A self-adaptive data analysis for fault diagnosis of an automotive air-conditioner blower

This paper presents a fault diagnosis system for an automotive air-conditioner blower based on a noise emission signal using a self-adaptive data analysis technique. The proposed diagnosis system consists of feature extraction using the empirical mode decomposition (EMD) method and fault classification using the artificial neural network technique. The EMD method has been developed quite recently to adaptively decompose the non-stationary and non-linear signals. It sifts the complex signal of time series without losing its original properties and then obtains some useful intrinsic mode function (IMF) components. Calculating the energy of each component can reduce the computation dimensions and enhance classification performance. These energy features of various fault conditions are used as inputs to train the artificial neural network. In the fault classification, the probabilistic neural network (PNN) is used to verify the performance of the proposed system and compare with the traditional technique, back-propagation neural network (BPNN). The experimental results indicated the proposed technique performed well for quickly and accurately estimating fault conditions.     

An engine fault diagnosis system using intake manifold pressure signal and Wigner–Ville distribution technique

This paper proposed an engine fault diagnosis system based on intake manifold pressure signal and artificial neural network with the Wigner–Ville distribution technique. Traditionally, the engine diagnostic method depends on the experience of the technician, but some faults might be inaccurately judged by the technician’s experience when the engine is operating. In the present study, an engine platform diagnosis system using intake manifold pressure was developed. The algorithm of the proposed system consisted of Wigner–Ville distribution (WVD) for feature extraction and the neural network technique for fault classification. In previous work, the Wigner–Ville distribution was often used to analyze the non-stationary signal, because it provides a simple and clear energy spectrum diagram both in the time and frequency domains. This instantaneous energy diagram presented the magnitude of each engine fault under various operating conditions. The Wigner–Ville distribution extracts these features as database input to a neural network and the neural network is used to develop the training and testing modules. To prove the efficiency of the neural network, both the radial basis function neural network and generalized regression neural network are used and compared. The experimental results demonstrated the proposed system is effective and the performance is satisfactory.     

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.     

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.     

A study of fault diagnosis in a scooter using adaptive order tracking technique and neural network

An expert system for scooter fault diagnosis using sound emission signals based on adaptive order tracking and neural networks is presented in this paper. The order tracking technique is one of the important approaches for fault diagnosis in rotating machinery. The different faults present different order figures and they can be used to determine the fault in mechanical systems. However, many breakdowns are hard to classify correctly by human experience in fault diagnosis. In the present study, the order tracking problem is treated as a parametric identification and the artificial neural network technique for classifying faults. First, the adaptive order tracking extract the order features as input for neural network in the proposed system. The neural networks are used to develop the training module and testing module. The artificial neural network techniques using a back-propagation network and a radial basis function network are proposed to develop the artificial neural network for fault diagnosis system. The performance of two techniques are evaluated and compared through experimental investigation. The experimental results indicated that the proposed system is effective for fault diagnosis under various engine conditions.     

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.     

An expert system for fault diagnosis in internal combustion engines using probability neural network

An expert system for fault diagnosis in internal combustion engines using adaptive order tracking technique and artificial neural networks is presented in this paper. The proposed system can be divided into two parts. In the first stage, the engine sound emission signals are recorded and treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with a high-resolution adaptive filter algorithm. The vital features of signals with various fault conditions are obtained and displayed clearly by order figures. Then the sound energy diagram is utilized to normalize the features and reduce computation quantity. In the second stage, the artificial neural network is used to train the signal features and engine fault conditions. In order to verify the effect of the proposed probability neural network (PNN) in fault diagnosis, two conventional neural networks that included the back-propagation (BP) network and radial-basic function (RBF) network are compared with the proposed PNN network. The experimental results indicated that the proposed PNN network achieved the best performance in the present fault diagnosis system.     

