Applied Mechanics and Materials Vols. 303-306

Abstract: In order to monitor meteorological parameters such as temperature, humidity, pressure, wind speed and wind direction, a meteorological monitor system of wireless sensor networks was analyzed, which was based on the combination of Zigbee and GPRS communication technology. Short-distance data transmission was achieved through sensors local-area network completed by Zigbee technology, and long-distance data communication was achieved through GPRS technology. Terminal devices distributed in environment collected real-time data gained by sensors, and sent the data to the base station via Zigbee communication technology. The data was received and processed by Mobile base station and base stations choose to use GPRS or Ethernet technology access to Internet and transferred data to the monitoring center. Experimental results show that: temperature accuracy can reach 0.2°C, humidity accuracy can achieve 3.0%RH, and the pressure accuracy is up to 1.5mbar. The Meteorological data is real-time transmitted and the system solves the problem of data loss.

Abstract: The smart reading system based on wireless sensor network technology provides a reliable and economic choice for energy data acquisition of smart grid. The structure, main features and technological solutions of smart meter reading system based on wireless sensor network are analyzed in detail in this paper. Finally, this paper points out that the keys for developing China’s wireless sensor network technology are to self-establish standard system and break through critical technology.

Abstract: The proposed method for Miniature Inertial Measurement Unit (MIMU) system enables the exact measurement of sports parameter even under challenging conditions. With the aid of sports biomechanical parameter, proposed method suppresses the drift. The Hilbert-spectrum responds the sports rhythm law. It will provide aided information for trainer.

Abstract: Power production department is paying more and more attention to the test results of high voltage test instrument and equipment as the power construction is vigorously developing. Currently, the metrological verification of high voltage test instrument is mainly conducted by metrological department such as provincial Electric Power Research Institute (EPRI) of the Power Grid, and standard appliances of measurement are stored in regular laboratory condition and inspection method of them is bottom-to-top periodic inspection by users, which has the disadvantage of inconvenient inspection. The cyclic detection laboratory developed in this paper, which can support comprehensive calibration and on-site detection for the main functions of mainstream high voltage test instruments of the market, is designed aiming at the requirements of cyclic detection for high voltage test instrument of power system. Operating principles and core technologies of the developed detection equipment are detailed introduced in this paper. By using the developed laboratory, performances of test instruments can be effectively assessed, diverse needs of metrological detection for high voltage test instrument of power system can be further met, and operation safety of power system and economic benefit of power industry can be improved as well as on-site metrological detection capacity being strengthened.

Abstract: Industrial equipment has large amount of historical operation data, and the real-time sampling data is massive and multi-dimensions. This paper proposes an equipment condition intelligent monitoring algorithm based on data mining. The algorithm firstly makes adaptive clustering analysis of historical data on the good running condition of equipment, builds the mathematical model of the equipment, and makes prediction of equipment operation state according to this model and the value of real-time state of equipment operation. This algorithm fully considers the actual demand of industrial application, automatically determines the number of clustering class, solves the problem of large cost and low efficiency of traditional clustering algorithm processing massive historical data, and ensures the efficiency of regression forecasting process.

Abstract: A identification method via phase space reconstruction and BP neural network was proposed for identifying three types of voltage disturbances (voltage swells, voltage sag, voltage flicker). In this method, firstly, phase space reconstruction was utilized for describing voltage disturbances; secondly, the mean radius of each cycle of phase space trajectory in accordance with the time-domain was extracted from voltage signals; finally, the identification of voltage disturbances was obtained by BP neural network. The simulation results in Matlab show that the proposed method is capable of high accuracy to identify three types of voltage disturbances, and further validates the efficiency of phase space theory in power quality analysis.

Abstract: According to the problem of petrochemical heat equipment status inspection and fault diagnosis, a method based on edge detection of infrared image segmentation was presented studying the infrared image segmentation based on edge detection and combining Roberts operator into best threshold segmentation method to do simulation of buoyant, medium and heavy damaged equipments. Experimental result shows that edge detection operator of best threshold value has ideal effects to the image edge extraction's target area of thermal infrared equipment.

Abstract: The power battery state of charge (SOC) in electric vehicles is not easy to measure accurately or apply a sensor but the expense is increased. However the variable of SOC is great importance to control of electric vehicles. A power battery model is built by the Partnership for a New Generation of Vehicles (PNGV) model to estimate the state of SOC. In order to make a high accurate estimate for SOC value, an information fusion algorithm based on unscented kalman filter (UKF) is introduced to design an observer. The test results show that the observer based information fusion and UKF are effective and accuracy, so it is may apply it the electric vehicle control and observation.

Abstract: Robust estimation methods can efficiently eliminate or weaken the effects of gross errors on parameter estimation when such errors exist in observations. Remainder reliability can essentially show whether gross errors may be eliminated or weakened by robust estimation. Taking 8 leveling networks and 4 trilateration networks with different remainder reliability adjustments as examples, simulation experiments are used to compare 4 frequently used robust estimation methods. The results indicate that when observations simultaneously contain two gross errors and if the remainder reliability RR20.45, all the four robust estimation methods may effectively eliminate or weaken these effects.

Abstract: For the problem of weak target’s detection under lower signal noise ratio in radar detection system, a key technique is to enhance target echo energy by long time coherent integration. During the integration period, the target may migrate across radar range cells with integration time increasing. Direct coherent integration will lead to echo energy spread in range cells and deteriorate integration effect. A coherent integration algorithm based on a modified keystone transform is proposed in this paper. It can correct range migration caused by the radial velocity of the moving target before coherent integration. Simulation result shows that the proposed algorithm can correct range migration efficiently and improve coherent integration capability.