Novel Approach for Fault Detection and Diagnosis of Sensors in Air Handling Units Using Wavelet Energy Entropy

A novel fault diagnosis method for sensors in air handling unit(AHU)using wavelet energy entropy was presented.Instead of directly comparing the numerous data under noise conditions,the wavelet energy entropy residual was compared in the proposed method.Three-level wavelet analysis was used to decompose the measurement data under both fault-free and faulty operation conditions.The concept of Shannon entropy was referred to define wavelet energy entropy of the wavelet coefficients.The sensor faults were diagnosed by comparing the deviation of the wavelet energy entropy of the measured signal and the estimated one with the preset threshold.Testing results showed that the wavelet energy entropy was sensitive to diagnose the biased faults.The wavelet energy entropy residuals exceed the threshold significantly when faults occur.In addition,the severer the faults were,the larger the residuals would be.The results prove that the proposed method is feasible and effective for the fault detection and diagnosis of the sensors.     

Discrete wavelet transform-based fault diagnosis for driving system of pipeline detection robot arm

A real-time wavelet multi-resolution analysis （MRA）-based fault detection algorithm is proposed. The first stage detailed MRA signals extracted from the original signals were used as the criteria for fault detection. By measuring sharp variations in the detailed MRA signals, faults in the motor driving system of pipeline detection robot arm could be detected. The fault type was then identified by comparison of the three-phase MRA sharp variations. The effects of the faults were examined. The simulation results show that this algorithm is effective and robust, it is promising for fault detection in a robot＇ s joint driving system. The method is simple, rapid and it can operate in real time.     

Finds in Testing Experiments for Model Evaluation

To evaluate the fault location and the failure prediction models, simulation-based and codebased experiments were conducted to collect the required failure data. The PIE model was applied to simulate failures in the simulation-based experiment. Based on syntax and semantic level fault injections, a hybrid fault injection model is presented. To analyze the injected faults, the difficulty to inject (DTI) and difficulty to detect (DTD) are introduced and are measured from the programs used in the code-based experiment. Three interesting results were obtained from the experiments: 1) Failures simulated by the PIE model without consideration of the program and testing features are unreliably predicted; 2) There is no obvious correlation between the DTI and DTD parameters; 3) The DTD for syntax level faults changes in a different pattern to that for semantic level faults when the DTI increases. The results show that the parameters have a strong effect on the failures simulated, and the measurement of DTD is not strict.     

Applications of Wigner High-order Spectra in Feature Extraction of Acoustic Emission Signals

The characteristics of typical AE signals initiated by mechanical component damages are analyzed. Based on the extracting principle of acoustic emission（AE） signals from damaged components,the paper introduces Wigner high-order spectra to the field of feature extraction and fault diagnosis of AE signals. Some main performances of Wigner binary spectra,Wigner triple spectra and Wigner-Ville distribution （WVD） are discussed,including of time-frequency resolution,energy accumulation,reduction of crossing items and noise elimination. Wigner triple spectra is employed to the fault diagnosis of rolling bearings with AE techniques. The fault features reading from experimental data analysis are clear,accurate and intuitionistic. The validity and accuracy of Wigner high-order spectra methods proposed agree quite well with simulation results. Simulation and research results indicate that wigner high-order spectra is quite useful for condition monitoring and fault diagnosis in conjunction with AE technique,and has very important research and application values in feature extraction and faults diagnosis based on AE signals due to mechanical component damages.     

Dynamic Reconstruction-Based Fuzzy Neural Network Method for Fault Detection in Chaotic System

This paper presents a method for detecting weak fault signals in chaotic systems based on the chaotic dynamics reconstruction technique and the fuzzy neural system （FNS）. The Grassberger-Procaccia algorithm and least squares regression were used to calculate the correlation dimension for the model order estimate. Based on the model order, an appropriately structured FNS model was designed to predict system faults. Through reasonable analysis of predicted errors, the disturbed signal can be extracted efficiently and correctly from the chaotic background. Satisfactory results were obtained by using several kinds of simulative faults which were extracted from the practical chaotic fault systems. Experimental results demonstrate that the proposed approach has good prediction accuracy and can deal with data having a -40 dB signal to noise ratio （SNR）. The low SNR requirement makes the approach a powerful tool for early fault detection.     

Oscillation Test Strategy for Analog Filters by Monitoring Output Voltage and Supply Current

A test strategy for analog filters was proposed. The output voltage and supply current of the circuit were monitored when using the oscillation test technique. The frequency, average value, maximum value and amplitude of both output voltage and supply current were taken as test parameters. Tolerance bands of test parameters were analyzed. Fault detectabilities of test parameters were compared and combined, and optimal parameter sets were derived. Experimental results show that both the output voltage and supply current give significant contribution to fault detection. Considering catastrophic, single and double parametric faults, the fault coverage in testing the benchmark circuit can be raised from 90.6% for traditional voltageonly oscillation test strategy to 97.2% by monitoring both output voltage and current parameters.     

