基于小波包分解和EMD-SVM的轴承故障诊断方法

针对轴承振动信号具有的非平稳和故障诊断样本数据难以按需获取的问题,设计了一种基于小波包分解和EMD-SVM的故障诊断方法。首先,采用Mallat塔式算法对信号进行降噪,实现信号的小波分解,获得重构后的故障诊断子频带信号。然后,在经典的EMD算法的基础上定义了改进的EMD算法,采用改进的EMD算法对经过小波包降噪的故障诊断子频带信号进行特征提取,从而获得故障诊断特征向量。最后,采用适合小样本分类的SVM进行故障诊断,将经过小波包降噪和EMD特征提取的样本数据用于训练SVM,得到用于故障诊断的多个二分类SVM故障诊断模型,通过投票机制来确定样本数据最终对应的故障诊断类别。在Matlab环境下对轴承故障诊断进行实验,实验结果证明了文中基于小波包和EMD-SVM的方法一种适用于小样本的故障诊断方法,且与其它方法相比,具有诊断效率高和精度高的优点。     

小波包和GA-SVM在轴承故障诊断中的应用

为了解决傅里叶变换难以兼顾信号在时域和频域中的全貌和局部化特征以及支持向量机惩罚参数 和核函数参数 选取的问题,提出了基于小波包和GA-SVM的轴承故障诊断方法。首先通过实验采集多种工况下故障轴承和正常轴承的振动信号,从振动信号中提取能够表征轴承运行状态的时频域特征以及基于小波包分析的特征向量来作为GA-SVM的输入,然后在SVM的基础上,针对SVM的惩罚参数和核函数参数在不同应用场景下的取值难以确定的特性,采用了遗传算法对支持向量机进行参数优化的GA-SVM算法进行模式识别。实验结果显示,基于小波包和GA-SVM的轴承故障诊断方法比SVM和BP都具有更高的识别精度。     

基于HMM-SVM的模拟电路早期故障诊断

针对模拟电路在故障预测与健康管理（PHM）系统中早期故障识别率不高的问题,提出了一种基于隐马尔科夫模型（HMM）和支持向量机（SVM）相结合的模拟电路故障诊断方法,利用HMM对动态连续信号的较强识别能力和SVM良好的模式分类能力解决模拟电路早期故障诊断问题。采用主成分分析（PCA）和K-means聚类算法对故障数据进行数据降维和特征提取,建立HMM与 SVM相结合的诊断模型进行故障诊断。仿真实验表明,HMM-SVM能很好地识别模拟电路早期故障,并对模拟电路中元件小范围参数变化的状态识别,相较单一HMM模型具有更高的准确率。     

Walsh变换对鱼类特征识别的研究*

鱼种的快速识别是渔业资源评估乃至海洋生态系统监测重要组成部分。与传统的拖网捕捞等方法相比,声学方法具有快速有效、调查区域广、不损坏生物资源、可持续观察等优点。鱼类的声学识别方法主要是基于鱼类回波信号特征的识别, 鱼体形状及组成结构的复杂多样导致其回波信号非常复杂,因此利用简单的回波包络或能量特征识别鱼类效果往往不能令人满意。本文提出一种基于Walsh 变换的鱼类回波识别方法。试验获取鲫鱼、 嘎鱼、武昌鱼的回波信号,处理过程中分别提取三种鱼类回波包络信号的Walsh谱作为识别特征量,并利用BP神经网络分类器对其进行了分类。结果表明利用回波的Walsh谱可以成功识别不同形状的鱼类,其中对武昌鱼的识别正确率达90%以上。     

分布式EDFA中受激喇曼散射对光孤子脉冲传输的影响

本文用数值方法研究了透明传输下,受激喇曼散射(SRS)对由分布式掺饵光纤放大器(d-EDFA)级联的传输线中传输的光孤子脉冲的泵功率、孤子脉冲平均功率演化、均方根脉宽谱宽积、孤子系统的能量辐射、以及对光孤子传输稳定性的影响.结果表明:SRS降低了系功率,减弱了孤子平均功率起伏,有利于光孤子传输,但也使孤子脉(谱)宽展宽,增大了孤子系统的能量辐射,对孤子传输稳定性产生不良的影响.     

