多传感器融合综述

对智能机器人领域的多传感器融合的研究现状进行了讨论。首先，对该领域研究中的一些问题进行了概述，包括信息描述空间、数据关联与时间同步、验前信息和融合结构等，然后把已有的融合方法分类成概率统计方法和人工智能方法进行重点介绍。最后，强调了融合结构在多传感器融合研究中的作用，并且对多传感器融合的未来研究作了进一步的展望。     

仿神经网络多传感器组合

提出了仿神经网络多传感器系统组合技术。采用多向、多结点网络连接的方法，使多传感器系统本身具有人工神经网络的某些特征，可以对传感器信号进行处理或预处理，实现传感器信号的自动融合及并行传输。     

一致性多传感器数据融合方法的改进

多传感器数据融合是指将经过集成处理的多传感器数据进行合成，形成对外部环境某一特征的一种表达方式的过程。本文首先介绍了Ｌｕｏ［１］的一致性多传感器数据融合方法。然后，针对Ｌｕｏ方法的不足之处，改进了一致性融合方法。该改进方法计算量小，能简便、快速地确定一致性的传感器数据。     

改进的镍电解三段净化智能控制系统

利用各种传感器功能上的差异与互补建立多传感器系统，通过对各传感器提供的信息进行分析、综合，获得比任何单一传感更加稳定、可靠的信息，这就是愈来愈受到重视的多传感器信息集成与融合技术本文提出了多传感器集成与多传感器融合的概念，根据镍电解三段净化过程以及所检测的传感信号，给出了多传感器集成系统概率模型和黑板式控制结构．给出了多传感器信息融合的拓扑结构和融合方法。     

用BP神经网络及其改进算法改善传感器特性

BP算法即多层网络误差反传算法，是近几年在传感器输出信号补偿技术领域中一种较新的方法，广泛应用于传感器信息处理、自动控制、通信等领域。但这种方法也有其固有的缺点，西安交通大学的张永怀博士及刘君华教授对此方法进行了深入的研究，提出了附加动量法、自适应参数变化法为主要内容的BP神经网络改进算法，以对这种方法进行改善。 传感器是测量系统的核心部件，其输出特性直接影响整个系统的性能，提高传感器的精度具有十分重要的意义。传感器的输出受许多环境因素的影响，目前人们多采用软件补偿法来对传感器信号进行处理。传统的…     

基于多Agent的信息化战场多传感器仿真模型

为了实施未来信息化战场多传感器作战运用项目先期概念技术演示，必须建立其仿真模型。通过采用基于多Agent的仿真方法，把多传感器系统的内部组元直接映射成相应的Agent，在分析Agent与传感器之间映射关系的基础上，研究了基于多Agent的多传感器系统仿真过程，提出了基于Agent联邦的多传感器仿真系统总体框架；并进一步研究了传感器Agent的定义、工作原理与过程。构建的多传感器系统仿真模型，以Agent的表现形式获得了对多传感器战场运用这一客观、复杂过程的深刻认识，从而可实现对这种动态作战环境行为的仿真。     

多传感器数据融合在Lonworks总线下的实现

介绍了分布式多传感器数据融合理论，主要包括：分站预决策和融合方法，融合后误警概率和检测概率的算法，以智能小区中火灾报警为对象，研究了如何在Lonworks总线下用Neuron芯片对数据进行压缩，传输和融合，最后对分布式多传感器融合的结果和单一传感器的结果进行了比较。     

传感器非线性的一个拟合方法

介绍了非线性传感器的一种典线拟合方法，通过对某霍尔位移传感器位移、电压值的多项式拟合曲线的特点进行分析，发现该传感器曲线具有抛物线的特点，所以采用含开方项的多项拟合。     

一种在线迭代聚类的多传感器一致性数据融合方法

为了提高多传感器一致性数据融合的效果,在进行多传感器测量数据融之前,必须要对各传感器的测量数据是否具有一致性进行判定处理;针对现有各种多传感器一致性数据融合方法存在的不足,文章运用统计学中的假设检验理论、聚类分析和正态分布的优良性质,提出了一种在线迭代聚类的多传感器一致性数据融合方法,用Matlab进行大量仿真实验的结果表明,文章给出方法优于现有的多传感器一致性数据融合方法。     

可对多目标测量的硅传感器系统􀀁

本文介绍了一种新型的可对多个目标进行测量的硅压力传感器系统的研制，它是把多个硅压力传感器组合一个完整的传感器系统，用于对多个不同的对象同时完成独立测量，文章具体描述了对压力、流量、干度实测测量的原理，过程以及传感器系统的结构制作。     

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.     

