Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function

As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.

Analysis of signal-obtaining error factor of sensor in gyro

The digital signal-obtaining for gyroscope is given. The single optic-fiber sensor via modulating intensity of light is used as measuring component . The influence on static transmission properties resulting from the special working environment (e.g. cryogenic and vacuum) ,the measure error because of the reflector shape of rotor,the abnormity of facula from sensor caused by the existence of engraving error,and the fixing error of sensor and the error of machine tool’s initial lignment are investigated. The mathematic model in every condition is founded, the simulation and relative experiments are done and the outcome is analyzed. The mathematic model and method of compensating technology are studied and some relative experiments are made. The result of study is useful to improvement of the signal-obtaining system.

Modeling random telegraph signal noise in CMOS image sensor under low light based on binomial distribution

The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.

SENSOR FAILURE DETECTION AND SIGNAL RECOVERY BASED ON BP NEURAL NETWORK

A study is given on the application of BP neural network (BPNN) in sensorfailure detection in control systems, and on the networ architecture desgn, the redun-dancy,the quickness and the insensitivity to sensor noise of the BPNN based sensor detec-tion methed. Besules, an exploration is made into tbe factors accounting for the quality ofsignal recovery for failed sensor using BPNN. The results reveal clearly that BPNN can besuccessfully used in sensor failure detection and data recovery.

Design of ispPAC-based Humidity Sensor Signal Processing Circuits

The widely used sensitive elements of humidity sensors can be divided into 3 types,i.e.,resistor,capacitor,and electrolyte.Humidity sensors consisting of these sensitive elements have corresponding signal processing circuit unique to each type of sensitive elements.This paper presents an ispPAC (in-system programmable Programmable Analog Circuit) -based humidity sensor signal processing circuit designed with software method and implemented with in-system programmable simulators.Practical operation shows that humidity sensor signal processing circuits of this kind,exhibit stable and reliable performance.

A Sensor Failure Detection Method Based on Artificial Neural Network and Signal Processing

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Direction finding of coexisted independent and coherent signals using electromagnetic vector sensor

The existing direction of arrival （DOA） estimation algorithms based on the electromagnetic vector sensors array barely deal with the coexisting of independent and coherent signals. A two-dimensional direction finding method using an L-shape electromagnetic vector sensors array is proposed. According to this method, the DOAs of the independent signals and the coherent signals are estimated separately, so that the array aperture can be exploited sufficiently. Firstly, the DOAs of the independent signals are estimated by the estimation of signal parameters via rotational invariance techniques, and the influence of the co- herent signals can be eliminated by utilizing the property of the coherent signals. Then the data covariance matrix containing the information of the coherent signals only is obtained by exploiting the Toeplitz property of the independent signals, and an improved polarimetric angular smoothing technique is proposed to de-correlate the coherent signals. This new method is more practical in actual signal environment than common DOA estimation algorithms and can expand the array aperture. Simulation results are presented to show the estimating performance of the proposed method.

基于热释电红外传感器的弱磁信号智能采集方法研究

对弱磁信号采集受到环境干扰和采集传感设备部署节点不合理等因素的影响,导致采集的信号具有大量干扰噪声,为了提高弱磁信号采集的分辨能力,结合阵列信号处理技术,提出基于热释电红外传感器的弱磁信号智能采集方法。构建弱磁信号智能采集的热释电红外传感器的节点分布阵列模型,结合相位偏移修正和噪声干扰抑制的方法,构建弱磁信号采集的滤波模型,基于信号波形的相干阵列检测方法实现对弱磁信号的空域特征增强,结合多径信号相干匹配技术和相应的多个DOA估计技术,通过热释电红外传感器的阵列节点输出信号的增强和滤波处理实现信号智能采集。实验结果表明,采用该方法进行弱磁信号智能采集的输出信号幅度和相位增益性较好,输出信噪比为45.9 dB,弱磁信号的误码率为0.55 bit,说明本方法具有实用性。

强信号全覆盖下反射式强度型传感器信号调制设计

反射式强度型传感器在强信号全覆盖环境下常常受到干扰和影响,导致传感器信号的质量下降甚至失真,给后续的信号处理和数据分析带来了困难。对此,提出强信号全覆盖下反射式强度型传感器信号调制方法。首先,判断强信号全覆盖环境下传感器信号的稀疏性,并根据环境的干扰因素建立信号的观测矩阵,通过优化算法计算出稀疏向量的估计值,使用稀疏向量的估计值对观测信号进行补偿,得到补偿后的信号;然后,应用正交锁相放大技术,将补偿后的信号与严格正交的两路参考信号相乘,提高信噪比,获得增强后的信号;最后,利用连续相位多支路调制方法,对增强后的传感器信号展开比特信息分离、二维信号映射与载波相位调制操作,完成信号调制。实验结果表明,所提方法可扩展性强、稳定性高且抗干扰能力强。

