This paper advances a new approach based on wavelet and wavelet packet transforms in tandem with a fuzzy cluster neural network,abbreviated WPFCNN.Wavelets and wavelet packets decompose a vibration signal into differe...This paper advances a new approach based on wavelet and wavelet packet transforms in tandem with a fuzzy cluster neural network,abbreviated WPFCNN.Wavelets and wavelet packets decompose a vibration signal into different bands at different levels and provides multiresolution or multiscale views of a signal which is stationary or nonstationary. Fuzzy mathematics processes uncertain problems in engineering and converts the attributes extracted by wavelet packets to fuzzy membership degree.To achieve self-organizing classification,the MAXNET neural network is employed.WPFCNN integrates the advantages of wavelet packets and fuzzy cluster with MAXNET.The approach is adopted to process and classify vibration signal of a NH_3 compressor in a petrochemical plant.The results indicate that it is a useful and effective intelligence classification in the field of condition monitoring and fault diagnosis.展开更多
One of the limitations of using OFDM technique is its higher PAPR in the time domain signal. The higher PAPR OFDM signal would cause the fatal degradation of BER performance and undesirable spectrum regrowth in the no...One of the limitations of using OFDM technique is its higher PAPR in the time domain signal. The higher PAPR OFDM signal would cause the fatal degradation of BER performance and undesirable spectrum regrowth in the nonlinear channel. One of the promising PAPR reduction methods for OFDM signal is the Partial Transmit Sequence (PTS) method which can achieve better PAPR performance with reasonable computation complexity. However the PTS method is required to inform the phase coefficients of PTS as the side information to the receiver for the correct demodulation of data information through the data or separate channels. To simplify the transceiver of OFDM system with the PTS method, the phase coefficients of PTS are usually embedded in the data information. However since the phase coefficients of PTS are obtained after the PTS processing only for the data information at each OFDM symbol, it is hard to embed the phase coefficients of PTS in the data information separately without degradation of PAPR performance. To solve this problem, this paper proposes a new PAPR reduction method based on the packet-switched transmission systems in which all the clusters within the certain number of OFDM symbols have the sequential cluster ID numbers embedded in the header of each cluster. The salient features of the proposed method are to reduce the PAPR performance by re-ordering of clusters (ROC) in the frequency domain at the transmitter and to reconstruct the original ordering of clusters by using the cluster ID number demodulated from each cluster at the receiver. This paper also proposes a reduction technique of computation complexity for the proposed ROC method by using the feature of IFFT processing. This paper presents various computer simulation results to verify the effectiveness of the proposed ROC method with the reduction technique of computation complexity.展开更多
Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, d...Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, decomposing cutting torque components from the original signals by wavelet packet decomposition （WPD）; second, extracting wavelet coefficients of different wear states （i.e., slight, normal, or severe wear） with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network （RBFNN）. The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.展开更多
基金This project was supported by National Natural Science Foundation of China
文摘This paper advances a new approach based on wavelet and wavelet packet transforms in tandem with a fuzzy cluster neural network,abbreviated WPFCNN.Wavelets and wavelet packets decompose a vibration signal into different bands at different levels and provides multiresolution or multiscale views of a signal which is stationary or nonstationary. Fuzzy mathematics processes uncertain problems in engineering and converts the attributes extracted by wavelet packets to fuzzy membership degree.To achieve self-organizing classification,the MAXNET neural network is employed.WPFCNN integrates the advantages of wavelet packets and fuzzy cluster with MAXNET.The approach is adopted to process and classify vibration signal of a NH_3 compressor in a petrochemical plant.The results indicate that it is a useful and effective intelligence classification in the field of condition monitoring and fault diagnosis.
文摘One of the limitations of using OFDM technique is its higher PAPR in the time domain signal. The higher PAPR OFDM signal would cause the fatal degradation of BER performance and undesirable spectrum regrowth in the nonlinear channel. One of the promising PAPR reduction methods for OFDM signal is the Partial Transmit Sequence (PTS) method which can achieve better PAPR performance with reasonable computation complexity. However the PTS method is required to inform the phase coefficients of PTS as the side information to the receiver for the correct demodulation of data information through the data or separate channels. To simplify the transceiver of OFDM system with the PTS method, the phase coefficients of PTS are usually embedded in the data information. However since the phase coefficients of PTS are obtained after the PTS processing only for the data information at each OFDM symbol, it is hard to embed the phase coefficients of PTS in the data information separately without degradation of PAPR performance. To solve this problem, this paper proposes a new PAPR reduction method based on the packet-switched transmission systems in which all the clusters within the certain number of OFDM symbols have the sequential cluster ID numbers embedded in the header of each cluster. The salient features of the proposed method are to reduce the PAPR performance by re-ordering of clusters (ROC) in the frequency domain at the transmitter and to reconstruct the original ordering of clusters by using the cluster ID number demodulated from each cluster at the receiver. This paper also proposes a reduction technique of computation complexity for the proposed ROC method by using the feature of IFFT processing. This paper presents various computer simulation results to verify the effectiveness of the proposed ROC method with the reduction technique of computation complexity.
文摘Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, decomposing cutting torque components from the original signals by wavelet packet decomposition （WPD）; second, extracting wavelet coefficients of different wear states （i.e., slight, normal, or severe wear） with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network （RBFNN）. The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.
