Applied Mechanics and Materials Vols. 602-605

Abstract: To improve the ability of fault diagnosis for mechanical equipment, a Radial Basis Function Neural Network (RBFNN) diagnosis method based on Unscented Kalman Filter (UKF) algorithm is proposed. In the algorithm, at first, UKF algorithm is used to estimate the parameters of RBFNN, and then the proposed method is introduced into the fault diagnosis of mechanical equipment. The simulation indicates that the established model has a good diagnosis performance for mechanical fault diagnosis.

Abstract: A novel method of vehicle type recognition based on template matching is proposed to improve the real-time performance of the vehicle type recognition in real traffic scenes. GRM is applied and the template is normalized for realizing parallel template matching. Then, we realize the rapid vehicle type recognition through lookup tables by the hierarchical index of vehicle type template with k-means clustering and size normalization processing. The results show that the algorithm can recognize vehicle type in traffic scenes efficiently.

Abstract: In this study, the theory of factor analysis is used to establish a model for measuring and assessing corporate governance and identify major factors affecting corporate governance. The findings of this study can provide theoretical basis for comprehensive evaluation of corporate governance.

Abstract: A selective method was developed for the determination of aminophylline by Spectrophotometric method. The method was simple with high sensitivity and selectivity.It was shown that recovery of the method by standard addition method was respectively valued 99.21% for aminophylline.

Abstract: The star identification is a key technology of Near infrared starry-sky image navigation. We can calculate the position of the imaging platform after the identification of at least three stars.In this paper, we designed a method of star matching and identifying based on the two characteristics of magnitude differ and star point pairs. It can bring high accurate of the identification.

Abstract: Analysis the common gear fault signal, using continuous wavelet transform well time-frequency characteristics and failure mechanism gear features, combined with quasi-periodic signal rotary mechanical characteristics of the continuous wavelet transform fault signal. This method can reduce the noise source and other incentives interference, remove the specific needs of the signal to improve signal stripping effect, the use of a certain type of gear failures this step, to get a clear diagnosis results show that this method has proven a strong application space.

Abstract: Buses running state based on the technology of "4G" wireless monitoring system has bus dynamic information query, vehicle dynamic tracking, vehicle scheduling, vehicle classification management four big functions. The system has nine subsystems respectively complete vehicle GPS positioning, operations management, IC card charging management, large screen display control center, vehicle operation coordination, GIS data query and selection, mobile communications, exception handling, information service, etc. The system applied to public transport vehicle scheduling management, the results show that can alleviate the urban public traffic congestion, improve line of vehicle safety, improve the efficiency of the vehicle running, reduce the air pollution in cities.

Abstract: In this passage we propose a computationally efficient adaptive filtering algorithm for sparse system identification.The algorithm is based on dichotomous coordinate descent iterations, reweighting iterations,iterative support detection.In order to reduce the complexity we try to discuss in the support.we suppose the support is partial,and partly erroneous.Then we can use the iterative support detection to solve the problem.Numerical examples show that the proposed method achieves an identification performance better than that of advanced sparse adaptive filters (l1-RLS,l0-RLS) and its performance is close to the oracle performance.

Abstract: The standard least mean square algorithm does not consider the sparsity of the impulse response,and the performs of the ZA-LMS algorithm deteriorates ,as the degree of system sparsity reduces or non-sparse . Concerning this issue ,the ZA-LMS algorithm is studied and modified in this paper to improve the performance of sparse system identification .The improved algorithm by modify the zero attraction term, which attracts the coefficients only in a certain range (the “inactive” taps), thus have a good performance when the sparsity decreases. The simulations demonstrate that the proposed algorithm significantly outperforms then the ZA-LMS with variable sparisity.

Abstract: The detection precision of fault diagnosis based on frequency spectral analysis of stator current is easily restricted by noise jamming and frequency resolution. A fault diagnosis method for induction motor based on linear mixing model is proposed to resolve this problem. The fault characteristic signals are separated from the motor stator current by Fast-ICA algorithm and its amplitude is calculated according to the estimated mixing matrix. The fault diagnosis is achieved by difference of the amplitude on the normal state and the fault state of the motor. In this paper, the fault diagnosis of the broken rotor bars faults is used as an example to explain the conclusion as mentioned. Experiment result shows that the broken-rotor-bar fault can be diagnosed by the algorithm with better effect on the condition of short data block.