基于混合神经网络的多维视觉传感信号模式分类

传感器采集的数字信号分类精度差,导致关键信息的丢失。为了提高传感数据的可靠性和有效性,提出基于混合神经网络的多维视觉传感信号模式分类方法。结合卷积神经网络(CNN)、循环神经网络(RNN)构建混合神经网络,以更有效地表示多维视觉数据中的特征;其中,卷积神经网络负责对多维的空间信号进行去噪处理并提取特征;循环神经网络负责对时域和频域信号进行特征提取;混合神经网络通过联合训练CNN和RNN各自的参数,以调整其权重,并且结合两者从不同层级提取的特征来实现多维视觉传感信号模式的分类。仿真结果表明,使用所提方法进行分类时,信号光滑度保持在0.9以上,传感信号分类结果与实际结果拟合度较高,有效实现多维视觉传感信号模式分类。

基于物联网的水务移动监测平台应用研究

由于水务系统的各项硬件设备长期运行在潮湿环境下,导致故障的发生频率升高。为了实时监测水务系统的运行,提出了基于物联网的水务移动监测平台应用研究。根据电压探测芯片的感知原理,设计了电源监测电路。在恒定电流下,将压敏元件应用到压力/流量传感器信号处理电路中,结合水务物联网移动监测终端的设计,完成平台的硬件设计。在平台的软件设计中,通过给出供水压力、水泵流量和调速比的约束条件,构建了水务系统优化调度模型。通过监测供水管的裂痕情况,设计了水务移动监测算法,实现平台的软件设计。测试结果表明,监测平台的各项功能均可以正常运行,并将监测数据传输时间控制在5 s以内,提高了监测的实时性。

传感器信息融合下新能源汽车动力电池信号故障检测方法

新能源汽车作为节能环保的新产品具有较好社会前景,内在的动力电池是新能源汽车的主动力源,但电池发动机是一个较为复杂的系统,在处于恶劣环境时,可能出现各种故障问题。新能源汽车的动力电池若发生故障,不但会使汽车的系统性能下降,还会造成灾难性的后果,为此,研究传感器信息融合下新能源汽车动力电池信号故障检测方法。通过一致性定律整理电池系统传感器数据,在近似概率和频率中估算新能源汽车动力电池信号;选择熵权重法理论对数据信号进行区分,以时刻内单体电压作为评价指标,在预处理后构建判断信号故障矩阵;通过故障判断矩阵确定异常信号,在传感器信息融合算法下修订权值,以最大误差范围检测信号输出,检测新能源汽车动力电池信号故障,完成检测方法设计。实验以四组不同类型的新能源汽车作为测试对象,对其动力电池的运动工况进行信号模拟,在不同的接口处获取故障电压信号并完成检测测试,设计的电池信号故障检测方法能够实现精准的故障信号跟踪,完成较为精准的故障信号检测,具有一定的应用价值。

扩展卡尔曼滤波的改进蛇定位算法在WSN中的应用

针对接收信号强度指示(Received Signal Strength Index,RSSI)定位易受到环境因素的影响,提出了一种基于RSSI扩展卡尔曼滤波的改进蛇定位算法(RSSI Extended Kalman Filter-based Improved Snake Optimization Localization Algorithm,RSSI-EISL)。该算法利用扩展卡尔曼滤波(Extended Kalman Filter,EKF)模型对RSSI信号值进行平滑处理,使其能够抑制噪声和异常值对估计结果的影响,从而提高测距的准确性和鲁棒性。通过引入Levy飞行和非线性收敛因子的改进蛇优化算法(Improved Snake Optimization Algorithm,ISO),提升了蛇优化算法(Snake Optimization Algorithm,SO)的寻优能力,使之能够更加准确地计算出待测节点的坐标。根据仿真结果显示,相较于基于RSSI最小二乘定位算法(RSSI Ordinary Least Squares Localization Algorithm,ROL)、基于RSSI EKF的灰狼定位算法(RSSI Extended Kalman Filter-based Grey Wolf Optimization Algorithm,REGL)和基于RSSI EKF的蛇定位算法(RSSI EKF-based Snake Optimization Localization Algorithm,RESL),RSSI-EISL的定位精度分别提高了26.4%、8.75%和5.6%,算法的收敛速度和全局搜索能力也有所提升。

机械设备电气故障自动检测系统优化设计

当前系统不能有效消除干扰信号,数据采集精度低,影响机械设备电气故障诊断,为此设计一种新的机械设备电气故障自动检测系统。采集电气故障检测数据,提取电气故障特征,根据特征判断机械设备的电气故障原因,计算纠正参数完善电气故障检测结果,线性处理电压检测曲线,完成机械设备电气故障检测。测试结果验证,所提系统能够有效消除干扰信号,保证整体功能运行稳定,相对于对比系统,响应时间更短,数据采集精度得到明显改善。

基于LabVIEW-VISA的传感器检测虚拟实验平台设计

为提升应用本科机电专业学生智能传感应用能力,设计传感器检测虚拟实验平台。提出基于LabVIEWVISA的传感器检测虚拟实验平台及实验方法,在LabVIEW-VISA应用设计基础上搭建了传感器检测虚拟实验平台整体架构;设计了传感器实验箱、MyDAQ便携式测量、万用表、应变式传感器等主要硬件;进行了数据采集模块、数据处理、输出模块及信号处理电路等软件设计,利用信号处理电路实现数据在平台中的双通道传输,实现了预期目标。结果表明,设计的传感器检测虚拟实验平台实验验证灵敏度高、线性误差小,有利于提高人才实践动手能力培养。