基于极限学习机的船舶柴油机故障诊断

针对传统深度核极限学习机网络仅利用端层特征进行分类导致特征不全面，以及故障诊断分类器中核函数选择不恰当等问题，提出基于多层特征表达和多核极限学习机的船舶柴油机故障诊断方法。利用深度极限学习机网络提取故障数据的多层特征；将提取出的各层特征级联为一个具有多属性特征的故障数据特征向量；使用多核极限学习机分类器准确地实现柴油机的故障诊断。在标准分类数据集和船舶柴油机仿真故障数据集上的实验结果表明，与其他极限学习机算法相比，该方法能够有效提高故障诊断的准确率和稳定性，且具有较好的泛化性能，是柴油机故障诊断一个更为优秀实用的工具。     

Fault diagnosis of rolling bearing based on back propagation neural network optimized by cuckoo search algorithm

In order to improve the accuracy of rolling bearing fault diagnosis in mechanical equipment, a new fault diagnosis method based on back propagation neural network optimized by cuckoo search algorithm is proposed. This method use the global search ability of the cuckoo search algorithm to constantly search for the best weights and thresholds, and then give it to the back propagation neural network. In this paper, wavelet packet decomposition is used for feature extraction of vibration signals. The energy values of different frequency bands are obtained through wavelet packet decomposition, and they are input as feature vectors into optimized back propagation neural network to identify different fault types of rolling bearings. Through the three sets of simulation comparison experiments of Matlab, the experimental results show that, Under the same conditions, compared with the other five models, the proposed back propagation neural network optimized by cuckoo search algorithm has the least number of training iterations and the highest diagnostic accuracy rate. And in the complex classification experiment with the same fault location but different bearing diameters, the fault recognition correct rate of the back propagation neural network optimized by cuckoo search algorithm is 96.25%.

     

Momentum-based wavelet and double wavelet neural networks for power system applications

In order to minimize the power loss and to control the voltage in the power systems, the proposed momentum-based wavelet neural network and proposed momentum-based double wavelet neural network are proposed in this paper. The training data are obtained by using linear programming method by solving several abnormal conditions. The control variables considered are generator voltages and transformer taps, and the dependent variables are generator reactive powers and load bus voltages. The IEEE 14-bus system and IEEE 30-bus system are tested using the linear programming, Levenberg–Marquardt artificial neural network, proposed momentum-based wavelet neural network and proposed momentum-based double wavelet neural network to validate the effectiveness of the proposed MDWNN method. The trained neural networks are capable of controlling the voltage, and reactive power in power systems is proved by the results with the high level of precision and speed.

     

基于模式识别的传感器故障诊断

为满足模式识别故障诊断算法的鲁棒性要求,在小波包分解提取特征向量的基础上,提出了有监督模式分类与无监督模式分类相结合的故障诊断方法．利用小波包分解提取各个频带的能量作为特征向量;采用LVQ神经网络作为有监督的模式分类器进行故障诊断;运用无监督的减法聚类方法对新型故障模式进行辨识．最后,通过动力系统管路流量传感器数据对算法进行检验,验证了所提出方法的实用性和有效性．     

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.     

A neuro-wavelet based approach for diagnosing bearing defects

In recent years advanced signal processing techniques are used increasingly to excavate the nonstationary vibration signals and extract elemental-fault information. However, managing and analyzing a multicomponent signal mixed with background noise using only a single analysis tool is not a simple task and may lead to low diagnostic accuracy and a delayed diagnosis. This paper introduces a novel intelligent neuro-wavelet based system with high diagnostic accuracy based on nonrecursive variational mode decomposition (VMD) and wavelet-based neural network, which mainly consists of three steps (i.e. feature extraction (FE), dimension reduction (DR), and fault classification). Firstly, the vibration signals are segmented and processed by a novel nonrecursive VMD, which can decompose the nonstationary signals into a series of discrete modes adaptively, to extract informative features from vibration signals. Multi-Class generalized discriminant analysis is then used in the second step that aims to reduce the dimension of the feature set and improve the computational burden by selecting meaningful information and removing redundant features. In the next step, the obtained features vector is fed to a state-of-the-art hierarchical multi-resolution classifier, so-called wavelet neural network (WNN), which possesses the advantages of both wavelet transform and artificial neural networks for the decision-making. Additionally, to evaluate the information extraction capability of VMD, the subsequent DR method and the calculation accuracy of WNN, other state-of-the-art techniques are used in this work. In this regard, the superiority of the proposed approach is also confirmed through an experimental comparison with published works in the literature.     

