Wavelet neural network based fault diagnosis in nonlinear analog circuits

The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studled. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The fault dictionary of nonlinear circuits is constructed based on improved back-propagation（BP） neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification and deducibility.

Wavelet Transform and Neural Networks in Fault Diagnosis of a Motor Rotor

In the motor fault diagnosis technique, vibration and stator current frequency components of detection are two main means. This article will discuss the signal detection method based on vibration fault. Because the motor vibration signal is a non-stationary random signal, fault signals often contain a lot of time-varying, burst proper- ties of ingredients. The traditional Fourier signal analysis can not effectively extract the motor fault characteristics, but are also likely to be rich in failure information but a weak signal as noise. Therefore, we introduce wavelet packet transforms to extract the fault characteristics of the signal information. Obtained was the result as the neural network input signal, using the L-M neural network optimization method for training, and then used the BP net- work for fault recognition. This paper uses Matlab software to simulate and confirmed the method of motor fault di- agnosis validity and accuracy

Study on Power Transformers Fault Diagnosis Based on Wavelet Neural Network and D-S Evidence Theory

>Transformer faults are quite complicated phenomena and can occur due to a variety of reasons.There have been several methods for transformer fault synthetic diagnosis,but each of them has its own limitations in real fault diagnosis applications.In order to overcome those shortcomings in the existing methods,a new transformer fault diagnosis method based on a wavelet neural network optimized by adaptive genetic algorithm(AGA)and an improved D-S evidence theory fusion technique is proposed in this paper.The proposed method combines the oil chromatogram data and the off-line electrical test data of transformers to carry out fault diagnosis.Based on the fusion mechanism of D-S evidence theory,the comprehensive reliability of evidence is constructed by considering the evidence importance,the outputs of the neural network and the expert experience.The new method increases the objectivity of the basic probability assignment(BPA)and reduces the basic probability assigned for uncertain and unimportant information.The case study results of using the proposed method show that it has a good performance of fault diagnosis for transformers.

Feature evaluation and extraction based on neural network in analog circuit fault diagnosis

Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently. The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency. A fault diagnosis illustration validated this method.

Combinatorial Optimization Based Analog Circuit Fault Diagnosis with Back Propagation Neural Network

Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit,a combinatorial optimization diagnosis scheme was proposed with back propagation( BP) neural network( BPNN).The main contributions of this scheme included two parts:( 1) the random tolerance samples were added into the nominal training samples to establish new training samples,which were used to train the BP neural network based diagnosis model;( 2) the initial weights of the BP neural network were optimized by genetic algorithm( GA) to avoid local minima,and the BP neural network was tuned with Levenberg-Marquardt algorithm( LMA) in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability,and effectively promotes analog circuit fault diagnosis performance based on BPNN.

Fault Diagnosis of Analog Circuit Based on PSO and BP Neural Network

In order to improve the speed and accuracy of analog circuit fault diagnosis,using Back Propagation Neural Network(BPNN),a new method is proposed based on Particle Swarm Optimization(PSO)to adjust weights of BP neural network.The model can not only overcome the limitations of the slow convergence and the local extreme values by basic BP algorithm,but also improve the learning ability and generalization ability with a higher precision.The response signals of analog circuit is preprocessed by Wavelet Packet Transform(WPT)as the fault feature.The simulation result shows that the proposed method has higher diagnostic accuracy and faster convergence speed,which is effective for fault location.

Fault Diagnosis of Valve Clearance in Diesel Engine Based on BP Neural Network and Support Vector Machine

Based on wavelet packet transformation(WPT), genetic algorithm(GA), back propagation neural network(BPNN)and support vector machine(SVM), a fault diagnosis method of diesel engine valve clearance is presented. With power spectral density analysis, the characteristic frequency related to the engine running conditions can be extracted from vibration signals. The biggest singular values(BSV)of wavelet coefficients and root mean square(RMS)values of vibration in characteristic frequency sub-bands are extracted at the end of third level decomposition of vibration signals, and they are used as input vectors of BPNN or SVM. To avoid being trapped in local minima, GA is adopted. The normal and fault vibration signals measured in different valve clearance conditions are analyzed. BPNN, GA back propagation neural network(GA-BPNN), SVM and GA-SVM are applied to the training and testing for the extraction of different features, and the classification accuracies and training time are compared to determine the optimum fault classifier and feature selection. Experimental results demonstrate that the proposed features and classification algorithms give classification accuracy of 100%.