基于滑动峭度相关性准则的局部特征尺度分解分量筛选方法

轴承故障振动信号具有非平稳、非线性特征,且可视为多个调幅-调频分量的叠加,单分量的包络蕴含了轴承的故障特征。局部特征尺度分解可将振动信号准确分解为多个内禀尺度分量之和,某些分量能清晰反映轴承的运行状态,根据包络谱可进行故障诊断。为了准确筛选有用分量,提出了基于滑动峭度相关性准则的分量筛选方法。首先,对信号进行局部特征尺度分解,得到若干个内禀尺度分量;然后,对分量和原始信号分别计算滑动峭度,生成时间序列;最后,依据分量滑动峭度序列与原始信号滑动峭度序列的互相关系数筛选有用分量。通过轴承内圈故障数据分析发现：有用分量与非有用分量之间的滑动峭度互相关系数比互相关系数差异明显,区分度更大,有益于分量的分类、筛选。     

基于主元分析与动态时间弯曲的故障诊断方法及应用研究

轴承是工程实际中常用而又极易损坏的部件,特别是对其早期微弱响应的辨识,具有重要的社会价值和意义。为提高运转轴承的安全可靠性和可维护性,提出了基于主元分析与动态时间弯曲距离的故障诊断方法,它可以准确对早期微弱动态响应辨识、诊断。该方法首先将典型故障样本信号与待测信号小波去噪并EMD分解,并对若干固有模态分量主元分析求取主元,然后对主元分量进行分析,获得相关特征值组成特征向量,计算待测信号与已知故障样本信号特征向量的弯曲距离,弯曲距离越小表明两信号越相似,从而辨识故障。此外,还可将其应用于转子、碰磨、齿轮故障诊断中,工程应用实例表明该方法可以准确故障分类,高效故障诊断。     

252Cf源驱动功率谱密度法测量系统

介绍了252Cf源驱动功率谱密度法测量原理,采用硬件和软件相结合的方式构建实现了一种实际的测量系统和研究平台,以服务于反应堆核参数测量。描述了基于3通道、1 GHz采样率和1 ns同步精度的超高速数据采集卡的中子脉冲序列检测方法,并设计了PC机端的数据处理流程和功率谱密度分析算法。实际测量结果表明,该252Cf源驱动功率谱密度法测量系统能准确高效地得到核随机过程的相关函数和功率谱密度等重要标签参数。     

采用数字信号处理的复合材料超声谱分析

在一块玻璃比例不氧树脂复合材料板的热压固化过程中，人为加进不同种类，大不及放入深度的薄膜片，并预置一定大小的裂纹，分别利用反射法和地获取回流，并对其高速采样及截取，然后进行幅度谱，相位谱，功率谱，相关谱，自相关函数及互相关函数平方包络提取的分析，获得了一些针对不同预置杂质，缺陷的特征。     

航空物流传送设备中轴承故障稀疏特征提取

航空物流传送设备中的轴承由于长期受外侵灰尘影响,其内外环极易发生故障;利用计算机采集轴承的振动信号并进行故障特征提取是轴承故障诊断的常用方法;提出了基于稀疏分解的轴承故障特征提取方法;首先,分析轴承故障特征稀疏提取原理;然后,构造参数化Gabor字典,利用遗传算法对故障特征成分进行匹配追踪 (Matching Pursuit,简称MP),以峭度值最大原则作为迭代结束条件;最后,重构提取的特征成分,进行包络谱分析,得出故障类型;对仿真数据和轴承振动数据的测试结果表明,所提方法能有效提取轴承故障特征成分,为航空物流传送设备中轴承的故障监测提供了一种有效方法。     

An intelligent approach for engine fault diagnosis based on Hilbert–Huang transform and support vector machine

