Cell voltage is a widely used signal that can be measured online from an industrial aluminum electrolysis cell.A variety of parameters for the analysis and control of industrial cells are calculated using the cell vol...Cell voltage is a widely used signal that can be measured online from an industrial aluminum electrolysis cell.A variety of parameters for the analysis and control of industrial cells are calculated using the cell voltage.In this paper,the frequency segmentation of cell voltage is used as the basis for designing filters to obtain these parameters.Based on the qualitative analysis of the cell voltage,the sub-band instantaneous energy spectrum(SIEP)is first proposed,which is then used to quantitatively represent the characteristics of the designated frequency bands of the cell voltage under various cell conditions.Ultimately,a cell condition-sensitive frequency segmentation method is given.The proposed frequency segmentation method divides the effective frequency band into the[0,0.001]Hz band of lowfrequency signals and the[0.001,0.050]Hz band of low-frequency noise,and subdivides the lowfrequency noise into the[0.001,0.010]Hz band of metal pad abnormal rolling and the[0.01,0.05]Hz band of sub-low-frequency noise.Compared with the instantaneous energy spectrum based on empirical mode decomposition,the SIEP more finely represents the law of energy change with time in any designated frequency band within the effective frequency band of the cell voltage.The proposed frequency segmentation method is more sensitive to cell condition changes and can obtain more elaborate details of online cell condition information,thus providing a more reliable and accurate online basis for cell condition monitoring and control decisions.展开更多
Incipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods.To address this challenge,this paper proposes an effective local spectr...Incipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods.To address this challenge,this paper proposes an effective local spectrum enhancement‐based diagnostic method that can identify weak fault frequencies in the original complicated raw signals.For this purpose,a traversal frequency band segmentation technique is first proposed for dividing the raw signal into a series of subfrequency bands.Then,the proposed synthetic quantitative index is constructed for selecting the most informative local frequency band(ILFB)containing fault features from the divided subfrequency bands.Furthermore,an improved grasshopper optimization algorithmbased stochastic resonance(SR)system is developed for enhancing weak fault features contained in the selected most ILFB with less computation cost.Finally,the enhanced weak fault frequencies are extracted from the output of the SR system using a common spectrum analysis.Two experiments on a laboratory planetary gearbox and an open bearing data set are used to verify the effectuality of the proposed method.The diagnostic results demonstrate that the proposed method can identify incipient faults of gears and bearings in an effective and accurate manner.Furthermore,the advantages of the proposed method are highlighted by comparison with other methods.展开更多
The chaotic motion characteristics are expounded by taking the Duffing equation system as an example.The frequency band segmentation ability and the frequency resolution of the orthogonal multiresolution analysis and ...The chaotic motion characteristics are expounded by taking the Duffing equation system as an example.The frequency band segmentation ability and the frequency resolution of the orthogonal multiresolution analysis and the orthogonal wavelet packet analysis are compared.A new orthogonal wavelet packet analysis-based chaos recognition method for chaotic motion characteristics is put forward.The chaotic,random,and periodic motions are identified effectively by use of the subfrequency band energy distribution in the signal spectrum.The characteristic frequency of chaotic motion is thus extracted.展开更多
基金This work was supported by the Program of the National Natural Science Foundation of China(61988101,61773405,and 61751312).
文摘Cell voltage is a widely used signal that can be measured online from an industrial aluminum electrolysis cell.A variety of parameters for the analysis and control of industrial cells are calculated using the cell voltage.In this paper,the frequency segmentation of cell voltage is used as the basis for designing filters to obtain these parameters.Based on the qualitative analysis of the cell voltage,the sub-band instantaneous energy spectrum(SIEP)is first proposed,which is then used to quantitatively represent the characteristics of the designated frequency bands of the cell voltage under various cell conditions.Ultimately,a cell condition-sensitive frequency segmentation method is given.The proposed frequency segmentation method divides the effective frequency band into the[0,0.001]Hz band of lowfrequency signals and the[0.001,0.050]Hz band of low-frequency noise,and subdivides the lowfrequency noise into the[0.001,0.010]Hz band of metal pad abnormal rolling and the[0.01,0.05]Hz band of sub-low-frequency noise.Compared with the instantaneous energy spectrum based on empirical mode decomposition,the SIEP more finely represents the law of energy change with time in any designated frequency band within the effective frequency band of the cell voltage.The proposed frequency segmentation method is more sensitive to cell condition changes and can obtain more elaborate details of online cell condition information,thus providing a more reliable and accurate online basis for cell condition monitoring and control decisions.
基金sponsored by the National Natural Science Foundation of China(No.51875105).
文摘Incipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods.To address this challenge,this paper proposes an effective local spectrum enhancement‐based diagnostic method that can identify weak fault frequencies in the original complicated raw signals.For this purpose,a traversal frequency band segmentation technique is first proposed for dividing the raw signal into a series of subfrequency bands.Then,the proposed synthetic quantitative index is constructed for selecting the most informative local frequency band(ILFB)containing fault features from the divided subfrequency bands.Furthermore,an improved grasshopper optimization algorithmbased stochastic resonance(SR)system is developed for enhancing weak fault features contained in the selected most ILFB with less computation cost.Finally,the enhanced weak fault frequencies are extracted from the output of the SR system using a common spectrum analysis.Two experiments on a laboratory planetary gearbox and an open bearing data set are used to verify the effectuality of the proposed method.The diagnostic results demonstrate that the proposed method can identify incipient faults of gears and bearings in an effective and accurate manner.Furthermore,the advantages of the proposed method are highlighted by comparison with other methods.
基金study was supported by the 7th Younger Teacher Fund of Fok Ying Tung Education Foundation (No.71061).
文摘The chaotic motion characteristics are expounded by taking the Duffing equation system as an example.The frequency band segmentation ability and the frequency resolution of the orthogonal multiresolution analysis and the orthogonal wavelet packet analysis are compared.A new orthogonal wavelet packet analysis-based chaos recognition method for chaotic motion characteristics is put forward.The chaotic,random,and periodic motions are identified effectively by use of the subfrequency band energy distribution in the signal spectrum.The characteristic frequency of chaotic motion is thus extracted.
