An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network

This paper describes a fault diagnosis system for automotive generators using discrete wavelet transform (DWT) and an artificial neural network. Conventional fault indications of automotive generators generally use an indicator to inform the driver when the charging system is malfunction. But this charge indicator tells only if the generator is normal or in a fault condition. In the present study, an automotive generator fault diagnosis system is developed and proposed for fault classification of different fault conditions. The proposed system consists of feature extraction using discrete wavelet analysis to reduce complexity of the feature vectors together with classification using the artificial neural network technique. In the output signal classification, both the back-propagation neural network (BPNN) and generalized regression neural network (GRNN) are used to classify and compare the synthetic fault types in an experimental engine platform. The experimental results indicate that the proposed fault diagnosis is effective and can be used for automotive generators of various engine operating conditions.     

Speaker identification using discrete wavelet packet transform technique with irregular decomposition

This paper presents the study of speaker identification for security systems based on the energy of speaker utterances. The proposed system consisted of a combination of signal pre-process, feature extraction using wavelet packet transform (WPT) and speaker identification using artificial neural network. In the signal pre-process, the amplitude of utterances, for a same sentence, were normalized for preventing an error estimation caused by speakers’ change in volume. In the feature extraction, three conventional methods were considered in the experiments and compared with the irregular decomposition method in the proposed system. In order to verify the effect of the proposed system for identification, a general regressive neural network (GRNN) was used and compared in the experimental investigation. The experimental results demonstrated the effectiveness of the proposed speaker identification system and were compared with the discrete wavelet transform (DWT), conventional WPT and WPT in Mel scale.     

Fault diagnosis of spur bevel gear box using discrete wavelet features and Decision Tree classification

The wavelet transform (WT) is used to represent all possible types of transients in vibration signals generated by faults in a gear box. It is shown that the transform provides a powerful tool for condition monitoring and fault diagnosis. The vibration signal of a spur bevel gear box in different conditions is used to demonstrate the application of various wavelets in feature extraction. In present work, a discrete wavelet, Daubechies wavelets (db1–db15) is used for feature extraction and their relative effectiveness in feature extraction is compared. The major steps in pattern classification are feature extraction and classification. This paper investigates the use of discrete wavelets for feature extraction and a Decision Tree for classification. J48 Decision Tree algorithm has been used for feature selection as well as for classification. This paper illustrates the powerfulness and flexibility of the discrete wavelet transform to decompose linear and non-linear processing of vibration signal.     

小波分析算法研究及在齿轮与滚动轴承故障诊断中应用

分析了齿轮与滚动轴承故障振动信号的特征,利用小波变换的分解和重构算法,有效地提取出齿轮与滚动轴承故障特征信号,得到实验结果.通过比较频谱分析和小波分析的特点,有效地证明了小波分析在微弱故障信号提取中的优势.     

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.     

基于优选小波基和模糊SOM网络的模拟电路故障诊断

为了解决模拟电路故障诊断中的特征提取困难并实现对模拟电路故障模式准确的分类,提出一种优选小波基、模糊理论和自组织特征映射网络(SOM,self-organizing feature map)相结合的模拟电路故障诊断方法.该方法首先对模拟电路故障响应信号进行小波分解、提取能量值、均值和方差组成输入特征向量,同时采用余弦分离度评价小波变换在不同小波基函数下获取故障特征的有效性,据此选择余弦分离度最小的小波基分解的特征向量输入到自组织特征映射网络进行故障分类.仿真实验表明,利用余弦分离度选择的最优小波基能有效提高模拟电路故障特征提取,模糊神经网络能对故障模式进行精确分类.     

某型导弹测角仪故障检测的小波分析方法研究

现有的时频分析方法很难检测到某型导弹测角仪故障,针对这个问题,提出测角仪故障检测的连续和离散小波变换相结合的时频分析法.给出该方法提出的过程,分析连续和离散小波变换时烦分析法,将两种分析方法的特点结合起来共同分析测角仪故障时的信号信息.通过仿真实验证明,提出的方法能准确的提取测角仪故障时的信号特征,对测角仪故障的检测是...     

Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction

This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) model for estimation of vigilance level by using electroencephalogram (EEG) signals recorded during transition from wakefulness to sleep. The developed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. This study comprises of three stages. In the first stage, three types of EEG signals (alert signal, drowsy signal and sleep signal) were obtained from 30 healthy subjects. In the second stage, for feature extraction, obtained EEG signals were separated to its sub-bands using discrete wavelet transform (DWT). Then, entropy of each sub-band was calculated using Shannon entropy algorithm. In the third stage, the ANFIS was trained with the back-propagation gradient descent method in combination with least squares method. The extracted features of three types of EEG signals were used as input patterns of the three ANFIS classifiers. In order to improve estimation accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The performance of the ANFIS model was tested using the EEG data obtained from 12 healthy subjects that have not been used for the training. The results confirmed that the developed ANFIS classifier has potential for estimation of vigilance level by using EEG signals.     