基于多传感器的刀具状态监测系统

以铣削加工为对象,研究了多刃切削加工过程的刀具状态监测问题,从系统的角度分析了刀具状态的多传感器监测原理,并以此为依据确定了采用声发射（ＡＥ）传感器和动态切削力传感器可有效地监测加工过程。文中提出了一种多传感器信号的特征提取方法,该方法利用偏最小二乘法计算样本矩阵的本征值,根据置信因子确定特征维数。为验证该方法的有效性,建立了一个铣削加工实验系统,实验结果表明,该方法可在多种切削条件下获得较高的识别率。     

Tool condition monitoring in metal cutting: A neural network approach

This paper discusses the application of neural network-based pattern recognition techniques for monitoring the metal-cutting process. The specific application considered is in-process monitoring of the condition of the cutting tool. Tool condition monitoring is an important prerequisite for successful automation of the metal cutting process. In this paper, we demonstrate the application of supervised and unsupervised neural network paradigms to pattern recognition of sensor signal features. The supervised technique used is backpropagation and the unsupervised technique used is adaptive resonance theory (ART). The results support the premise that, despite excellent classification accuracy by both networks, the unsupervised system holds greater promise in a real world setting. The advantages are discussed and a framework for exploiting them in tool condition monitoring systems is presented.This work was completed as part of graduate research at University of California, Berkeley, Department of Mechanical Engineering.     

新型高压直流电网触网和断网的检测方法

文章介绍了一种用于监测监狱高压直流电网触网和断网的传感器的设计方法、传感器的电路设计及其工作原理。     

基于无线传感器网络的土壤湿度监控系统

为提高水资源的利用效率,且针对传统土壤湿度监控系统难以布线、抗干扰性能差等缺点,设计了一种无线土壤湿度监控系统。传感器节点以CC430单片机为节点核心并辅有土壤湿度传感器等一些外部元件,多节点间应用SimpliciTI无线通信协议组成一个小型的射频网络。主控节点通过GPRS模块向数据监控中心上传传感器节点采集的湿度数据并管理灌溉控制模块,数据监控中心可向无线网络发送控制命令。实验结果显示,基于协议的该系统稳定性高、移植性强、易于扩充,本设计将无线传感器网络技术应用到农业生产中,提出了一种新型实用的方案。     

多个Zigbee监测网络远程监控的实现

Zigbee传感器网络技术作为一种短距离、低速率无线网络技术,目前成了监测领域应用研究的热点。结合IP网络技术,可方便实现对Zigbee监测网络的远程监控。基于嵌入式Webserver的B/S实现模式,对于多个分布的Zigbee监测网络的集中管理存在一定困难。本文给出了利用消息中间技术,实现对多个Zigbee监测网络远程集中管理的方法。     

On-line tool condition monitoring system with wavelet fuzzy neural network

In manufacturing systems such as flexible manufacturing systems (FMS), one of the most important issues is accurate detection of the tool conditions under given cutting conditions. An investigation is presented of a tool condition monitoring system (TCMS), which consists of a wavelet transform preprocessor for generating features from acoustic emission (AE) signals, followed by a high speed neural network with fuzzy inference for associating the preprocessor outputs with the appropriate decisions. A wavelet transform can decompose AE signals into different frequency bands in the time domain. The root mean square (RMS) values extracted from the decomposed signal for each frequency band were used as the monitoring feature. A fuzzy neural network (FNN) is proposed to describe the relationship between the tool conditions and the monitoring features; this requires less computation than a back propagation neural network (BPNN). The experimental results indicate the monitoring features have a low sensitivity to changes of the cutting conditions and FNN has a high monitoring success rate in a wide range of cutting conditions; TCMS with a wavelet fuzzy neural network is feasible.     

On-line robust identification of tool-wear via multi-sensor neural-network fusion

Real-time identification and monitoring of tool-wear in shop-floor environments is essential for the optimization of machining processes and the implementation of automated manufacturing systems. This paper analyzes the signals from an acoustic emission sensor and a power sensor during machining processes, and extracts a set of feature parameters that characterize the tool-wear conditions. In order to realize real-time and robust tool-wear monitoring for different cutting conditions, a sensor-integration strategy that combines the information obtained from multiple sensors (acoustic emission sensor and power sensor) with machining parameters is proposed. A neural network based on an improved backpropagation algorithm is developed, and a prototype scheme for the real-time identification of tool-wear is implemented. Experiments under different conditions have proved that a higher rate of tool-wear identification can be achieved by using the sensor integration model with a neural network. The results also indicate that neural networks provide a very effective method of implementing sensor integration for the on-line monitoring of tool abnormalities.     

一种新型谐振式非接触流体声发射传感器的研制

研制高灵敏度、安装使用方便、抗干扰能力强的传感器是刀具磨破损监测研究需要解决的关键技术 .本文根据刀具磨、破损监测的特点 ,研制了既可用于刀具磨损状态监测 ,也可用于刀具破损监测的谐振式高灵敏度流体声发射传感器 .对研制的流体声发射传感器性能进行了实验研究 ,结果表明传感器对刀具磨损产生的声发射信号具有较高的灵敏度 .     

多传感器信息融合火灾预报技术研究

论述了火灾探测器设计和安装方式,研究了多传感器火灾监测系统及其智能化数据融合方法、数据处理、故障检测和系统可靠性等方面的基本问题。