滚动轴承是机械设备中的常见关键部件,准确预测其剩余使用寿命对机械设备的安全稳定运行至关重要。针对目前轴承寿命预测存在的轴承退化特征不明显、模型泛化能力差以及数据长期依赖关系难以捕捉的问题,提出基于时频域信号优化器(Time-F...滚动轴承是机械设备中的常见关键部件,准确预测其剩余使用寿命对机械设备的安全稳定运行至关重要。针对目前轴承寿命预测存在的轴承退化特征不明显、模型泛化能力差以及数据长期依赖关系难以捕捉的问题,提出基于时频域信号优化器(Time-Frequency domain signal Ratio Optimizer,TFRO)的多重膨胀多核时间卷积网络(Multi inflated Multi kernel Time Convolutional Network,Mi-MkTCN)模型。TFRO优化器为了精准记忆重要信息,在每一个时间节点上,将过去信息和当前信息重组,其中过去信息中的重要的时频域特征经过了有比例的分配。Mi-MkTCN利用多重膨胀确保重要特征不丢失,再利用多核时间卷积网络实现对不同尺度特征的提取。最终的消融对比实验验证了改进方法的有效性,模型的平均绝对误差、均方误差及均方根误差指标分别为0.00145、0.05069和0.12045。实验结果表明,所提方法显著提升了轴承剩余使用寿命的预测精度,为轴承剩余使用寿命预测提供了高精度、高鲁棒性的解决方案。展开更多
With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and ...With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform （STFT） to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adap- tively extracted by using a convolutional neural network （CNN）. The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by tra- ditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.展开更多
Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in t...Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address the first problem, we propose a subsequence-based dilated convolution method (SDC). By using multiple convolutional filters to convolve elements of neighboring subsequences, the method extracts temporal features from a growing receptive field via a growing subsequence rather than a single element. Ultimately, the receptive field of each output element can cover the whole input sequence. To address the second problem, we propose a difference and compensation method (DCM). The method reduces the discrepancies between and within the input sequences by difference operations and then compensates the outputs for the information lost due to difference operations. Based on SDC and DCM, we further construct a temporal subsequence-based convolutional network with difference (TSCND) for time series forecasting. The experimental results show that TSCND can reduce prediction mean squared error by 7.3% and save runtime, compared with state-of-the-art models and vanilla TCN.展开更多
One of the technical bottlenecks of traditional laser-induced breakdown spectroscopy(LIBS) is the difficulty in quantitative detection caused by the matrix effect. To troubleshoot this problem,this paper investigated ...One of the technical bottlenecks of traditional laser-induced breakdown spectroscopy(LIBS) is the difficulty in quantitative detection caused by the matrix effect. To troubleshoot this problem,this paper investigated a combination of time-resolved LIBS and convolutional neural networks(CNNs) to improve K determination in soil. The time-resolved LIBS contained the information of both wavelength and time dimension. The spectra of wavelength dimension showed the characteristic emission lines of elements, and those of time dimension presented the plasma decay trend. The one-dimensional data of LIBS intensity from the emission line at 766.49 nm were extracted and correlated with the K concentration, showing a poor correlation of R_c^2?=?0.0967, which is caused by the matrix effect of heterogeneous soil. For the wavelength dimension, the two-dimensional data of traditional integrated LIBS were extracted and analyzed by an artificial neural network(ANN), showing R_v^2?=?0.6318 and the root mean square error of validation(RMSEV)?=?0.6234. For the time dimension, the two-dimensional data of time-decay LIBS were extracted and analyzed by ANN, showing R_v^2?=?0.7366 and RMSEV?=?0.7855.These higher determination coefficients reveal that both the non-K emission lines of wavelength dimension and the spectral decay of time dimension could assist in quantitative detection of K.However, due to limited calibration samples, the two-dimensional models presented over-fitting.The three-dimensional data of time-resolved LIBS were analyzed by CNNs, which extracted and integrated the information of both the wavelength and time dimension, showing the R_v^2?=?0.9968 and RMSEV?=?0.0785. CNN analysis of time-resolved LIBS is capable of improving the determination of K in soil.展开更多
The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries an...The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.展开更多