短丝纤维卷绕牵伸齿轮箱故障信号的图像特征提取方法

针对短丝纤维卷绕牵伸齿轮箱故障信号不易提取的问题,提出了基于图像纹理信息的特征提取方法。通过对齿轮箱振动信号进行小波包双谱分析,获得具有稳定纹理信息的振动信号双谱图,采用基于小波变换对双谱图进行图像融合,提高图像的综合纹理特征。采用灰度共生矩阵的四个特征参数对振动信号的双谱图进行加权融合特征提取。在短丝生产线上对齿轮箱常见的齿轮破损和裂纹进行了实验分析,结果表明本文方法的故障识别率达到85%以上。     

Faulted gear identification of a rotating machinery based on wavelet transform and artificial neural network

In this paper, a condition monitoring and faults identification technique for rotating machineries using wavelet transform and artificial neural network is described. Most of the conventional techniques for condition monitoring and fault diagnosis in rotating machinery are based chiefly on analyzing the difference of vibration signal amplitude in the time domain or frequency spectrum. Unfortunately, in some applications, the vibration signal may not be available and the performance is limited. However, the sound emission signal serves as a promising alternative to the fault diagnosis system. In the present study, the sound emission of gear-set is used to evaluate the proposed fault diagnosis technique. In the experimental work, a continuous wavelet transform technique combined with a feature selection of energy spectrum is proposed for analyzing fault signals in a gear-set platform. The artificial neural network techniques both using probability neural network and conventional back-propagation network are compared in the system. The experimental results pointed out the sound emission can be used to monitor the condition of the gear-set platform and the proposed system achieved a fault recognition rate of 98% in the experimental gear-set platform.     

The speaker identification by using genetic wavelet adaptive network based fuzzy inference system

In this paper, an intelligent speaker identification system is presented for speaker identification by using speech/voice signal. This study includes both combination of the adaptive feature extraction and classification by using optimum wavelet entropy parameter values. These optimum wavelet entropy values are obtained from measured Turkish speech/voice signal waveforms using speech experimental set. It is developed a genetic wavelet adaptive network based on fuzzy inference system (GWANFIS) model in this study. This model consists of three layers which are genetic algorithm, wavelet and adaptive network based on fuzzy inference system (ANFIS). The genetic algorithm layer is used for selecting of the feature extraction method and obtaining the optimum wavelet entropy parameter values. In this study, one of the eight different feature extraction methods is selected by using genetic algorithm. Alternative feature extraction methods are wavelet decomposition, wavelet decomposition – short time Fourier transform, wavelet decomposition – Born–Jordan time–frequency representation, wavelet decomposition – Choi–Williams time–frequency representation, wavelet decomposition – Margenau–Hill time–frequency representation, wavelet decomposition – Wigner–Ville time–frequency representation, wavelet decomposition – Page time–frequency representation, wavelet decomposition – Zhao–Atlas–Marks time–frequency representation. The wavelet layer is used for optimum feature extraction in the time–frequency domain and is composed of wavelet decomposition and wavelet entropies. The ANFIS approach is used for evaluating to fitness function of the genetic algorithm and for classification speakers. It has been evaluated the performance of the developed system by using noisy Turkish speech/voice signals. The test results showed that this system is effective in detecting real speech signals. The correct classification rate is about 91% for speaker classification.     

S能量谱特征提取在轴承故障诊断中的应用

准确提取振动信号的特征,是滚动轴承故障检测的关键问题,为此提出一种基于S能量谱特征提取的故障诊断方法。该方法对振动信号进行S变换,得到时频矩阵,并构建S能量谱,对S能量谱进行奇异值分解（Singular Value Decomposition,SVD）分析,得到能够反映S能量谱特征的奇异值,利用变量预测模型（Variable Predictive Model based Class Discriminate,VPMCD）方法,通过建立特征值之间的内在关系,构建故障识别模型。将所提方法应用于滚动轴承故障检测,实验结果表明,S能量谱特征提取轴承故障诊断方法具有较高的正判率。     

基于EEMD-Treelet和高斯过程的起重机齿轮故障诊断

齿轮故障诊断对于起重机安全运行至关重要;提出了一种基于集成经验模态分解(ensemble empirical mode decomposition, EEMD)-Treelet变换和高斯过程(gaussian process, GP)的起重机齿轮振动故障诊断新方法;设计一种细菌觅食算法(bacterial foraging optimization, BFO)优化高斯过程模型超参数;建立基于集成经验模态分解-希尔伯特变换的齿轮振动参数信号特征提取方法,利用Treelet变换实现这些特征的降维学习;建立基于细菌觅食算法优化高斯过程的齿轮故障模型;实验结果表明,EEMD-Treelet-GP诊断方法不仅可以识别最佳特征向量,而且可以识别故障位置。     

