基于声信号三维谱分析的转子故障特征提取的实验研究

对转子的一些典型故障（不平衡、松动、碰磨、不对中以及突加不平衡）进行试验模拟,对故障发生过程中的声信号采用声望话筒和十六通道索尼磁带记录仪进行采集的基础上,分析转子故障声信号的三维谱特性,提取和总结出由故障造成的声信号突变和对应的声谱特征。分析结果表明,采用声信号对转子的一些故障进行诊断是可行的。     

Single event transient characterization of SiGe HBT by SPA experiment and 3-D process simulation

Science China Technological Sciences - The single-photon absorption induced single event transient in the silicon-germanium heterojunction bipolar transistor is investigated. The laser wavelength...     

A comparison of triazole cross‐linked polymers based on poly‐AMMO and GAP: Mechanical properties and curing kinetics

Triazole cross‐linked polymers based on poly(3‐azidomethyl‐3‐methyl oxetane) (poly‐AMMO) and glycidyl azide polymer (GAP) were prepared using bis‐propargyl‐1,4‐cyclohexyl‐dicarboxylate (BPHA) as curing agent, respectively. Swelling tests demonstrated that cross‐linking densities of the resulted polymers both increased with the increase of BPHA. Triazole cross‐linked polymers based on poly‐AMMO showed superior tensile strength and elongation at break than those of GAP at comparable stoichiometry. The curing kinetics was also investigated by FTIR, and GAP exhibited faster reaction rate when reacted with BPHA than that of poly‐AMMO. In addition, with the increase of cross‐linking density, the glass transition temperature (T_g) of as‐prepared polymers significantly increased, and poly‐AMMO‐based polymers showed stronger T_g‐raising effect than GAP‐based polymers. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43341.     

Stochastic speed prediction for connected vehicles using improved bayesian networks with back propagation

Advanced vehicular control technologies rely on accurate speed prediction to make ecological and safe decisions. This paper proposes a novel stochastic speed prediction method for connected vehicles by incorporating a Bayesian network (BN) and a Back Propagation (BP) neural network. A BN model is first designed for predicting the stochastic vehicular speed in a priori. To improve the accuracy of the BN-based speed prediction, a BP-based predicted speed error compensation module is constructed by formulating a mapping between the predicted speed and whose corresponding prediction error. In the end, a filtering algorithm is developed to smoothen the compensated stochastic vehicular speed. To validate the workings of the proposed approaches in experiments, two typical scenarios are considered: one predecessor vehicle in a double-vehicle scenario and two predecessor vehicles in a multi-vehicle scenario. Simulation results under the considered scenarios demonstrate that the proposed BN-BP fusion method outperforms the BN-based method with respect to the root mean square error, standardized residuals, R-squared, and the online prediction time of proposed fusion prediction can satisfy a real-time application requirement. The main highlighted contributions of this article are threefold: (1) We put forward an improved BN method, which is combined with a BP neural network, to construct a stochastic vehicular speed prediction method under connected driving; (2) different from existing methods, a unique interconnected framework that consists of a stochastic vehicular speed prediction module, a compensation module, and a speed smoothing module is proposed; (3) extensive simulation studies based on a set of evaluation metrics are illustrated to reveal the advantages and merits of the proposed approaches.

     

Estimation on structural responses using multi scale measurements

Different measurands from the different types of sensors can obtain different information regarding the structural behavior in a real structural health monitoring system.To enrich information and estimate the structural responses based on much more known information,the estimation on structural responses using multi scale measurements from multi-type sensors is proposed in this paper.Pattern identification is constructed with the pattern library given by strain measurements and deformation measurements.Considering the uncertainty of the measurements as well as to enhance the robustness of the proposed algorithm,more than one best pattern is selected to synthesize the finally estimated stress responses.To validate the capacity of the proposed acquisition method using multi scale measurements,finite element model analysis is conducted to estimate the structural stress response in Shenzhen Bay Stadium as an example.The performance of the pattern identifications,constructed by two kinds of pattern libraries captured by sole strain measurement,and multi scale measurements which are constructed by both kinds of strain measurements and deformation measurements,respectively,are compared in this paper to observe measurements constructed from strain measurements and deformation measurements outperformed others.Errors analysis for a series of parametric studies in which noise at different levels has also included in the measurements are further carried out,and robustness of the proposed information acquisition scheme under noisy measurement is demonstrated.     

基于ARM嵌入式平台的作物抗倒伏性检测系统

针对现有作物茎秆在推力作用下倾斜角度和对应推力的测量装置需要人工读数,测量误差大,并且后期数据处理不方便等问题,以茎秆受力简化模型为研究对象,利用力学传感器和倾角传感器,基于ARM嵌入式平台设计了作物抗倒伏性检测系统,可以实时、准确的测量茎杆在推力作用下倾斜角度和对应推力大小。经测试表明,系统有效的提高了测量田间作物茎秆拉弯强度的工作效率,为茎秆抗倒伏性研究提供了科学的数据依据和决策支持。     

扩频通信系统干扰及其仿真技术

扩展频谱技术有多种基本实现方式,本文主要介绍的是直接序列扩频技术,特别针对二进制的PSK调制解调技术,直接序列扩频系统的抗干扰能力分析与直接序列扩频系统的同步方法,并进行了相关仿真分析。     

Evolved clustering analysis of 300 MW boiler furnace pressure sequence based on entropy characterization

The furnace process is very important in boiler operation,and furnace pressure works as an important parameter in furnace process.Therefore,there is a need to analyze and monitor the pressure signal in furnace.However,little work has been conducted on the relationship with the pressure sequence and boiler’s load under different working conditions.Since pressure sequence contains complex information,it demands feature extraction methods from multi-aspect consideration.In this paper,fuzzy c-means analysis method based on weighted validity index(VFCM)has been proposed for the working condition classification based on feature extraction.To deal with the fluctuating and time-varying pressure sequence,feature extraction is taken as nonlinear analysis based on entropy theory.Three kinds of entropy values,extracted from pressure sequence in time-frequency domain,are studied as the clustering objects for work condition classification.Weighted validity index,taking the close and separation degree into consideration,is calculated on the base of Silhouette index and Krzanowski-Lai index to obtain the optimal clustering number.Each time FCM runs,the weighted validity index evaluates the clustering result and the optimal clustering number will be obtained when it reaches the maximum value.Four datasets from UCI Machine Learning Repository are presented to certify the effectiveness in VFCM.Pressure sequences got from a 300 MW boiler are then taken for case study.The result of the pressure sequence case study with an error rate of 0.5332%shows the valuable information on boiler’s load and pressure sequence in furnace.The relationship between boiler’s load and entropy values extracted from pressure sequence is proposed.Moreover,the method can be considered to be a reference method for data mining in other fluctuating and time-varying sequences.     

开发区将向何处去

本文从开发区的作用、开发区以往的优势、开发区面临的问题、开发区如何再创优势等四个方面对开发区的未来发展趋势进行了论述。