Based on the techniques of Hilbert–Huang transform (HHT) and support vector machine (SVM), a noise-based intelligent method for engine fault diagnosis (EFD), so-called HHT–SVM model, is developed in this paper. The noises of a sample engine under normal and several fault states are first measured and denoised by using the wavelet packet threshold method to initially lower the noise level with negligible signal distortion. To extract fault features of the engine, then, the HHT is selected and applied to the measured noise signals. A nine-dimensional vector, which consists of seven intrinsic mode functions (IMFs) from the empirical mode decomposition (EMD), maximum value of HHT marginal spectrum and its corresponding frequency component, is specified to represent each engine fault feature. Finally, an optimal SVM model is established and trained for engine failure classification by using the fault feature vectors of the noise signals. Cross-validation results show that the proposed noise-based HHT–SVM method is accurate and effective for engine fault diagnosis. Due to outstanding time–frequency characteristics and pattern recognition capacity of the HHT and SVM, the newly proposed HHT–SVM can be used to deal with both the stationary and nonstationary signals, and even the transient ones. In the view of applications, the HHT–SVM technique may be suggested not only to detect the abnormal states of vehicle engines, but also to be extended to other fields for failure diagnosis in engineering.     

Envelope order tracking for fault detection in rolling element bearings

An envelope order tracking analysis scheme is proposed in the paper for the fault detection of rolling element bearing (REB) under varying-speed running condition. The developed method takes the advantages of order tracking, envelope analysis and spectral kurtosis. The fast kurtogram algorithm is utilized to obtain both optimal center frequency and bandwidth of the band-pass filter based on the maximum spectral kurtosis. The envelope containing vibration features of the incipient REB fault can be extracted adaptively. The envelope is re-sampled by the even-angle sampling scheme, and thus the non-stationary signal in the time domain is represented as a quasi-stationary signal in the angular domain. As a result, the frequency-smear problem can be eliminated in order spectrum and the fault diagnosis of REB in the varying-speed running condition of the rotating machinery is achieved. Experiments are conducted to verify the validity of the proposed method.     

一种云环境下的发动机健康管理系统及其应用

针对航空公司对大量飞机发动机进行健康管理的需求,通过建立发动机健康管理云端数据中心,建立了一种云环境下的民航发动机健康管理系统,该系统对于验证发动机故障诊断方法的有效性具有突出优势,并且对于实现多种方法协同进行发动机故障诊断具有重要价值;提出了一种基于灰色关联分析的灰色故障识别方法,通过在云端平台使用灰色故障识别方法实现JT9D-7R4发动机的典型气路性能故障诊断为例,表明云环境下的发动机健康管理系统可以有效地进行航空发动机故障诊断。     

Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy

The multi-disc wet clutch is widely used in transmission systems as it transfers the torque and power between the gearbox and the driving engine. During service, the buckling of the friction components in the wet clutch is inevitable, which can shorten the lifetime of the wet clutch and decrease the vehicle performance. Therefore, fault diagnosis and online monitoring are required to identify the buckling state of the friction components. However, unlike in other rotating machinery, the time-domain features of the vibration signal lack efficiency in fault diagnosis for the wet clutch. This paper aims to present a new fault diagnosis method based on multi-speed Hilbert spectrum entropy to classify the buckling state of the wet clutch. Firstly, the wet clutch is classified depending on the buckling degree of the disks, and then a bench test is conducted to obtain vibration signals of each class at varying speeds. By comparing the accuracy of different classifiers with and without entropy, Hilbert spectrum entropy shows higher efficiency than time-domain features for the wet clutch diagnosis. Thus, the classification results based on multi-speed entropy achieve even better accuracy.     