基于双树复小波变换—最大熵谱估计和参数优化概率神经网络的齿轮箱故障诊断研究

齿轮振动信号具有非平稳性和非线性的特点。为了准确提取其故障特征并进行故障诊断,提出一种基于双树复小波变换(DTCWT)-最大熵谱估计(MESE)和惯性权重线性递减粒子群优化(LDWPSO)算法-参数优化概率神经网络(PNN)的齿轮故障诊断方法。首先,利用DTCWT把状态已知的齿轮振动信号分解为不同频带的模态分量。其次,采用MESE得到每个分量的最小偏差频谱估计,计算出不同频段的能量熵作为故障特征矩阵。然后利用LDWPSO算法寻找出最优神经网络参数——平滑因子。最后,将故障特征矩阵输入优化后的PNN模型,建立起故障特征和齿轮运行状况之间的数值化映射关系,进而完成齿轮故障诊断模型。经试验数据分析表明,采用提出的DTCWT处理齿轮的振动信号,并引入MESE处理关键分量,可以提取稳定的信号特征并降低噪声干扰。另外,相比于传统的PNN,基于改进的PNN的齿轮故障状态的数值化判别具有更高的诊断精度和稳定性。     

大型装备传动系统非线性频谱特征提取与故障诊断

基于Volterra级数的非线性频谱分析方法,建立了大型数控装备传动系统伺服电机的非线性频谱模型,对传动系统两类参数型故障的频谱特征进行了分析.在此基础上,提出一种实用的在线频谱特征提取与故障识别方法,采用自适应辨识算法求解时域Volterra核,用快速多维傅立叶变换获得非线性频谱特征.实验结果表明,该方法实时性好,故障识别率高.     

基于WP-ICA及SVM的齿轮故障诊断研究

针对旋转机械设备齿轮故障诊断问题,为全面提取反映齿轮运行状态的特征信息,提出了基于WP(小波包)与ICA(独立成分分析)相融合的特征提取及SVM(支持向量机)相适配的故障诊断方法。用小波包对信号进行分析并提取其能量特征,采用独立成分分析方法对提取的能量特征进一步优化,进而得到反映齿轮运行状态的特征向量。最后采用支持向量机对齿轮运行状态的四种类型(正常、轻微故障、中等故障、断齿故障)进行诊断评估。通过纵向比较和横向比较研究表明,所提特征提取方法较单一的小波包特征提取方法更能全面反映齿轮状态信息。采用SVM方法进行齿轮故障模式诊断,较其它方法具有更高的分类准确率,达到了很好的诊断效果。     

An intelligent diagnosis system based on principle component analysis and ANFIS for the heart valve diseases

In this paper, an intelligent diagnosis system based on principle component analysis (PCA) and adaptive network based on fuzzy inference system (ANFIS) for the heart valve disease is introduced. This intelligent system deals with combination of the feature extraction and classification from measured Doppler signal waveforms at the heart valve using the Doppler ultrasound (DHS). Here, the wavelet entropy is used as features. This intelligent system has three phases. In pre-processing phase, the data acquisition and pre-processing for DHS signals are performed. In feature extraction phase, the feature vector is extracted by calculating the 12 wavelet entropy values for per DHS signal and dimension of Doppler signal dataset, which are 12 features, is reduced to 6 features using PCA. In classification phase, these reduced wavelet entropy features are given to inputs ANFIS classifier. The correct diagnosis performance of the PCA–ANFIS intelligent system is calculated in 215 samples. The classification accuracy of this PCA–ANFIS intelligent system was 96% for normal subjects and 93.1% for abnormal subjects.     

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。     

一种有效的齿轮故障识别方法

针对齿轮故障特征信息往往被信号中的噪声淹没的问题,提出了一种基于谐波小波包、样本熵和灰色关联度的齿轮故障识别方法。首先,采用顺序形态滤波器,并结合实际选用最简单的直线结构元素,对实测齿轮振动信号进行顺序形态滤波降噪预处理。然后,采用谐波小波包将不同故障的齿轮振动信号分解到3层共8个频带上,并计算各频带的样本熵。最后,以样本熵为元素构造特征向量,通过计算标准故障模式特征向量与待识别样本的灰色关联度来判断齿轮的工作状态和故障类型。试验结果表明,该方法能够有效地应用于齿轮系统的故障诊断。     

基于有理双树复小波和SVM的滚动轴承故诊断方法

滚动轴承故障类型被支持向量机(SVM)智能识别的关键是故障特征的提取。为了提取最优的故障特征,提高SVM的分类识别精度,提出了基于有理双树复小波和SVM的滚动轴承故障诊断方法。首先通过双树复小波包变换将非平稳的振动信号分解得到不同频带的分量,然后对每个分量求能量并作归一化处理,最后将从各个频带分量中提取的能量特征参数作为支持向量机的输入来识别滚动轴承的故障类型。研究结果表明该方法可以有效、准确地识别轴承的故障模式。