To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery usin...To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.展开更多
The study of damage in rotating machineries is of fundamental interest in the fields of machine and structure design. A rotating system, supported by bearings and under some dynamic conditions, can generate a variety ...The study of damage in rotating machineries is of fundamental interest in the fields of machine and structure design. A rotating system, supported by bearings and under some dynamic conditions, can generate a variety of problems that are encountered in many different types of rotating machines. One of these problems is the unbalance due to non-homogeneous mass distribution along the shaft. One of the techniques which are widespread today is the identification of parameters and excitation forces that may well followed by monitoring the evolution and change of possible variations of these parameters. Although several methods for the identification of unbalance excitation force are available in the literature, none of them can be considered unrestricted to be applied for all rotating systems. In this study, two methodologies to identify unknown excitations, such as unbalance, have been proposed. This project refers to the analysis of unbalanced forces from displacement parameters and speed by using methods of identification by Fourier series and Legendre polynomials together with the finite element method, state observers in reasons of the problem of absence of signs of rotational displacement, bandpass filter were used to noise suppression of the data collected from the experimental part, Quasi-Newton method to minimize a function in which the bearing stiffness and its damping are unknowns, and also the experimental verification of the methodology, using for this system owned by a rotary mechanical vibrations of the Department of Mechanical Engineering of Faculty of Engineering, campus of llha Solteira.展开更多
The structure characteristics of ID precision ultrathin monocrystalline silicon section cutting machine tool spindle with force monitoring bearings functioning as force measuring sensors were detected with the new H...The structure characteristics of ID precision ultrathin monocrystalline silicon section cutting machine tool spindle with force monitoring bearings functioning as force measuring sensors were detected with the new Hilbert theory based signal wave envelope detection method, presented to replace the conventional hardware device in order to ensure that the signal is measured online with high fidelity. According to the probability of anomalous incidents in the cutting process, a mathematical recognition model has been designed and verified on an STC 22ID machine.展开更多
The rotating machinery,as a typical example of large and complex mechanical systems,is prone to diversified sorts of mechanical faults,especially on their rotating components.Although they can be collected via vibrati...The rotating machinery,as a typical example of large and complex mechanical systems,is prone to diversified sorts of mechanical faults,especially on their rotating components.Although they can be collected via vibration measurements,the critical fault signatures are always masked by overwhelming interfering contents,therefore difficult to be identified.Moreover,owing to the distinguished time-frequency characteristics of the machinery fault signatures,classical dyadic wavelet transforms(DWTs) are not perfect for detecting them in noisy environments.In order to address the deficiencies of DWTs,a pseudo wavelet system(PWS) is proposed based on the filter constructing strategies of wavelet tight frames.The presented PWS is implemented via a specially devised shift-invariant filterbank structure,which generates non-dyadic wavelet subbands as well as dyadic ones.The PWS offers a finer partition of the vibration signal into the frequency-scale plane.In addition,in order to correctly identify the essential transient signatures produced by the faulty mechanical components,a new signal impulsiveness measure,named spatial spectral ensemble kurtosis(SSEK),is put forward.SSEK is used for selecting the optimal analyzing parameters among the decomposed wavelet subbands so that the masked critical fault signatures can be explicitly recognized.The proposed method has been applied to engineering fault diagnosis cases,in which the processing results showed its effectiveness and superiority to some existing methods.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.50875056)
文摘To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.
文摘The study of damage in rotating machineries is of fundamental interest in the fields of machine and structure design. A rotating system, supported by bearings and under some dynamic conditions, can generate a variety of problems that are encountered in many different types of rotating machines. One of these problems is the unbalance due to non-homogeneous mass distribution along the shaft. One of the techniques which are widespread today is the identification of parameters and excitation forces that may well followed by monitoring the evolution and change of possible variations of these parameters. Although several methods for the identification of unbalance excitation force are available in the literature, none of them can be considered unrestricted to be applied for all rotating systems. In this study, two methodologies to identify unknown excitations, such as unbalance, have been proposed. This project refers to the analysis of unbalanced forces from displacement parameters and speed by using methods of identification by Fourier series and Legendre polynomials together with the finite element method, state observers in reasons of the problem of absence of signs of rotational displacement, bandpass filter were used to noise suppression of the data collected from the experimental part, Quasi-Newton method to minimize a function in which the bearing stiffness and its damping are unknowns, and also the experimental verification of the methodology, using for this system owned by a rotary mechanical vibrations of the Department of Mechanical Engineering of Faculty of Engineering, campus of llha Solteira.
文摘The structure characteristics of ID precision ultrathin monocrystalline silicon section cutting machine tool spindle with force monitoring bearings functioning as force measuring sensors were detected with the new Hilbert theory based signal wave envelope detection method, presented to replace the conventional hardware device in order to ensure that the signal is measured online with high fidelity. According to the probability of anomalous incidents in the cutting process, a mathematical recognition model has been designed and verified on an STC 22ID machine.
基金supported financially by the National Natural Science Foundation of China(Grant Nos.51275382 and 11176024)the Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20110201130001)
文摘The rotating machinery,as a typical example of large and complex mechanical systems,is prone to diversified sorts of mechanical faults,especially on their rotating components.Although they can be collected via vibration measurements,the critical fault signatures are always masked by overwhelming interfering contents,therefore difficult to be identified.Moreover,owing to the distinguished time-frequency characteristics of the machinery fault signatures,classical dyadic wavelet transforms(DWTs) are not perfect for detecting them in noisy environments.In order to address the deficiencies of DWTs,a pseudo wavelet system(PWS) is proposed based on the filter constructing strategies of wavelet tight frames.The presented PWS is implemented via a specially devised shift-invariant filterbank structure,which generates non-dyadic wavelet subbands as well as dyadic ones.The PWS offers a finer partition of the vibration signal into the frequency-scale plane.In addition,in order to correctly identify the essential transient signatures produced by the faulty mechanical components,a new signal impulsiveness measure,named spatial spectral ensemble kurtosis(SSEK),is put forward.SSEK is used for selecting the optimal analyzing parameters among the decomposed wavelet subbands so that the masked critical fault signatures can be explicitly recognized.The proposed method has been applied to engineering fault diagnosis cases,in which the processing results showed its effectiveness and superiority to some existing methods.