文摘滚动轴承是机械设备中的常见关键部件,准确预测其剩余使用寿命对机械设备的安全稳定运行至关重要。针对目前轴承寿命预测存在的轴承退化特征不明显、模型泛化能力差以及数据长期依赖关系难以捕捉的问题,提出基于时频域信号优化器(Time-Frequency domain signal Ratio Optimizer,TFRO)的多重膨胀多核时间卷积网络(Multi inflated Multi kernel Time Convolutional Network,Mi-MkTCN)模型。TFRO优化器为了精准记忆重要信息,在每一个时间节点上,将过去信息和当前信息重组,其中过去信息中的重要的时频域特征经过了有比例的分配。Mi-MkTCN利用多重膨胀确保重要特征不丢失,再利用多核时间卷积网络实现对不同尺度特征的提取。最终的消融对比实验验证了改进方法的有效性,模型的平均绝对误差、均方误差及均方根误差指标分别为0.00145、0.05069和0.12045。实验结果表明,所提方法显著提升了轴承剩余使用寿命的预测精度,为轴承剩余使用寿命预测提供了高精度、高鲁棒性的解决方案。
基金Supported by National Natural Science Foundation of China(Grant No.51405241,51505234,51575283)
文摘With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform （STFT） to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adap- tively extracted by using a convolutional neural network （CNN）. The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by tra- ditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.
基金supported by the National Key Research and Development Program of China(No.2018YFB2101300)the National Natural Science Foundation of China(Grant No.61871186)the Dean’s Fund of Engineering Research Center of Software/Hardware Co-Design Technology and Application,Ministry of Education(East China Normal University).
文摘Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address the first problem, we propose a subsequence-based dilated convolution method (SDC). By using multiple convolutional filters to convolve elements of neighboring subsequences, the method extracts temporal features from a growing receptive field via a growing subsequence rather than a single element. Ultimately, the receptive field of each output element can cover the whole input sequence. To address the second problem, we propose a difference and compensation method (DCM). The method reduces the discrepancies between and within the input sequences by difference operations and then compensates the outputs for the information lost due to difference operations. Based on SDC and DCM, we further construct a temporal subsequence-based convolutional network with difference (TSCND) for time series forecasting. The experimental results show that TSCND can reduce prediction mean squared error by 7.3% and save runtime, compared with state-of-the-art models and vanilla TCN.
基金supported by National Natural Science Foundation of China (Grant No. 61505253)National Key Research and Development Plan of China (Project No. 2016YFD0200601)
文摘One of the technical bottlenecks of traditional laser-induced breakdown spectroscopy(LIBS) is the difficulty in quantitative detection caused by the matrix effect. To troubleshoot this problem,this paper investigated a combination of time-resolved LIBS and convolutional neural networks(CNNs) to improve K determination in soil. The time-resolved LIBS contained the information of both wavelength and time dimension. The spectra of wavelength dimension showed the characteristic emission lines of elements, and those of time dimension presented the plasma decay trend. The one-dimensional data of LIBS intensity from the emission line at 766.49 nm were extracted and correlated with the K concentration, showing a poor correlation of R_c^2?=?0.0967, which is caused by the matrix effect of heterogeneous soil. For the wavelength dimension, the two-dimensional data of traditional integrated LIBS were extracted and analyzed by an artificial neural network(ANN), showing R_v^2?=?0.6318 and the root mean square error of validation(RMSEV)?=?0.6234. For the time dimension, the two-dimensional data of time-decay LIBS were extracted and analyzed by ANN, showing R_v^2?=?0.7366 and RMSEV?=?0.7855.These higher determination coefficients reveal that both the non-K emission lines of wavelength dimension and the spectral decay of time dimension could assist in quantitative detection of K.However, due to limited calibration samples, the two-dimensional models presented over-fitting.The three-dimensional data of time-resolved LIBS were analyzed by CNNs, which extracted and integrated the information of both the wavelength and time dimension, showing the R_v^2?=?0.9968 and RMSEV?=?0.0785. CNN analysis of time-resolved LIBS is capable of improving the determination of K in soil.
基金supported by the China Scholarship Council and the CERNET Innovation Project under grant No.20170111.
文摘The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.