A Bearing Fault Diagnosis Method Based on PAVME and MEDE

When rolling bearings have a local fault, the real bearing vibration signal related to the local fault is characterized by the properties of nonlinear and nonstationary. To extract the useful fault features from the collected nonlinear and nonstationary bearing vibration signals and improve diagnostic accuracy, this paper proposes a new bearing fault diagnosis method based on parameter adaptive variational mode extraction (PAVME) and multiscale envelope dispersion entropy (MEDE). Firstly, a new method hailed as parameter adaptive variational mode extraction (PAVME) is presented to process the collected original bearing vibration signal and obtain the frequency components related to bearing faults, where its two important parameters (i.e., the penalty factor and mode center-frequency) are automatically determined by whale optimization algorithm. Subsequently, based on the processed bearing vibration signal, an effective complexity evaluation approach named multiscale envelope dispersion entropy (MEDE) is calculated for conducting bearing fault feature extraction. Finally, the extracted fault features are fed into the k-nearest neighbor (KNN) to automatically identify different health conditions of rolling bearing. Case studies and contrastive analysis are performed to validate the effectiveness and superiority of the proposed method. Experimental results show that the proposed method can not only effectively extract bearing fault features, but also obtain a high identification accuracy for bearing fault patterns under single or variable speed.     

某型飞机发动机故障诊断专家系统设计

针对某型飞机发动机故障诊断困难以及视情维修对维修技术提出的更高要求,利用专家系统人工智能技术设计了该型飞机发动机故障诊断专家系统。该系统利用自动检测技术获得发动机状态参数,通过智能诊断实现故障定位。系统利用模块化设计思想进行了人机交互界面设计、故障知识数据库建立、推理机制设计、获取知识程序设计、解释程序设计,实现了发动机故障的快速定位,提高了发动机诊断维修的时效性,保证了发动机的完好率。     

Diagnostic method based on the analysis of the vibration and acoustic emission energy for emergency diesel generators in nuclear plants

Ensuring the availability and reliability of the Emergency Diesel Generators (EDGs) in nuclear power plants is a critical aspect to guarantee the plant safe shutdown in case of an emergency scenario. An effective engine diagnosis method is essential to fulfill that goal. For that purpose, this paper presents a fast and automatic engine diagnosis method based on a single parameter: the vibration/AE signals energy. The method is based on the comparison of the vibration and AE energy with reference values to determine whether the engine condition is faulty. The method was applied in a test engine, and proved to work satisfactorily. Therefore, it was used to diagnose the EDGs in a nuclear power plant, where regular rigorous inspections are effected periodically. An injector fault was detected thanks to the diagnosis method.     

利用声信号对滚动轴承进行故障诊断的研究

旋转机械在运行过程中产生的声信号包含了滚动轴承的运行状态信息,且可采用非接触式测量,本文应用它对滚动轴承进行故障诊断。基于morlet小波变换的包络分析对采集的声信号进行降噪及包络处理,然后变换到频域,提取出特征频率并经过转换后作为线性神经网路的输入向量,辨识滚动轴承的状态。实验表明,本方法对滚动轴承故障诊断是有效的。     

运动声源多普勒畸变信号自适应校正方法EI北大核心CSCD

提出了一种自适应多普勒畸变校正方法,以声源移动速度v、初始时刻麦克风与声源横向距离x两个运动学参数为优化变量,以最大化重采样信号的频域统计指标为优化目标,通过参数寻优进行v和x的估计,通过幅值还原和时域插值拟合进行畸变校正。仿真分析结果表明,频谱峭度、频谱偏度、频谱脉冲因子和频谱峰值因子4种统计指标均能准确识别运动学参数,且频谱峭度的抗噪能力最好,临界信噪比达到-3.1 dB。实验分析结果表明,列车故障轴承多普勒畸变声音信号校正后,包络谱故障频率成分及其倍频成分清晰准确,说明多普勒畸变得到有效校正。该方法可基于信号本身实现多普勒畸变信号时频结构的全面校正。     

基于支持向量机的舰船图像识别

支持向量机(SVM)是一种基于超平面分类的新的学习方法,具有很强的泛化能力。研究了支持向量机的学习机理,以及实现支持向量机的序贯最小优化算法(SMO),并用来对舰船图像进行识别。首先将待识别目标进行二维小波分解,获取不同尺度下的小波系数,然后对其进行主元分析,得到的主元分量作为支持向量机的特征量输入。实验结果表明,该方法具有良好的分类性能。