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A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box 总被引:2,自引:0,他引:2
N. Saravanan V.N.S. Kumar Siddabattuni K.I. Ramachandran 《Expert systems with applications》2008,35(3):1351-1366
The condition of an inaccessible gear in an operating machine can be monitored using the vibration signal of the machine measured at some convenient location and further processed to unravel the significance of these signals. This paper deals with the effectiveness of wavelet-based features for fault diagnosis using support vector machines (SVM) and proximal support vector machines (PSVM). The statistical feature vectors from Morlet wavelet coefficients are classified using J48 algorithm and the predominant features were fed as input for training and testing SVM and PSVM and their relative efficiency in classifying the faults in the bevel gear box was compared. 相似文献
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Rule learning based approach to fault detection and diagnosis is becoming very popular, mainly due to their high accuracy when compared to older statistical methods. Fault detection and diagnosis of various mechanical components of centrifugal pump is essential to increase the productivity and reduce the breakdowns. This paper presents the use of rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a centrifugal pump. A fuzzy inference system (FIS) is built using rough set rules and tested using test data. The effect of different types of membership functions on the FIS performance is also presented. Finally, the performance of this classifier is compared to that of a fuzzy-antminer classifier and to multi-layer perceptron (MLP) based classifiers. 相似文献
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V. Sugumaran G.R. Sabareesh K.I. Ramachandran 《Expert systems with applications》2008,34(4):3090-3098
Roller bearing is one of the most widely used rotary elements in a rotary machine. The roller bearing’s nature of vibration reveals its condition and the features that show the nature are to be extracted through some indirect means. Statistical parameters like kurtosis, standard deviation, maximum value, etc. form a set of features, which are widely used in fault diagnostics. Finding out good features that discriminate the different fault conditions of the bearing is often a problem. Selection of good features is an important phase in pattern recognition and requires detailed domain knowledge. This paper addresses the feature selection process using decision tree and uses kernel based neighborhood score multi-class support vector machine (MSVM) for classification. The vibration signal from a piezoelectric transducer is captured for the following conditions: good bearing, bearing with inner race fault, bearing with outer race fault, and inner and outer race faults. The statistical features are extracted therefrom and classified successfully using MSVM. The results of MSVM are compared with and binary support vector machine (SVM). 相似文献
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Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM) 总被引:4,自引:0,他引:4
Vibration signals extracted from rotating parts of machineries carries lot many information with in them about the condition of the operating machine. Further processing of these raw vibration signatures measured at a convenient location of the machine unravels the condition of the component or assembly under study. This paper deals with the effectiveness of wavelet-based features for fault diagnosis of a gear box using artificial neural network (ANN) and proximal support vector machines (PSVM). The statistical feature vectors from Morlet wavelet coefficients are classified using J48 algorithm and the predominant features were fed as input for training and testing ANN and PSVM and their relative efficiency in classifying the faults in the bevel gear box was compared. 相似文献
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Vibrant fault diagnosis for hydroelectric generator units with a new combination of rough sets and support vector machine 总被引:2,自引:0,他引:2
Xiaoyuan ZhangJianzhong Zhou Jun GuoQiang Zou Zhiwei Huang 《Expert systems with applications》2012,39(3):2621-2628