Soft Fault Diagnosis for Analog Circuits Based on Slope Fault Feature and BP Neural Networks

Fault diagnosis is very important for development and maintenance of safe and reliable electronic circuits and systems. This paper describes an approach of soft fault diagnosis for analog circuits based on slope fault feature and back propagation neural networks (BPNN). The reported approach uses the voltage relation function between two nodes as fault features; and for linear analog circuits, the voltage relation function is a linear function, thus the slope is invariant as fault feature. Therefore, a unified fault feature for both hard fault (open or short fault) and soft fault (parametric fault) is extracted. Unlike other NN-based diagnosis methods which utilize node voltages or frequency response as fault features, the reported BPNN is trained by the extracted feature vectors, the slope features are calculated by just simulating once for each component, and the trained BPNN can achieve all the soft faults diagnosis of the component. Experiments show that our approach is promising.

Research method of circuit fault diagnosis based on FCM

Using fuzzy C cluster mean (FCM), fuzzy theory and neural network, a fault diagnosis method was proposed, which was based on fuzzy C-means clustering algorithm of neural network that was applied in non-linear analog circuits and in diagnoses the ARNIC 429 reception circuit of aviation aircraft avionics. The C cluster algorithm can make the amount of the fuzzy rule automatically and can create an initial fuzzy rule database of fault diagnosis. A type of fuzzy neural network and a fault tree were generated. The algorithm avoids the disadvantage that gets into the part of optimum circumstance. A validate application was implemented, which proves that the method is effective. Therefore, the method is superior to the traditional methods in fault diagnosis, and the efficiency is heavily improved.

Hierarchical Neural Networks Method for Fault Diagnosis of Large-Scale Analog Circuits

A novel hierarchical neural networks (HNNs) method for fault diagnosis of large-scale circuits is proposed. The presented techniques using neural networks(NNs) approaches require a large amount of computation for simulating various faulty component possibilities. For large scale circuits, the number of possible faults, and hence the simulations, grow rapidly and become tedious and sometimes even impractical. Some NNs are distributed to the torn sub-blocks according to the proposed torn principles of large scale circuits. And the NNs are trained in batches by different patterns in the light of the presented rules of various patterns when the DC, AC and transient responses of the circuit are available. The method is characterized by decreasing the over-lapped feasible domains of responses of circuits with tolerance and leads to better performance and higher correct classification. The methodology is illustrated by means of diagnosis examples.

基于同步挤压小波变换和Transformer的轴承故障诊断模型

针对采用神经网络对滚动轴承进行故障诊断时,故障信息利用不充分,特征提取困难的问题,提出了一种基于同步挤压小波变换(SST)-Transformer的滚动轴承智能故障诊断方法。首先,以同步挤压小波变换作为信号处理模块,将一维振动信号转为时频图;接着,设计了一种最大程度保留故障信息的时频图分割方式,将时频图分割为一系列图像块序列;然后,将序列输入到具有强大的处理序列数据能力的Transformer模型中,进行了特征提取;最后,将特征数据输入分类器进行了分类,对比了不同的时频图分割方式的诊断效果,并将SST-Transformer模型与基准算法相比较。研究结果表明:相较于其他分割方式,基于SST-Transformer的滚动轴承智能故障诊断方法的诊断准确率提升了3.45%,并大幅提升了模型训练的收敛速度;相比于其他基准算法,该方法的平均准确率至少提升了1.05%。该方法有较高的诊断准确率和较好的稳定性。

A Wavelet and Neural Networks Based on Fault Diagnosis for HAGC System of Strip Rolling Mill

The fault diagnosis of HAGC （Hydraulic Gauge Control） system of strip rolling mill is researched. Taking the advantage of the accompanying characteristics of the closed loop control system, rolling force forecasting model is built based on neural networks. The comparison results of the prediction and the actual signal are taken as residual signals. Wavelet transform is used to obtain the components of high and low frequency of the residual signal. Wave let decomposition results make fault feature clear and time-domain positioning accurately. Fault numerical criterion is established through Lipschitz exponent. By analyzing the varied fault features which correspond to varied fault rea sons, the fault diagnosis of HAGC system is implemented successfully.

