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RISS 인기검색어
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- 저자 : Ali Solhmirzaei (검색결과 2 건)
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Road profile estimation using wavelet neural network and 7-DOF vehicle dynamic systems
Ali Solhmirzaei,Shahram Azadi,Reza Kazemi 대한기계학회 2012 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.26 No.10
Road roughness is a broad term that incorporates everything from potholes and cracks to the random deviations that exist in a profile. To build a roughness index, road irregularities need to be measured first. Existing methods of gauging the roughness are based either on visual inspections or using one of a limited number of instrumented vehicles that can take physical measurements of the road irregularities. This paper more specifically focuses on the estimation of a road profile (i.e., along the "wheel track"). This paper proposes a solution to the road profile estimation using a wavelet neural network (WNN) approach. The method incorporates a WNN which is trained using the data obtained from a 7-DOF vehicle dynamic model in the MATLAB Simulink software to approximate road profiles via the accelerations picked up from the vehicle. In this paper, a novel WNN, multi-input and multi-output feed forward wavelet neural network is constructed. In the hidden layer, wavelet basis functions are used as activate function instead of the sigmoid function of feed forward network. The training formulas based on BP algorithm are mathematically derived and a training algorithm is presented. The study investigates the estimation capability of wavelet neural networks through comparison between some estimated and real road profiles in the form of actual road roughness.
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Davood Younesian,Ali Solhmirzaei,Alireza Gachloo 대한기계학회 2009 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.23 No.8
Bogies are one of the multifunctional parts of trains which are extremely subjected to random loads. This type of oscillating and random excitation arises from irregularities of the track including rail surface vertical roughness, rail joints, variance in super-elevation, and also wheel imperfections like wheel flats and unbalancy. Since most of the prementioned sources have random nature, a random based theory should be applied for fatigue life estimation of the bogie frame. Two methods of fatigue life estimation are investigated in this paper. The first approach which is being implemented in time domain is based on the damage accumulation (DA) approach. Using Monte-Carlo simulation algorithm, the rail surface roughness is generated. Finite element (FE) model of the bogie is subjected to the generated random excitation in the first approach and the stress time histories are obtained, and consequently the fatigue life is estimated by using the rain-flow algorithm. In the second approach, the fatigue life is estimated in frequency domain. Power spectral density (PSD) of the stress is obtained by using the FE model of the bogie frame and the fatigue life is estimated using Rayleigh technique in random fatigue theory. A comprehensive parametric study is carried out and effects of different parameters like the train speeds and level of the rail surface vertical roughness on the estimated fatigue life are investigated.
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