Applied Mechanics and Materials Vols. 687-691

Abstract: A mechanical system shows different dynamical features under normal running conditions and faulty. The fractal dimension is a probability measurement of a dynamical system strange attractor. It is very sensitive to the inhomogeneity of a stranger attractor. Therefore it is often used feature value for indicating machine fault. The correlation dimension is proposed to be used in detecting the bearing fault of a power plant blower. Analysis result demonstrates the correlation dimension from measured bearing vibration signals is able to identify different running conditions of the blower. The correlation dimension values of the normal condition and faulty condition can be classified clearly.

Abstract: The simulation optimization platform for fault grey prediction of diesel engine is designed by MATLAB GUI, to ensure a reliable operating environment. The GM (1,1) model and improved models are presented in the platform to solve the optimization problem of prediction precision. The platform sets input, output, simulation calculation and post processing functions as one and is compiled to the executable program at last . As is proved by simulation examples, the platform simplifies the modeling process and improves the efficiency of the simulation, It is confirmed that the simulation platform for fault grey prediction of diesel engine is practical.

Abstract: High voltage circuit breaker is one of the most significant devices and its health status will impact security of the power system. In this paper, the method of high voltage circuit breakers mechanical fault diagnosis is discussed, fault diagnosis method based on vibration signal is proposed. Firstly, the collected acoustic signals are proceed by blind source separation processing through fast independent component analysis. Then, the acoustic signal feature vector is extracted by improved ensemble empirical mode decomposition (EEMD) and the residual signal is filtered by fractional differential. Finally, the feature vectors are input into support vector machine (SVM) for fault diagnosis. Experiment shows that the proposed method can get more precise fault classification to high voltage circuit breakers.

Abstract: GM(1,1) model can be used in the analysis of a little amount samples of experiments. Shortcommings of the traditional GM(1,1) model were aimed at, and the historical data by weakening buffer operator was corrected. Then the buffer operator and modified prediction GM(1,1) model is carried out on the trial data of certain two gears fatigue life. The examples shows that Buffer Operator and Modified GM(1,1) model has a higher accuracy than the traditional GM(1,1) model.

Abstract: In order to get rid of noise from the angular displacement of the crank rocker mechanism, the wavelet transform method is introduced. After the noise corresponds to the high frequency band of wavelet domain of the signal and the signal corresponds to the low frequency band of wavelet domain of the signal, the signal is decomposed into four layers, and the high frequency brand is set zero. The test results show that this method was most ideal for the de-noising effect on displacement signals, which is able to not only retain valid signals but to effectively remove the noise.

Abstract: The present study analyzed data collection, coding and transmission modes involved in building energy consumption monitoring platform. By analyzing and designing the functions, data collection and management of the system, data analysis and display, data monitoring and remote control, monitoring and prediction and analysis of pipe network, it proposed a design framework of the building energy consumption monitoring platform; and made a verification in the campus building energy consumption monitoring platform, which realized the real-time data collection of energy consumption monitoring and the dynamic monitoring of building energy consumption, and improved the decision level of intelligent building management.

Abstract: Research on multi-target tracking wireless sensor networks, the main problem is how to improve tracking accuracy and reduce energy consumption. Proposed use of forecasting methods to predict the target state, the selection of target detection range forecast based on the relationship between states and between sensor nodes deployed. And in accordance with the selected detection range, to wake up and form a cluster to track the target. In multi-target tracking will use to adjust the detection range, time to time to separate the conflict node of conflict, in order to achieve a successful track multiple targets. Simulation results show that the proposed method can indeed improve the chances of success of the track.

Abstract: Based on cloud computing distributed network measurement system compared to traditional measurement infrastructure, the use of cloud computing platform measurement data stored in massive large virtual resource pool to ensure the reliability of data storage and scalability, re-use cloud computing platform parallel processing mechanism, the mass measurement data for fast, concurrent analytical processing and data mining. Measuring probe supports a variety of different measurement algorithms deployed to support a variety of data acquisition formats, in the measurement method provides a congestion response policies and load balancing strategies.

Abstract: In order to provide precise guidance for the landing of carrier-based aircrafts, the phased array antenna and the principle of time reference scanning beam are used in the shipborne MLS. The phased array antenna is installed under the flight deck of the ship stern, so the vibration could change the relative position of antenna elements, which will cause the navigation error. In this paper, through the establishment of antenna array pattern and phased array antenna vibration error model, an error calibration algorithm is proposed after the simulation of phased array antenna’s random vibration effects on the guiding performance of shipborne MLS in three axes. Computer simulations show that the calibration algorithm could improve the guiding performance of shipborne MLS.

Abstract: To investigate the brain default mode network (DMN) of healthy young people, a novel hierarchical clustering method was proposed to detect similarities of low-frequency fluctuations between any two out of 160 regions of interest (ROI) all over the brain. Feature of these ROIs were firstextractedand analyzed the feature using hierarchical clustering approach.Combining with the strongest connected network node identified by network centric criterion, the default mode network which presented the strongest connectivity in resting state was then determined. The results demonstrated that cingulate had the highest value of average degree, making it the most suspectof where the centrality indices of DMN lay.The comparative results between nodes included by DMN returned by our method and these given by Dosenbach’s research showed quite high coincidence rates,indicating the proposed method of combining complex network theory and hierarchical clustering analysis feasible method to parse brain regions.