A Neural Network Appraoch to Fault Diagnosis in Analog Circuits

This paper presents a neural network based fault diagnosis approach for analog circuits, taking the tolerances of circuit elements into account. Specifi-cally, a normalization rule of input information, a pseudo-fault domain border (PFDB) pattern selection method and a new output error function are proposed for training the backpropagation (BP) network to be a fault diagnoser. Experi-mental results demonstrate that the diagnoser performs as well as or better than any classical approaches in terms of accuracy, and provides at Ieast an order-of magnitude improvement in post-fault diagnostic speed.

Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

A novel approach by introducing a statistical parameter to estimate the severity of incipient stator inter-turn short circuit(ITSC)faults in induction motors(IMs)is proposed.Determining the incipient ITSC fault and its severity is challenging for several reasons.The stator currents in the healthy and faulty cases are highly similar during the primary stage of the fault.Moreover,the conventional statistical parameters resulting from the analysis of fault signals do not consistently show a systematic variation with respect to the increase in fault intensity.The objective of this study is the early detection of incipient ITSC faults.Furthermore,it aims to determine the percentage of shorted turns in the faulty phase,which acts as an indicator for severe damage to the stator winding.Modeling of the motor in healthy and defective cases is performed using the Clarke Concordia transform.A discrete wavelet transform is applied to the motor currents using a Daubechies-8 wavelet.The statistical parameters L1 and L2 norms are computed for the detailed coefficients.These parameters are obtained under a variety of loads and defects to acquire the most accurate and generalized features related to the fault.Combining L1 and L2 norms creates a novel statistical parameter with notable characteristics to achieve the research aim.An artificial neural network-based back propagation algorithm is employed as a classifier to implement the classification process.The classifier output defines the percentage of defective turns with a high level of accuracy.The competency of the adopted methodology is validated via simulations and experiments.The results confirm the merits of the proposed method,with a classification test correctness of 95.29%.

基于短时傅里叶变换和深度网络的模块化多电平换流器子模块IGBT开路故障诊断

针对现有模块化多电平换流器(MMC)子模块故障诊断过程中所需传感器较多、测量干扰较大等问题,提出一种基于深度学习的MMC子模块IGBT开路故障诊断方法。在对MMC子模块开路故障特征进行分析的基础上,利用短时傅里叶变换(STFT)提取桥臂电压信号的谐波分量信息作为故障诊断所需的特征参数。将所得到的特征参数进行处理后构建故障诊断样本,在通过深度置信网络实现故障类型快速检测的基础上,依据不同故障类型,构建多个基于卷积神经网络的故障定位网络,进而实现开路故障的检测与定位。通过129电平的MMC系统仿真模型和降功率的MMC实验系统搭建,对该文所提方法进行了验证。仿真和实验结果表明,所提故障诊断方法可以在减少传感器数量的基础上实现子模块开路故障的诊断,提高系统的可靠性。

基于CWT-RES34的风电机组叶片裂纹状态评估

为有效进行风电机组叶片运行时的裂纹状态评估,提出一种基于连续小波变换(Continue Wavelet Transform,CWT)和残差神经网络(Residual Networks,ResNet)结合的叶片裂纹状态评估方法。首先对叶片加速度振动信号做CWT后生成二维彩色时频图像,然后将图像分别作为训练集和测试集,使用34层ResNet进行训练和诊断,最后选取天津某风电场提供的1.5 MW风力发电机作为研究对象,根据其样本数据将叶片故障程度按照裂纹长度和宽度分为健康、轻微、中等、严重、危险5种状态,评估平均准确率高达98.23%,方法的有效性和可行性得到验证。

