一种基于小波变换的导弹运输车辆故障诊断方法

利用离散小波变换的时频特性和连续小波变换检测信号边沿的原理,进行模拟导弹运输 车辆轮胎和板簧的故障检测、分离和定位.该方法不需要对象的数学模型.模型车的故障诊断实验 结果表明.该方法灵敏度高,对噪声具有较好的鲁棒性.     

分形信号的小波谱相关研究及仿真

小波变换作为一种新兴时频分析方法,其小波谱表征了信号的时频特性.文中对信号的小波谱进行了较为详细的讨论,并与魏格纳-维尔分布进行了比较.根据时频分析的理论,把小波谱扩展到了小波相关域分析,提出了信号的小波谱相关分析方法,并应用到分形信号的分析上,探讨了它的相应特性.对探讨的结果进行了仿真,给出了小波谱相关在信号检测中的应用前景.     

利用小波分析检测航空发动机传感器故障

该文比较了傅立叶变换与小波分析的基本理论并研究了它们在航空发动机传感器故障检测应用中的特点,提出了一种基于小波变换的故障检测方法.该方法针对噪声和故障信号均具有呈现非平稳瞬态特性的特点,利用小波多分辨分析将量测信号分解到不同的频率通道中去,因此它就可以在一定的频率区间内,将故障信号成分和正常信号输出成分区分开来,提高传感器故障检测的准确度.仿真结果表明,该方法借助小波变换强大的时频分析能力,可以准确判定传感器软、硬故障,有效降低误报率和漏报率,具有良好的应用价值.     

基于小波包变换的电力系统谐波分析

传统的基于傅立叶变换的谐波检测方法不具有时间分辨能力,小波和小波包变换因其良好的时间局部化特性,成为电力系统谐波分析的有力工具.本文分别用小波变换和小波包变换对电网谐波信号进行了检测,小波包变换建立在小波变换的基础上,可以实现信号频带的均匀划分,能更好地提取信号的时频特性.仿真结果显示两种方法均能有效的分离基波和谐波,小波包变换能根据要求分离任意次谐波,仿真分析中指出了两种分析方法的缺点.     

基于小波分析的传感器故障诊断方法仿真研究

针对装甲车辆数据采集控制系统中传感器故障难于直接检测的问题,本文提出一种基于小波变换的方法对传感器故障进行诊断。首先介绍了小波分析的基本理论,对比阐述了连续小波变换和离散小波变换的原理和优缺点;其次利用Matlab/Simulink搭建模型进行仿真试验,采用连续小波变换对传感器故障进行检测,准确定位故障发生时刻;最后本文还选取了几种典型的传感器故障进行诊断,仿真结果验证了该方法的可行性和准确性。     

Hilbert-小波变换的齿轮箱故障诊断*

采用希尔伯特—小波变换对振动加速度传感器获取的齿轮箱振动响应信号进行特性分析。利用小波变换分解获得振动响应信号的各层高频信号小波系数和低频信号小波系数,对小波系数进行重构获得具有不同特征时间尺度的各高频信号和低频信号;再对分解的信号进行希尔伯特变换获得时频信息谱以提取系统的统计特征信息,实现监测齿轮运转工作状态,及时发现齿轮的早期故障,提高机械运行的安全性。仿真研究结果表明,小波变换分解和希尔伯特边际谱方法在故障信息诊断方面是可行和有效的,提高了故障检测的可靠性。     

分形信号的小波谱相关研究及仿真

小波变换作为一种新兴时频分析方法,其小波谱表征了信号的时频特性。文中对信号的小波谱进行了较为详细的讨论,并与魏格纳一维尔分布进行了比较。根据时频分析的理论,把小波谱扩展到了小波相关域分析,提出了信号的小波谱相关分析方法,并应用到分形信号的分析上,探讨了它的相应特性。对探讨的结果进行了仿真,给出了小波谱相关在信号检测中的应用前景。     

基于相干小波变换的信号相位差估计

精确地估计两列信号间的传输延迟在工程上有着重要的意义。傅里叶变换方法很难区分或识别信号的瞬时变化,而小波变换方法是一种时间窗和频率窗都可改变的时频局部化的分析方法,在非平稳信号的分析方面具有明显的优势。给出应用Morlet小波变换的相干性实现两信号相位差估计的算法,在不同信噪比条件下,对算法的估计性能进行了仿真研究。仿真研究结果表明,在低信噪比的条件下,基于小波变换的相位差估计方法可以实现信号相位差的精确估计。通过与基于离散时间傅里叶变换方法的比较,验证了小波变换方法在估计信号相位差方面的优越性。该方法还可用于对非平稳信号相位差的估计。     