Fault-Tolerant Control for a Class of Uncertain Systems with Actuator Faults

The problem of fault-tolerant controller design for a class of polytopic uncertain systems with actuator faults is studied in this paper. The actuator faults are presented as a more general and practical continuous fault model. Based on the affine quadratic stability (AQS), the stability of the polytopic uncertain system is replaced by the stability at all corners of the polytope. For a wide range of problems including H∞ and mixed H 2 /H∞ controller design, sufficient conditions are derived to guarantee the robust stability and performance of the closed-loop system in both normal and fault cases. In the framework of the linear matrix inequality (LMI) method, an iterative algorithm is developed to reduce conservativeness of the design procedure. The effectiveness of the proposed design is shown through a flight control example.     

Stator Winding Turn Faults Diagnosis for Induction Motor by Immune Memory Dynamic Clonal Strategy Algorithm

Quick detection of a small initial fault is important for an induction motor to prevent a consequent large fault.The mathematical model with basic motor equations among voltages,currents,and fluxes is analyzed and the motor model equations are described.The fault related features are extracted.An immune memory dynamic clonal strategy （IMDCS） system is applied to detecting the stator faults of induction motor.Four features are obtained from the induction motor,and then these features are given to the IMDCS system.After the motor condition has been learned by the IMDCS system,the memory set obtained in the training stage can be used to detect any fault.The proposed method is experimentally implemented on the induction motor,and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of stator winding turn faults in induction motors.     

Quantitative study of tectonic geomorphology along Haiyuan fault based on airborne LiDAR

High-precision and high-resolution topography are the fundamental data for active fault research.Light detection and ranging(LiDAR)presents a new approach to build detailed digital elevation models effectively.We take the Haiyuan fault in Gansu Province as an example of how LiDAR data may be used to improve the study of active faults and the risk assessment of related hazards.In the eastern segment of the Haiyuan fault,the Shaomayin site has been comprehensively investigated in previous research because of its exemplary tectonic topographic features.Based on unprecedented LiDAR data,the horizontal and vertical coseismic offsets at the Shaomayin site are described.The measured horizontal value is about 8.6 m,and the vertical value is about 0.8 m.Using prior dating ages sampled from the same location,we estimate the horizontal slip rate as4.0±1.0 mm/a with high confidence and define that the lower bound of the vertical slip rate is 0.4±0.1 mm/a since the Holocene.LiDAR data can repeat the measurements of field work on quantifying offsets of tectonic landform features quite well.The offset landforms are visualized on an office computer workstation easily,and specialized software may be used to obtain displacement quantitatively.By combining precious chronological results,the fundamentallink between fault activity and large earthquakes is better recognized,as well as the potential risk for future earthquake hazards.     

Correlation Dimension in Fault Diagnosis of 600 MW Steam Turbine Generator

GP algorithm of correlation dimension computation is ameliorated which overcomes the shortage of traditional one. Improved process of GP algorithm takes the influence of temporal correlative pairs of points on correlation dimension into account and promotes the computational efficiency prominently. Iterative SVD method is applied to remove the influence of noise on the result of correlation dimension. The faults of steam flow turbulence and oil film disturbance which occur in 600MW Steam Turbine Generator are analyzed and whose correlation dimensions are computed. More distinct quantitative index than FFT is gained to distinguish two faults and it‘s of little importance to apply correlation dimension to study the influence of various factors on steam flow turbulence fault for nonexistence of convergent floor in correlation integral curve, which presents a new way to learn the operational function of large capacity steam turbine generator and carry out comprehensive condition monitoring.     

一种基于小波变换的模糊聚类算法及其应用

为准确对输电线路故障性质、故障相等进行识别,提出一种基于小波能量比值的模糊C-均值聚类(FCM)算法,并研究了该算法在输电线路的永久性和瞬时性故障识别中应用的可行性.结合小波分析的时频分析能力、小波能量比值的特征提取能力和FCM的模式识别能力,建立一实际500kV输电线路的PSCAD模型,对单相故障产生时的暂态电流进行了聚类分析和识别.仿真结果表明:基于小波能量比值的FCM算法能较好地识别故障相与非故障相,且算法收敛速度快,识别结果准确.     

