Meng Qingfeng

Research on Linear Wireless Sensor Networks Used for Online Monitoring of Rolling Bearing in Freight Train

This paper presents a Wireless Sensor Networks (WSNs) technique for the purpose of on-line monitoring of rolling bearing in freight train. A new technical scheme including the arrangements of sensors, the design of sensor nodes and base station, routing protocols, signal acquirement, processing and transmission is described, and an on-line monitoring system is established. Considering the approximately linear arrangements of cars and the running state of freight train, a linear topology structure of WSNs is adopted and five linear routing protocols are discussed in detail as to obtain the desired minimum energy consumption of WSNs. By analysing the simulation results, an optimal multi-hop routing protocol named sub-section routing protocol according to equal distance is adopted, in which all sensor nodes are divided into different groups according to the equal transmission distance, the optimal transmission distance and number of hops of routing protocol are also studied. We know that the communication consumes significant power in WSNs, so, in order to save the limit power supply of WSNs, the data compression and coding scheme based on lifting integer wavelet and embedded zerotree wavelet (EZW) algorithms is studied to reduce the amounts of data transmitted. The experimental results of rolling bearing have been given at last to verify the effectiveness of data compression algorithm. The on-line monitoring system of rolling bearing in freight train will be applied to actual application in the near future.

Forecasting Conditions of Reactor Coolant Pump Based on Support Vector Machine

In the traditional fault diagnosis technology, classical life and reliability tests require sufficient sample size when diagnose the faults and forecast the future states. However, there is even less sample size for machinery products, especially for major equipment. The Support Vector Machine based on Statistical Learning Theory can solve this problem. In this paper, a forecast model for reactor coolant pump which combines LSSVM (Least Squares Support Vector Machine) and Time Series model is constructed. We studied the impact to forecast accuracy which caused by embedding dimension M, kernel function σ and regularization parameter γ. Meanwhile, the performance of LSSVM is verified by simulation data and field data. Then LSSVM is used to predict vibration signals of reactor coolant pump. As it is certified that the forecast data could match the actual data preferably and has achieved good results in forecasting field data.

Discussion of the Improved Methods for Analyzing a Cantilever Beam Carrying a Tip-Mass under Base Excitation

Two improved analytical methods of calculations for natural frequencies and mode shapes of a uniform cantilever beam carrying a tip-mass under base excitation are presented based on forced vibration theory and the method of separation of variables, respectively. The cantilever model is simplified in detail by replacing the tip-mass with an equivalent inertial force and inertial moment acting at the free end of the cantilever based on D’Alembert’s principle. The concentrated equivalent inertial force and inertial moment are further represented as distributed loads using Dirac Delta Function. In this case, some typical natural frequencies and mode shapes of the cantilever model are calculated by the improved and unimproved analytical methods. The comparing results show that, after improvement, these two methods are in extremely good agreement with each other even the offset distance between the gravity center of the tip-mass and the attachment point is large. As further verification, the transient and steady displacement responses of the cantilever system under a sine base excitation are presented in which two improved methods are separately utilized. Finally, an experimental cantilever system is fabricated and the theoretical displacement responses are validated by the experimental measurements successfully.

Discussion and Experimental Verification on the Relations of Polarization, Deformation and Induced Voltage of Piezoelectric Material

The poling process is described by force analysis for the dipoles inside of the piezoelectric material. The inducing process of electric charges of piezoelectric material under deformation of shrink or stretch along the poling direction is observed. In this case, three sandwiched cantilever models with one or two piezoelectric layers connected in series or parallel are designed and fabricated. Then, under an initially instant excitation, the generation of the induced positive and negative electric charges on the surface of the piezoelectric layers polarized along the thickness direction is analyzed and measured. Finally, experimental measurements verify theoretical results strongly.

A Compression Algorithm in Wireless Sensor Networks of Bearing Monitoring

The energy consumption of wireless sensor networks (WSNs) is always an important problem in the application of wireless sensor networks. This paper proposes a data compression algorithm to reduce amount of data and energy consumption during the data transmission process in the on-line WSNs-based bearing monitoring system. The proposed compression algorithm is based on lifting wavelets, Zerotree coding and Hoffman coding. Among of that, 5/3 lifting wavelets is used for dividing data into different frequency bands to extract signal characteristics. Zerotree coding is applied to calculate the dynamic thresholds to retain the attribute data. The attribute data are then encoded by Hoffman coding to further enhance the compression ratio. In order to validate the algorithm, simulation is carried out by using Matlab. The result of simulation shows that the proposed algorithm is very suitable for the compression of bearing monitoring data. The algorithm has been successfully used in online WSNs-based bearing monitoring system, in which TI DSP TMS320F2812 is used to realize the algorithm.