一种基于小波变换的红外导引头信号检测方法

针对某型号红外导引头信号的检测问题,提出了一种基于离散平稳小波变换的微弱脉冲信号检测方法.根据有用脉冲信号与噪声信号在频谱特性上的差异,对导引头信号进行多尺度的离散平稳小波变换,利用分解后得到的低频近似信号逼近信号中的低频噪声来滤出低频噪声的干扰,同时采用阈值去噪的方法处理信号中的白噪声.将该方法应用于仿真信号和真实导引头信号检测,仿真实验结果表明:该方法在有效克服传统离散正交小波变换去噪时容易产生的Gibbs现象的前提下,极大地提高了导引头信号的信噪比,增强了导引头的探测能力.     

基于同步压缩小波变换的检测锚杆参数方法

时频的聚集性是评判信号分析方法效果的主要因素，传统的分析方法已经不能满足时频聚集性要求，因而提出一种高分辨率的分析方法-同步压缩小波变换(Synchrosqueezing Wavelet Transform, SWT)。该方法在频率方向上把小波系数重新挤压和排列，不仅提升时频的分辨率，而且还能实现信号重构。将此方法应用于分析锚杆质量检测数据，与传统的EEMD方法对比表明，SWT能够较为准确地描述信号的频率构成且重构的信号有较好的降噪效果。     

基于离散小波变换的某型航空发动机故障诊断研究

主要研究了离散小波变换极值点的方法在航空发动机传感器故障诊断中的应用;对输入信号输出信号进行离散小波高、低频系数分解重构,利用该系数求出输入输出信号的奇异值,然后去除由于输入突变所引起的极值点,其余的极值点对应于传感器的故障;在MATLAB平台下,仿真结果表明,该故障诊断方法可以及时、有效地检测出航空发动机传感器出现的各类故障。     

EnvironmentalWaveletTool: Continuous and discrete wavelet analysis and filtering for environmental time series

A MATLAB-based computer code has been developed for the simultaneous wavelet analysis and filtering of several environmental time series, particularly focused on the analyses of cave monitoring data. The continuous wavelet transform, the discrete wavelet transform and the discrete wavelet packet transform have been implemented to provide a fast and precise time–period examination of the time series at different period bands. Moreover, statistic methods to examine the relation between two signals have been included. Finally, the entropy of curves and splines based methods have also been developed for segmenting and modeling the analyzed time series. All these methods together provide a user-friendly and fast program for the environmental signal analysis, with useful, practical and understandable results.     

基于小波分析的故障系统在旋转机械中的应用

就小波分析技术在旋转机械故障诊断应用中的故障特征提取问题进行了深入研究,提出了基于小波奇异性及小波变换模极大值的故障特征提取方法,通过对故障信号与小波变换的多分辨率方法以及奇异性理论相结合进行研究,发现小波分析便于对信号的总体和局部进行刻画;利用小波变换对信号的分解和重构特性,可有针对性地选取有关频带的信息以及降低噪声干扰,通过对重构信号的频谱分析能更有效地提取裂纹故障的典型特征。结果表明,对于旋转机械的非平稳信号来说,利用小波变换方法进行故障诊断是行之有效的。     

7500吨浮吊齿轮箱故障诊断系统的研究

针对7500吨浮吊齿轮箱故障诊断问题,将离散小波变换和Tikhonov支持向量机结合建立了一个浮吊齿轮箱故障诊断系统。在输入层对振动信号进行离散小波变换,提取不同频带的能量参数作为故障特征向量,利用这些特征向量进行Tikhonov支持向量机的学习,训练后的Tikhonov支持向量机诊断浮吊齿轮箱故障。实验结果表明,离散小波Tikhonov支持向量机具有很强的故障识别性能和鲁棒性,诊断精度优于常规的BP网络方法。     

Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference

In this paper, an intelligent diagnosis for fault gear identification and classification based on vibration signal using discrete wavelet transform and adaptive neuro-fuzzy inference system (ANFIS) is presented. The discrete wavelet transform (DWT) technique plays one of the important roles for signal feature extraction in the proposed system. The abnormal transient signals will show in different decomposition levels and can be used to recognize the various faults by the DWT figure. However, many fault conditions are hard to inspect accurately by the naked eye. In the present study, the feature extraction method based on discrete wavelet transform with energy spectrum is proposed. The different order wavelets are considered to identify fault features accurately. The database is established by feature vectors of energy spectrum which are used as input pattern in the training and identification process. Furthermore, the ANFIS is proposed to identify and classify the fault gear positions and the gear fault conditions in the fault diagnosis system. The proposed ANFIS includes both the fuzzy logic qualitative approximation and the adaptive neural network capability. The experimental results verified that the proposed ANFIS has more possibilities in fault gear identification. The ANFIS achieved an accuracy identification rate which was more satisfactory than traditional vision inspection in the proposed system.     

