In this paper, a problem formulation for determining optimal node location for Base Stations (BSs... more In this paper, a problem formulation for determining optimal node location for Base Stations (BSs) and Relay Stations (RSs) in relay-based 802.16 networks is developed. The formulation results in an Integer Programming problem. A small modification to the original model is also considered in which the state space is reduced by limiting which nodes can be associated with which. Standard branch and bound techniques are used to solve the problem. The key findings of the paper are that standard techniques can be used to find solutions to problems of small metropolitan scale or for areas within a larger city.
... Authors, Yang Yu, Motorola Labs. Loren J. Rittle, Motorola Labs. Vartika Bhandari, University... more ... Authors, Yang Yu, Motorola Labs. Loren J. Rittle, Motorola Labs. Vartika Bhandari, University of Illinois at Urbana-Champaign. Jason B. LeBrun, University of California, Davis. ... Yang Yu: colleagues. Loren J. Rittle: colleagues. Vartika Bhandari: colleagues. Jason B. LeBrun: colleagues ...
The main purpose of this paper is to propose a new fault feature extraction approach based on emp... more The main purpose of this paper is to propose a new fault feature extraction approach based on empirical mode decomposition (EMD) method and autoregressive (AR) model for roller bearings. AR model is an effective approach to extract the fault feature of the vibration signals and the fault pattern can be identified directly by the extracted fault features without establishing the mathematical model and studying the fault mechanism of the system. However, AR model can only be applied to stationary signals, while the fault vibration signals of a roller bearing are non-stationary. Aiming at this problem, in this paper, the EMD method is used as a pretreatment to decompose the non-stationary vibration signal of a roller bearing into a number of intrinsic mode function (IMF) components which are stationary, then the AR model of each IMF component can be established. The AR parameters and the remnant's variance of the AR models of each IMF components are regarded as the feature vectors. The Mahalanobis distance criterion function is used to identify the condition and fault pattern of a roller bearing. Experimental analysis results show that the roller bearing fault features can be extracted by the proposed approach effectively. r
Empirical mode decomposition (EMD) is a self-adaptive signal-processing method, which has been ap... more Empirical mode decomposition (EMD) is a self-adaptive signal-processing method, which has been applied in nonstationary signal-processing successfully. Aiming at the problem of intrinsic mode function (IMF) criterion in the EMD method, the energy difference tracking method is proposed in this paper according to the integrity and orthogonality of the IMFs and used to define the IMF in the sifting process. By analyzing the simulated and actual signals, it is confirmed that the IMFs defined by the energy difference tracking method meet the orthogonality condition and reflect the intrinsic and real information of the analysed signal. By comparing the energy difference tracking method with the Cauchy-type convergence criterion, it is demonstrated that the IMFs obtained by the energy difference tracking method can reflect the intrinsic information included in the signal more clearly and their index of orthogonal (IO) is smaller. r
According to the non-stationary characteristics of roller bearing fault vibration signals, a roll... more According to the non-stationary characteristics of roller bearing fault vibration signals, a roller bearing fault diagnosis method based on empirical mode decomposition (EMD) energy entropy is put forward in this paper. Firstly, original acceleration vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs), then the concept of EMD energy entropy is proposed. The analysis results from EMD energy entropy of different vibration signals show that the energy of vibration signal will change in different frequency bands when bearing fault occurs. Therefore, to identify roller bearing fault patterns, energy feature extracted from a number of IMFs that contained the most dominant fault information could serve as input vectors of artificial neural network. The analysis results from roller bearing signals with inner-race and out-race faults show that the diagnosis approach based on neural network by using EMD to extract the energy of different frequency bands as features can identify roller bearing fault patterns accurately and effectively and is superior to that based on wavelet packet decomposition and reconstruction. r
In this paper, a problem formulation for determining optimal node location for Base Stations (BSs... more In this paper, a problem formulation for determining optimal node location for Base Stations (BSs) and Relay Stations (RSs) in relay-based 802.16 networks is developed. The formulation results in an Integer Programming problem. A small modification to the original model is also considered in which the state space is reduced by limiting which nodes can be associated with which. Standard branch and bound techniques are used to solve the problem. The key findings of the paper are that standard techniques can be used to find solutions to problems of small metropolitan scale or for areas within a larger city.
... Authors, Yang Yu, Motorola Labs. Loren J. Rittle, Motorola Labs. Vartika Bhandari, University... more ... Authors, Yang Yu, Motorola Labs. Loren J. Rittle, Motorola Labs. Vartika Bhandari, University of Illinois at Urbana-Champaign. Jason B. LeBrun, University of California, Davis. ... Yang Yu: colleagues. Loren J. Rittle: colleagues. Vartika Bhandari: colleagues. Jason B. LeBrun: colleagues ...
The main purpose of this paper is to propose a new fault feature extraction approach based on emp... more The main purpose of this paper is to propose a new fault feature extraction approach based on empirical mode decomposition (EMD) method and autoregressive (AR) model for roller bearings. AR model is an effective approach to extract the fault feature of the vibration signals and the fault pattern can be identified directly by the extracted fault features without establishing the mathematical model and studying the fault mechanism of the system. However, AR model can only be applied to stationary signals, while the fault vibration signals of a roller bearing are non-stationary. Aiming at this problem, in this paper, the EMD method is used as a pretreatment to decompose the non-stationary vibration signal of a roller bearing into a number of intrinsic mode function (IMF) components which are stationary, then the AR model of each IMF component can be established. The AR parameters and the remnant's variance of the AR models of each IMF components are regarded as the feature vectors. The Mahalanobis distance criterion function is used to identify the condition and fault pattern of a roller bearing. Experimental analysis results show that the roller bearing fault features can be extracted by the proposed approach effectively. r
Empirical mode decomposition (EMD) is a self-adaptive signal-processing method, which has been ap... more Empirical mode decomposition (EMD) is a self-adaptive signal-processing method, which has been applied in nonstationary signal-processing successfully. Aiming at the problem of intrinsic mode function (IMF) criterion in the EMD method, the energy difference tracking method is proposed in this paper according to the integrity and orthogonality of the IMFs and used to define the IMF in the sifting process. By analyzing the simulated and actual signals, it is confirmed that the IMFs defined by the energy difference tracking method meet the orthogonality condition and reflect the intrinsic and real information of the analysed signal. By comparing the energy difference tracking method with the Cauchy-type convergence criterion, it is demonstrated that the IMFs obtained by the energy difference tracking method can reflect the intrinsic information included in the signal more clearly and their index of orthogonal (IO) is smaller. r
According to the non-stationary characteristics of roller bearing fault vibration signals, a roll... more According to the non-stationary characteristics of roller bearing fault vibration signals, a roller bearing fault diagnosis method based on empirical mode decomposition (EMD) energy entropy is put forward in this paper. Firstly, original acceleration vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs), then the concept of EMD energy entropy is proposed. The analysis results from EMD energy entropy of different vibration signals show that the energy of vibration signal will change in different frequency bands when bearing fault occurs. Therefore, to identify roller bearing fault patterns, energy feature extracted from a number of IMFs that contained the most dominant fault information could serve as input vectors of artificial neural network. The analysis results from roller bearing signals with inner-race and out-race faults show that the diagnosis approach based on neural network by using EMD to extract the energy of different frequency bands as features can identify roller bearing fault patterns accurately and effectively and is superior to that based on wavelet packet decomposition and reconstruction. r
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