Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

塔顶放大器在工程设计中的应用探讨

塔顶放大器在移动通信领域有较为广泛的应用，它通过降低(改善)等效接收噪声系数，从而提高上行接收灵敏度。本文将简要介绍塔顶放大器的应用和提高接收机灵敏度的计算。     

基于DSP+FPGA的扩频接收机快捕技术

提出一种在接收信号具有显著的多普勒频移的不确定性条件下,采用全数字的、适宜于扩频码和载波快速同步的新型扩频接收机结构。该接收机能在极短时间内建立快速同步,有效地实现实时突发通信。     

超宽带无线通信技术概述

主要介绍了超宽带(UWB)无线通信技术的起源、概念及其基本工作原理，包括信号产生、系统构成、天线、接收机、调制方式等，阐述了UWB中的关键技术及性能特点，对UWB的局限性作了简要的介绍。     

信号情报接收机的发展现状及趋势

信号情报系统主要由天线，接收机和处理器三大部分组成，接收机是信号情报系统的一个重要组成部分。本文主要对几种信号情报接收机的原理和现状作了介绍，并对其发展方向提出看法。     

Synchronous receiver initiated MAC protocol for long-lived sensor networks

In this paper, we discuss the design of a new Medium Access Control (MAC) protocol, SRI-MAC (Synchronous Receiver Initiated MAC), for wireless sensor networks whose goal is to extend the lifetime of the network by avoiding major energy waste causes, such as collisions, overhearing and idle listening.     

基于TMS320VC5402的RS_485通信设计

完成了基于TMS320VC5402 DSP的R5_485通信的硬件设计和软件编程.基于美国TI公司TMS320VC5402 DSP芯片的多通道缓冲口(McBSP)进行适当的硬件扩展,以SPI方式直接和MAXIM公司的MAX3100通用异步串行收发器接口,然后经Linear Technology公司的LTC 1480超低...     

金属零件的快速数字化制造

叙述了数字化技术在改造传统铸造工艺中的作用，详细论述了金属零件数字化铸造技术的内涵及其优势。研究表明，金属零件的数字化铸造具有极大的工艺柔性，能够实现虚拟铸造的部分功能；基于快速原型技术的数字化铸造使得常规生产需要2个月时间的金属零件生产周期缩短到一周，大大缩短了金属零件制造的时间，对于快速响应市场需求具有很大的优势。     

接收机运动情况下天线简谐运动对信道循环平稳性影响的分析

在均匀散射环境下,当天线做简谐运动时,信道中会引入循环平稳.通过分析信道循环平稳的产生机理,定义了循环平稳度来衡量引入的循环平稳.首先当接收机以恒速做直线运动且接收机上的天线做简谐运动时,研究了信道的自相关特性,包括循环平稳性;然后用Matlab仿真工具绘出有关曲线,仿真实验结果更加清晰地显示了信道的循环平稳性;最后揭示了信道循环平稳的研究意义,并对其在该领域的应用前景进行了展望.     

GNSS系统接收机自主完好性监测算法

提出一种适用于多星座情况的接收机自主完好性监测算法——基于完好性指标动态分配的完好性监测算法。该算法比传统算法更能适应多星座情况下不同星座观测量属性的差异,其可用性比传统算法提高2％～3％。在多星座和双频情况下,利用该算法分析完好性监测的性能,结果表明,该算法在LPV-200完好性要求下是适用的。