The fault diagnosis for hydroelectric generator unit (HGU) is significant to prevent dangerous accidents from occurring and to improve economic efficiency. The faults of HGU involve overlapping fault patterns which may denote a kind of faults in the early stage or a subset of samples that caused by multi-fault. But until now it has not been considered in the traditional classifier of fault diagnosis for HGU. In this paper, a novel classifier combined rough sets and support vector machine is proposed and applied in the fault diagnosis for HGU. Instead of classifying the patterns directly, the fault patterns lying in the overlapped region are extracted firstly. Then, upper and lower approximations of each class are defined on the basis of rough set technique. Next, for the fault patterns lying in the overlapped region, the reliability they belong to a certain class is calculated. At last, the proposed method is successfully applied in analyzing an international standard data set, as well as diagnosing the vibrant faults of a HGU. The results show that the proposed classifier can more properly describe the complex map between the faults and their symptoms, and is suitable to fault diagnosis for HGU. 相似文献
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Monitoring the transmission status of multi-joint industrial robots is very important for the accuracy of the robot motion. The fault diagnosis information is an indispensable basis for the collaborative maintenance of the robots in industry 4.0. In this paper, an attitude data-based intelligent fault diagnosis approach is proposed for multi-joint industrial robots. Based on the analysis of the transmission mechanism, the attitude change of the last joint is employed to reflect the transmission fault of robot components. An economical data acquisition strategy is performed by only installing one attitude sensor on the last joint of the multi-joint robot. Considering the characteristics of attitude data, a hybrid sparse auto-encoder (SAE) and support vector machine (SVM) approach, namely SAE-SVM, is subsequently presented to construct an intelligent fault diagnosis model by learning from the attitude dataset with multiple fault information. Experimental results show that the proposed fault diagnosis approach has promising performance in identifying different faults related to the reducer of a 6-axial multi-joint industrial robot accurately and reliably. 相似文献
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Intelligent diagnosis method for a centrifugal pump using features of vibration signals 总被引:1,自引:0,他引:1
In the field of machinery diagnosis, the utilization of vibration signals is effective in the detection of fault, because
the signals carry dynamic information about the machine state. However, knowledge of a distinguishing fault is ambiguous because
definite relationships between symptoms and fault types cannot be easily identified. This paper presents an intelligent diagnosis
method for a centrifugal pump system using features of vibration signals at an early stage. The diagnosis algorithm is derived
using wavelet transform, rough sets and a partially linearized neural network (PNN). ReverseBior wavelet function is used
to extract fault features from measured vibration signals and to capture hidden fault information across optimum frequency
regions. As the input parameters for the neural network, the non-dimensional symptom parameters that can reflect the characteristics
of a signal are defined in the amplitude domain. The diagnosis knowledge for the training of the PNN can be acquired by using
the rough sets. We also propose a diagnosis method based on the PNN, one which can deal with the ambiguity problem of condition
diagnosis, and distinguish fault types on the basis of the possibility distributions of symptom parameters automatically.
The decision method of optimum frequency region for extracting feature signals is also discussed using real plant data. Practical
examples of diagnosis for a centrifugal pump system are shown in order to verify the efficiency of the method. 相似文献
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基于粒子群算法优化支持向量机汽车故障诊断研究 总被引:1,自引:0,他引:1
汽车故障检测和诊断技术一直是国内外研究热点问题。支持向量机用于汽车故障诊断时,其多分类组合决策对分类正确率及诊断时间有很大影响,为了有效提高汽车系统故障诊断的效率和精度,提出了一种基于粒子群算法优化层次支持向量机汽车故障诊断检测方法。针对分解支持向量机具有测试时间短、结构难以确定的特点,利用粒子群算法,依据最大间隔距离原则优化层次支持向量机模型,使每个节点的支持向量机具有最大分类间隔,减少了误差积累,从而优化了多级二叉树结构的SVM,实现故障的分级诊断。仿真实验结果表明,提出的算法在所有参比模型中精度最高,能高效地对汽车系统的故障进行检测与定位,具有较强的泛化能力,同时缩短了故障诊断时间。 相似文献
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利用LabVIEW和C语言、MATLAB混合编程,设计并实现了航空发动机故障诊断系统。利用C语言设计了数据采集仪的DLL驱动程序,LabVIEW调用DLL实现了数据采集;针对航空发动机振动信号的特点,设计了信号处理与故障特征提取模块;利用MATLAB编译了多算法优化的支持向量机COM组件,LabVIEW调用该组件实现了故障诊断;利用数据库连接工具包设计了数据库管理模块。在航空发动机转子实验台上对该系统性能的测试结果表明,该系统达到了较高的故障诊断精度,同时也验证了文中设计思想的可行性。 相似文献
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基于ICA和SVM的滚动轴承故障诊断方法研究 总被引:2,自引:2,他引:2
通过对滚动轴承振动信号的分析处理,提出了基于独立分量分析和支持向量机的故障诊断方法,采用FastICA算法对信号进行分析处理,提取出代表轴承运行状态的投影系数矩阵,并以此作为特征向量来建立支持向量机分类器,利用SVM网络的智能性来判断滚动轴承的工作状态和故障类型。 相似文献
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机载燃油泵的健康状态是保障飞行任务完成的先决条件,实现机载燃油泵故障诊断的关键是敏感故障特征的提取。由于机械系统的复杂性,机载燃油泵振动信号的随机性表现在不同尺度上,因此需要对振动信号进行多尺度分析。基于此,在模糊熵的基础上引入尺度因子,对振动信号在不同尺度下进行复杂性度量。然后将多尺度模糊熵特征量作为支持向量机的输入参数以识别故障状态,并采用遗传算法对支持向量机的核函数参数及惩罚参数进行优化。实验结果分析表明,该方法可有效提取故障特征,实现机载燃油泵的故障诊断。 相似文献
