A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless ...A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel 0Cr17Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal.展开更多
A new algorithm was developed to correctly identify fault conditions and accurately monitor fault development in a mine hoist. The new method is based on the Wavelet Packet Transform (WPT) and kernel PCA (Kernel Princ...A new algorithm was developed to correctly identify fault conditions and accurately monitor fault development in a mine hoist. The new method is based on the Wavelet Packet Transform (WPT) and kernel PCA (Kernel Principal Compo- nent Analysis, KPCA). For non-linear monitoring systems the key to fault detection is the extracting of main features. The wavelet packet transform is a novel technique of signal processing that possesses excellent characteristics of time-frequency localization. It is suitable for analysing time-varying or transient signals. KPCA maps the original input features into a higher dimension feature space through a non-linear mapping. The principal components are then found in the higher dimen- sion feature space. The KPCA transformation was applied to extracting the main nonlinear features from experimental fault feature data after wavelet packet transformation. The results show that the proposed method affords credible fault detection and identification.展开更多
Selected milestones in the development and use of electrical tomography in powder conveying, slurry processing and multi-phase flow are highlighted. The ability to map concentration in opaque mixtures under process-re...Selected milestones in the development and use of electrical tomography in powder conveying, slurry processing and multi-phase flow are highlighted. The ability to map concentration in opaque mixtures under process-realistic conditions was a major innovation for the method and has had far reaching implications. Subsequent developments have enabled velocity information to be abstracted resulting in the ability to measure component flux and motion.展开更多
文摘A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel 0Cr17Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal.
基金Projects 50674086 supported by the National Natural Science Foundation of ChinaBS2006002 by the Society Development Science and Technology Planof Jiangsu Province20060290508 by the Doctoral Foundation of Ministry of Education of China
文摘A new algorithm was developed to correctly identify fault conditions and accurately monitor fault development in a mine hoist. The new method is based on the Wavelet Packet Transform (WPT) and kernel PCA (Kernel Principal Compo- nent Analysis, KPCA). For non-linear monitoring systems the key to fault detection is the extracting of main features. The wavelet packet transform is a novel technique of signal processing that possesses excellent characteristics of time-frequency localization. It is suitable for analysing time-varying or transient signals. KPCA maps the original input features into a higher dimension feature space through a non-linear mapping. The principal components are then found in the higher dimen- sion feature space. The KPCA transformation was applied to extracting the main nonlinear features from experimental fault feature data after wavelet packet transformation. The results show that the proposed method affords credible fault detection and identification.
基金support of co-workers in the tomography groups at University of Leeds and the University of Manchester with funding to enable us to develop the frontiers of tomography provided by EPSRC (EP/D031257/1)
文摘Selected milestones in the development and use of electrical tomography in powder conveying, slurry processing and multi-phase flow are highlighted. The ability to map concentration in opaque mixtures under process-realistic conditions was a major innovation for the method and has had far reaching implications. Subsequent developments have enabled velocity information to be abstracted resulting in the ability to measure component flux and motion.