基于IHHO-BP神经网络的模拟电路故障诊断

针对模拟电路故障类型多、故障状态不稳定以及故障数据冗余,使得模拟电路故障诊断困难的问题,提出利用改进哈里斯鹰算法(improved Harris Hawks optimization, IHHO)优化反向传播(back propagation, BP)神经网络,实现模拟电路故障特征选择与诊断。首先,将非线性自适应因子、柯西变异和随机差分扰动引入哈里斯鹰算法,实现收敛速度和精度的提升;其次,采用IHHO对模拟电路的单一故障和组合故障仿真数据进行特征选择,完成数据预处理;最后,采用IHHO-BP算法,对预处理后的故障数据进行训练和测试,实现模拟电路故障诊断。诊断结果表明,所提方法的诊断精度相较于其他算法提升了5.5%。

基于GADF-CWT-GCNN的滚动轴承故障诊断方法研究

针对滚动轴承故障诊断在小样本环境下引起的模型泛化能力差、诊断精度低的问题,提出一种基于格拉姆角分场(GADF)和连续小波变化(continuous wavelet transform,CWT)与并行二维组归一化卷积神经网络(parallel convolutional neural network,P2D-GCNN)的滚动轴承故障诊断方法。对采集的数据进行预处理,采用格拉姆角场和连续小波变换将一维振动信号转换成二维图像作为模型输入,再选用数据增强技术扩充样本子图,满足网络输入要求,并将其导入搭建的组归一化卷积神经网络中进行诊断检测。结果表明:文中数据处理方法与搭建模型在小样本环境下泛化能力远高于SVM和1D-CNN等其他网络模型。为进一步验证模型在小样本数据下的识别能力,取数据集的70%,40%和20%样本量进行多次实验,所对应的训练准确率及测试准确率分为99.38%,99.02%,99.47%,98.29%,99.05%,97.08%。结果证明,文中模型在小样本环境下对轴承故障诊断具有很高的准确率。

基于定子电流和电磁转矩双信号融合的齿轮故障智能诊断

在电机驱动的齿轮传动系统中,电机本体具有传感器的特性,因此可以通过电机的定子电流、电磁转矩信号来进行齿轮故障分析,由于受转速和负载转矩的影响,使得故障诊断结果的准确率较低。针对此问题,提出一种基于双信号融合与反向传播神经网络相结合的齿轮故障诊断方法。对电机齿轮传动系统一体化建模,进行电机齿轮传动系统联合仿真。对齿轮的不同故障进行模拟,得到电机侧定子电流和电磁转矩的故障信号,采用双树复小波变换来分析齿轮故障频段信号,提取故障特征量,建立了丰富的齿轮故障样本库。搭建反向传播神经网络并提出改进的自适应学习率算法,实现了对齿轮断齿、磨损故障的精确分类。为了验证所提方法的有效性,搭建齿轮故障试验平台,对相应齿轮故障进行诊断。结果表明,所提方法能够在不同转速和负载转矩条件下准确辨识齿轮的故障类型,相较于只采用定子电流和电磁转矩中一种信号对齿轮进行故障诊断,该方法准确率更高。

基于d-q变换及WOA-LSTM的异步电机定子匝间短路故障诊断方法

为了实现对异步电机定子绕组匝间短路故障的可靠在线诊断,提出一种基于d-q变换及鲸鱼优化算法(WOA)优化的长短期记忆网络(LSTM)的故障诊断方法。通过理论推导可知,d-q变换可有效提取定子电流中的特征频谱数据。采用鲸鱼优化算法对长短期记忆网络中的3个关键参数进行优化,建立WOA-LSTM故障分类模型。为了验证基于d-q变换和WOA-LSTM故障诊断方法的有效性,分别以小波变换、快速傅里叶变换及d-q变换提取电流频谱数据作为输入数据集,以一台YE2-100L1-4型异步电机为实验对象进行实验验证。研究结果表明:相比于小波变换及快速傅里叶变换,采用d-q变换能更准确的提取出定子电流中的故障特征,更精确地反映电机故障状态,有助于提高故障分类准确率;相比于传统的LSTM算法,经WOA优化后的LSTM算法分类准确率可达98.3%,能可靠地实现不同程度匝间短路故障的诊断。