Aero-Engine Surge Fault Diagnosis Using Deep Neural Network

Deep learning techniques have outstanding performance in feature extraction and model fitting. In the field of aero-engine fault diagnosis, the introduction of deep learning technology is of great significance. The aero-engine is the heart of the aircraft, and its stable operation is the primary guarantee of the aircraft. In order to ensure the normal operation of the aircraft, it is necessary to study and diagnose the faults of the aero-engine. Among the many engine failures, the one that occurs more frequently and is more hazardous is the wheeze, which often poses a great threat to flight safety. On the basis of analyzing the mechanism of aero-engine surge, an aero-engine surge fault diagnosis method based on deep learning technology is proposed. In this paper, key sensor data are obtained by analyzing different engine sensor data. An aero-engine surge dataset acquisition algorithm (ASDA) is proposed to sample the fault and normal points to generate the training set, validation set and test set. Based on neural network models such as one-dimensional convolutional neural network (1D-CNN), convolutional neural network (RNN), and long-short memory neural network (LSTM), different neural network optimization algorithms are selected to achieve fault diagnosis and classification. The experimental results show that the deep learning technique has good effect in aero-engine surge fault diagnosis. The aero-engine surge fault diagnosis network (ASFDN) proposed in this paper achieves better results. Through training, the network achieves more than 99% classification accuracy for the test set.     

小波神经网络对风力发电机齿轮箱的故障诊断

为研究风力发电机组齿轮箱的故障特性,提高其工作的可靠性,提出采用小波神经网络对齿轮箱的故障进行诊断的方案。该方案采用小波包分析与径向基函数(RBF)神经网络相结合组成小波神经网络,以准确地识别风力发电机组中齿轮箱常见的故障。诊断结果证明了方案的可行性。该方案在风力发电机组齿轮箱故障诊断领域具有良好的实用前景。     

基于共轭先验分布的贝叶斯网络分类模型

针对贝叶斯网络后验概率需计算样本边际分布,计算代价大的问题,将共轭先验分布思想引入贝叶斯分类,提出了基于共轭先验分布的贝叶斯网络分类模型.针对非区间离散样本,提出一种自适应的样本离散方法,将小波包提取模拟电路故障特征离散化作为分类模型属性.仿真验证表明,模型分类效果较好,算法运行速度得以提高,也可应用于连续样本和多分类的情况,扩展了贝叶斯网络分类的应用范围.     

轴承故障融合诊断方法

针对传统人工神经网络在故障诊断中应用的局限性 ,提出一种基于小波变换、遗传算法与神经网络的融合故障诊断方法。该方法先用小波变换对原始采样信号进行特征提取 ,再用遗传算法优化选择最为重要的特征作为神经网络的输入参数。最后 ,由神经网络进行状态识别和特征分类。这样不仅减少网络训练时间 ,降低网络计算量 ,而且有效提高分类的准确性及故障诊断的可靠性。轴承故障诊断实验结果表明 ,该方法是有效的。     

基于局部线性嵌入算法的柴油机故障诊断研究

为提高柴油机故障诊断准确率和效率,提出了改进局部线性嵌入算法的柴油机诊断系统。应用小波包能量谱分析方法提取某柴油机振动信号的特征值,将提取的高维特征向量映射到低维空间上,能将高维特征向量进行优化,即特征值的二次提取。该改进算法可模糊化近邻点k的选择,从而提高计算的速度,并应用SOM-BP神经网络进行故障识别。实验表明,经过局部线性嵌入算法的特征值优化,能减少SOM-BP神经网络的输入节点,可在一定程度上提高故障识别的效率和准确率。     

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.     

基于神经网络的大规模模拟电路故障检测系统

设计了一个基于小波和神经网络的信号处理系统，该系统主要针对大规模模拟电路故障检测。针对传统诊断技术的局限性，讨论了利用神经网络方法分级诊断大规模模拟电路软故障的方案，通过小波变换提取故障特征，并利用神经网络的非线性映射特性逼近故障诊断模型。诊断结果表明基于人工神经网络的电路故障诊断方法是行之有效的。此方法具有广阔的应用前景，为大规模模拟电路故障诊断提供了新的理论依据和检测方法，并有希望研制成一套高效的检测设备。