特征的识别。通过对比薄板的实际间隙宽度和试验计算出的间隙宽度,该算法平均误差在0.067 mm以内,满足工业中的精度要求,适合激光视觉的焊缝跟踪过程。
文摘针对目前空管特情处置过程中案例记录利用不足的问题,提出了空管特情案例利用框架,并重点研究了其中的案例特征提取方法。基于TextRank算法提出了融合空管特情领域知识与数据分析的特情案例特征提取算法(Special Situation Case TextRank,SSC TextRank)。所提方法利用空管特情领域知识构建领域词典,以提升分词效果,依据风险知识及文本数据分析结果,同时结合层次分析法赋权原理对文本中的特征词进行赋权,以优化各词的初始重要度以及词语重要度权重的计算方法。利用某地区空管局提供的2000年—2019年特情案例验证算法的有效性。结果表明:模型较传统自然语言处理中的关键词提取算法准确率提高了约40%,体现了所提方法在特情案例特征提取方面的有效性和优越性。