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Large non-residential buildings can contain complex and often inefficient water distribution systems. As requirements for water increase due to water scarcity and industrialization, it has become increasingly important to effectively detect and diagnose faults in water distribution systems in large buildings. In many cases, if water supply is not impacted, faults in water distribution systems can go unnoticed. This can lead to unnecessary increases in water usage and associated energy due to pumping, treating, and heating water. The majority of fault detection and diagnosis studies in the water sector are limited to municipal water supply and leakage detection. The application of detection and diagnosis for faults in building water networks remains largely unexplored and the ability to identify and distinguish between routine and non-routine water usage at this scale remains a challenge. This study using case-study data, presents the application of principal component analysis and a multi-class support vector machine to detect and classify faults for non-residential building water networks. In the absence of a process model (which is typical for such water distribution systems), principal component analysis is proposed as a data-driven fault detection technique for building water distribution systems for the first time herein. Hotelling T2-statistics and Q-statistics were employed to detect abnormality within incoming data, and a multi-class support vector machine was trained for fault classification. Despite the relatively limited training data available from the case-study (which would reflect the situation in many buildings), meaningful faults were detected, and the technique proved successful in discriminating between various types of faults in the water distribution system. The effectiveness of the proposed approach is compared to a univariate threshold technique by comparison of their respective performance in the detection of faults that occurred in the case-study site. The results demonstrate the promising capabilities of the proposed fault detection and diagnosis approach. Such a strategy could provide a robust methodology that can be applied to buildings to reduce inefficient water use, reducing their life-cycle carbon footprint. 相似文献
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Graph shift regularization is a new and effective graph-based semi-supervised classification method, but its performance is closely related to the representation graphs. Since directed graphs can convey more information about the relationship between vertices than undirected graphs, an intelligent method called graph shift regularization with directed graphs (GSR-D) is presented for fault diagnosis of rolling bearings. For greatly improving the diagnosis performance of GSR-D, a directed and weighted k-nearest neighbor graph is first constructed by treating each sample (i.e., each vibration signal segment) as a vertex, in which the similarity between samples is measured by cosine distance instead of the commonly used Euclidean distance, and the edge weights are also defined by cosine distance instead of the commonly used heat kernel. Then, the labels of samples are considered as the graph signals indexed by the vertices of the representation graph. Finally, the states of unlabeled samples are predicted by finding a graph signal that has minimal total variation and satisfies the constraint given by labeled samples as much as possible. Experimental results indicate that GSR-D is better and more stable than the standard convolutional neural network and support vector machine in rolling bearing fault diagnosis, and GSR-D only has two tuning parameters with certain robustness. 相似文献
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Shih-Wei Lin Kuo-Ching Ying Chou-Yuan Lee Zne-Jung Lee 《Applied Soft Computing》2012,12(10):3285-3290
Intrusion detection system (IDS) is to monitor the attacks occurring in the computer or networks. Anomaly intrusion detection plays an important role in IDS to detect new attacks by detecting any deviation from the normal profile. In this paper, an intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection is proposed. The key idea is to take the advantage of support vector machine (SVM), decision tree (DT), and simulated annealing (SA). In the proposed algorithm, SVM and SA can find the best selected features to elevate the accuracy of anomaly intrusion detection. By analyzing the information from using KDD’99 dataset, DT and SA can obtain decision rules for new attacks and can improve accuracy of classification. In addition, the best parameter settings for the DT and SVM are automatically adjusted by SA. The proposed algorithm outperforms other existing approaches. Simulation results demonstrate that the proposed algorithm is successful in detecting anomaly intrusion detection. 相似文献