非接触式生命体征检测装置的设计

为解决临床医学、急救或居家养老等应用场景中无法直接使用传统穿戴式监测仪器实时监测人体生命体征的问题,设计了一种基于多普勒雷达的非接触式生命体征检测装置,该装置通过STM32F429IGT6控制器从多普勒雷达检测的信号中实时提取人体呼吸和心跳信号。其硬件主要由微控制器、多普勒雷达传感器信号放大电路、滤波器、电平抬升电路等部分组成,系统通过IIR带通滤波实现呼吸和心跳信号分离与提取,使用FFT对分离的呼吸和心跳信号进行实时频谱分析,计算并显示呼吸和心跳次数。实验测试结果表明,该装置能有效实时检测人体的呼吸和心跳次数,实现2 m范围内的非接触式人体生命体征检测,心跳次数最大均值误差为9.14%,呼吸次数最大均值误差为4.7%。

面对分布式多域电磁态势感知的射频传感器设计

射频传感器是电磁态势感知的一种重要手段,通过对射频信号进行探测和分析,为电磁态势感知提供必要的数据支撑。针对现有射频传感器存在的获取信息不全面、集成度差等问题,提出基于分布式架构,以时、频、空、能等多域为切入点,设计一款分布式的多域电磁态势感知射频传感器,对射频信号进行多域协同侦测,为分析和评估战场电磁态势提供重要参考。首先基于Zynq多进程(Zynq multi-processor,ZynqMP)处理器和ADRV9009射频收发芯片,设计了传感器的硬件方案,并完成了系统实物制作与测试,然后在软件算法方面,从时、频、空、能四个方面入手,分别就各域的关键特征指标,设计了相应的提取算法,最后设计了相应的功能和性能对比测试内外场实验。实测结果表明,传感器在时域所获得波形的震荡次数的误差小于1.92%,频域中对信号带宽和中心频率的测量误差分别小于3.2%和0.04%,能域中对能量的计算存在小于5.6%的误差,空域中来波方向角的测量误差小于2.7%,达到了预期设计要求。

基于宽频振动传感器的电力设备状态监测方法可行性分析

宽频振动信号包括1 Hz~20 kHz的低频振动信号和20~500 kHz的高频超声信号。近年来,利用振动信号对变压器、电容器、电抗器等电力设备进行状态检测的手段日益成熟,超声信号也可以很好地检测出电力设备中的局部放电并定位,均已有较为成熟的应用。文中提出了基于宽频振动传感器的电力设备状态监测方法,并使用该传感器同时测量了脉冲振动源的振动以及油纸绝缘针板电极局部放电的超声,借助仿真对超声时延进行了修正,对局部放电进行了识别,并与脉冲电流法所得结果进行了对比。结果表明,所提出的监测方法可以很好地监测到振动和超声,并实现局部放电的识别,该方法应用于电力设备状态监测是可行的。

面向瘫痪肢体功能康复的无线多通道混合数据采集系统

设计了一款无线多通道混合数据采集系统,包含硬件系统和PC数据采集及分析处理软件系统两部分。硬件系统根据目标需求,通过按键设置体表肌电(sEMG,surface electromyography)信号采集通道数和惯性测量单元(IMU,inertial measurement unit)数量,基于FreeRTOS操作系统创建sEMG信号传输任务和与IMU传感器数量对应的运动学数据采集任务,采用互斥信号量技术确保各任务获取的数据通过Wi-Fi传输给PC软件系统;PC软件系统实现对获取的混合信号实时显示、存储、处理分析等。相比于当下商用Trigno无线基础混合信号采集系统和其他自研系统,所设计的混合数据采集系统在瘫痪肢体运动功能康复领域更实用、具有更强的扩展性。

人体爬行运动信号检测与处理的研究进展

爬行是幼儿粗大运动发育的重要标志,同时也是运动障碍患者重要的康复训练手段,准确评价人体爬行运动状态对于了解幼儿粗大运动发育进程以及运动障碍患者康复疗效等具有重要的临床应用价值。因此,近年来许多研究尝试通过检测分析人体爬行过程中的运动生理信号来实现对爬行状态的量化评价,但目前缺乏针对人体爬行运动信号检测与处理的整理工作,本文对相关研究进行系统综述。首先,从惯性传感器、压力传感器、表面肌电等角度介绍目前面向人体爬行运动信号检测的主流方法;然后,依次介绍针对人体爬行运动信号进行周期分割、运动学分析、动力学分析等内容,其中立足于运动协同理论,详细总结人体爬行运动过程中关节协同运动、肌肉协同收缩方面的研究进展;最后,对当前人体爬行运动分析存在的问题和未来的发展方向进行讨论及展望。