小波分析在变压器励磁涌流识别中的研究

励磁涌流是影响变压器差动保护正确动作的重要因素,而如何区分励磁涌流和内部故障电流又是变压器保护存在的主要问题,为此,文中利用Coif5小波函数在奇异信号处理方面的优势,分别对励磁涌流和内部故障电流进行小波分析。仿真研究结果表明:经过小波变换后励磁涌流呈现周期性奇异点,而内部故障的波形无奇异点,coif5小波变换后的周期性奇异信号能够很好地识别励磁涌流和内部故障,且方法简单、可靠。     

随机线性系统多执行器鲁棒故障诊断

针对随机线性控制系统提出了新的故障诊断方法·对于执行器故障情形,通过数学变换将故障转化为系统的未知输入,然后利用未知输入卡尔曼滤波器技术实现执行器故障诊断·诊断算法给出含噪故障估计值·为得到精确值,又采用了小波去噪方法·两种方法有机结合可对同时或接连发生的多执行器参数故障进行有效诊断,故障检测与估计及时准确·最后给出了仿真实例,结果验证了提出方法的正确性·     

基于小波分解和重构的暂态选线与扰动识别

在小波分解和重构理论的基础上,提出了基于故障暂态电流α模分量突变量的故障选线方法。根据小波理论善于处理突变信号的特点,利用小波理论对暂态电流、电压信号进行分析,由分解后的小波系数构成综合故障测度进行选线,小波重构信号则对故障和扰动进行识别。大量的仿真试验证明,提出的选线方法可以很好地对故障线路进行选择,同时不受扰动影响。     

基于小波分解方法的暂态选线分析

在小波理论的基础上,提出了基于故障暂态电流α模分量突变量的故障选线方法。根据小波理论善于处理突变信号的特点,并利用小波理论对暂态信号进行分析,由分解后的小波系数构成的综合故障测度进行选线。大量的仿真试验证明,此选线方法可以很好地对故障线路进行选择。     

单相自动重合闸永久性故障识别新方法研究

提出一种识别高压输电线路单相接地故障类型的新方法.高压输电线发生单相接地故障,两侧断路器完全开断后,从线路首端检测线路对地电流,瞬时性故障时为很小的本线路电容电流;永久性故障时为较大的电磁耦合电流.通过检测故障线路对地电流的大小实现短路故障类型的判断.该方法可有效识别单相接地短路故障类型,有一定耐过渡阻抗影响的能力,仿真结果验证了该方法的有效性.     

基于小波包的开关电流电路故障诊断

为提高开关电流电路故障诊断的精度,提出了一种基于小波包优选和优化BP神经网路的开关电流电路特征抽取与识别方法.首先对开关电流电路原始响应信号进行多层次的小波包分解,接着计算N层分解后的归一化能量值,以特征偏离度作为评价选择最优小波包基,构建最优故障特征向量,最后将提取的最优故障特征通过遗传算法优化的BP神经网络进行分类.该方法以实例电路进行验证,结果表明所有的软故障均得到了有效的分类,说明了该方法在开关电流电路故障诊断中的优越性.     

一种基于小波分时灰度矩与概率神经网络的电网故障诊断方法

为提高电网故障诊断的准确率和速度,提出一种将小波分时灰度矩与概率神经网络相结合的电网故障诊断方法,通过对小波灰度矩进行时间上的划分,计算得到故障发生后电流在不同时刻的灰度矩的值,从而得到小波系数随时间的变化情况;以小波分时灰度矩作为概率神经网络的输入,诊断结果作为输出,实现对电网故障的自动诊断,利用PSCAD/EMTDC对电网不同类型的故障进行了仿真,采用连续小波变换对电网发生短路故障后的暂态信息进行分析,提取其灰度矩信息,利用概率神经网络进行了故障识别。仿真结果表明,小波分时灰度矩具有较强的细节表现能力,可作为电网故障的故障特征,与概率神经网络相结合可有效地实现对电网故障的自动识别。     

基于LMD近似熵和SVM的自适应重合闸方法

自适应重合闸的功能是快速、准确地辨识故障性质及捕捉电弧熄灭时刻.在分析瞬时性故障和永久性故障断路器跳闸后的端电压波形复杂性的基础上,提出了局部均值分解(LMD)、近似熵和线性支持向量机(SVM)相结合的自适应重合闸整体实现方案.利用LMD分解故障信号得到若干个PF分量,选取前3个PF分量算出其近似熵值构成三维特征向量,将三维特征向量作为SVM的输入量来区分故障性质和捕捉电弧熄灭时刻.线路故障仿真结果表明,该方案可智能识别故障性质和捕捉电弧熄灭时刻且具有一定的抗噪能力.     

基于Mallat算法的小电流单相接地故障选线研究

提出一种利用小波分析中的Mallat分解算法对小电流系统故障线路进行诊断的方法.当小电流接地系统发生单相接地故障的时候,提取的暂态零序电流经过Mallat算法分解,通过模极大值理论有效的判断出故障线路.