基于自联想小波网络的汽轮发电机组故障诊断

针对电厂汽轮发电机组故障诊断问题,将小波变换和自联想神经网络结合构造了一个多层的自联想小波网络故障诊断系统。在输入层对振动信号进行二进离散小波变换,提取其在多尺度下的细节系数作为故障特征向量,根据这些特征向量进行自联想网络的学习,用学习过的自联想网络诊断故障。将该方法成功地应用于汽轮发电机组故障诊断,实验仿真结果表明,该方法优于常规的BP网络方法：某些单一故障的识别率提高了31.2%,综合故障的识别率提高了26.6%。     

多层导电结构缺陷电涡流扫描检测信号预处理技术研究

应用小波多分辨分解及重构技术和小波包分析技术进行检测信号预处理(噪声和干扰信号分离与去除)的基本原理,结合电涡流扫描检测实验,采用离散小波变换强制消噪法、软阈值消噪法和不同熵准则的小波包分析消噪法对检测信号进行预处理,并以SNR和RMSE为判断消噪效果好坏的标准,进行了效果的比较和优选.从理论分析和实验研究结果可知,分离提离等干扰信号,可采用强制法;消除高频噪声,采用基于Shannon熵准则的WPA法,效果最好.     

Investigation of engine fault diagnosis using discrete wavelet transform and neural network

An investigation of a fault diagnostic technique for internal combustion engines using discrete wavelet transform (DWT) and neural network is presented in this paper. Generally, sound emission signal serves as a promising alternative to the condition monitoring and fault diagnosis in rotating machinery when the vibration signal is not available. Most of the conventional fault diagnosis techniques using sound emission and vibration signals are based on analyzing the signal amplitude in the time or frequency domain. Meanwhile, the continuous wavelet transform (CWT) technique was developed for obtaining both time-domain and frequency-domain information. Unfortunately, the CWT technique is often operated over a longer computing time. In the present study, a DWT technique which is combined with a feature selection of energy spectrum and fault classification using neural network for analyzing fault signal is proposed for improving the shortcomings without losing its original property. The features of the sound emission signal at different resolution levels are extracted by multi-resolution analysis and Parseval’s theorem [Gaing, Z. L. (2004). Wavelet-based neural network for power disturbance recognition and classification. IEEE Transactions on Power Delivery 19, 1560–1568]. The algorithm is obtained from previous work by Daubechies [Daubechies, I. (1988). Orthonormal bases of compactly supported wavelets. Communication on Pure and Applied Mathematics 41, 909–996.], the“db4”, “db8” and “db20” wavelet functions are adopted to perform the proposed DWT technique. Then, these features are used for fault recognition using a neural network. The experimental results indicated that the proposed system using the sound emission signal is effective and can be used for fault diagnosis of various engine operating conditions.     

Analysis and validation of wavelet transform based DC fault detection in HVDC system

Fault detection plays an important role in both conventional AC and upcoming DC power systems. This paper aims to study the application of discrete wavelet transform (WT) for detecting the DC fault in the high voltage DC (HVDC) system. The methods of choosing the mother wavelet suited for DC fault is presented, based on degree of correlation to the fault pattern and the time delay. The wavelet analysis is performed on a multi-terminal HVDC system, built in PSCAD/EMTDC software. Its performance is judged for critical parameter like the fault location, resistance and distance. The analysis is further extended to validation using results from experiment, which is obtained from a lab-scale DC hardware setup. Load change, one of the transient disturbances in power system, is carried out to understand the effectiveness of the wavelet transform to differentiate it from the DC fault. The noise in the experimental result gives rise to non-zero wavelet coefficient during the steady-state. This can be improved by removing the unwanted noise using right filter while still retaining the fault-induced transient. The wavelet transform is compared with short-time Fourier transform to highlight the issue with window size and noise.