A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box

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.     

Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique

To determine the condition of an inaccessible gear in an operating machine the vibration signal of the machine can be continuously monitored by placing a sensor close to the source of the vibrations. These signals can be further processed to extract the features and identify the status of the machine. The vibration signal acquired from the operating machine has been used to effectively diagnose the condition of inaccessible moving components inside the machine. Suitable sensors are kept at various locations to pick up the signals produced by machinery and these signals are very meaningful in condition diagnosis surveillance. To determine the important characteristics and to unravel the significance of these signals, further analysis or processing is required.This paper presents the use of decision tree for selecting best statistical features that will discriminate the fault conditions of the gear box from the signals extracted. These features are extracted from vibration signals. A rule set is formed from the extracted features and fed to a fuzzy classifier. The rule set necessary for building the fuzzy classifier is obtained largely by intuition and domain knowledge. This paper also presents the usage of decision tree to generate the rules automatically from the feature set. The vibration signal from a piezo-electric transducer is captured for the following conditions – good bevel gear, bevel gear with tooth breakage (GTB), bevel gear with crack at root of the tooth (GTC), and bevel gear with face wear of the teeth (TFW) for various loading and lubrication conditions. The statistical features were extracted and good features that discriminate the different fault conditions of the gearbox were selected using decision tree. The rule set for fuzzy classifier is obtained by once using the decision tree again. A fuzzy classifier is built and tested with representative data. The results are found to be encouraging.     

A case study on classification of features by fast single-shot multiclass PSVM using Morlet wavelet for fault diagnosis of spur bevel gear box

This paper deals with the application of fast single-shot multiclass proximal support vector machine for fault diagnosis of a gear box consisting of twenty four classes. 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. 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 multiclass proximal support vector machine. The efficiency and time consumption in classifying the twenty four classes all-at-once is reported.     

Stock trend prediction based on a new status box method and AdaBoost probabilistic support vector machine

Stock trend prediction is regarded as one of the most challenging tasks of financial time series prediction. Conventional statistical modeling techniques are not adequate for stock trend forecasting because of the non-stationarity and non-linearity of the stock market. With this regard, many machine learning approaches are used to improve the prediction results. These approaches mainly focus on two aspects: regression problem of the stock price and prediction problem of the turning points of stock price. In this paper, we concentrate on the evaluation of the current trend of stock price and the prediction of the change orientation of the stock price in future. Then, a new approach named status box method is proposed. Different from the prediction issue of the turning points, the status box method packages some stock points into three categories of boxes which indicate different stock status. And then, some machine learning techniques are used to classify these boxes so as to measure whether the states of each box coincides with the stock price trend and forecast the stock price trend based on the states of the box. These results would support us to make buying or selling strategies. Comparing with the turning points prediction that only considered the features of one day, each status box contains a certain amount of points which represent the stock price trend in a certain period of time. So, the status box reflects more information of stock market. To solve the classification problem of the status box, a special features construction approach is presented. Moreover, a new ensemble method integrated with the AdaBoost algorithm, probabilistic support vector machine (PSVM), and genetic algorithm (GA) is constructed to perform the status boxes classification. To verify the applicability and superiority of the proposed methods, 20 shares chosen from Shenzhen Stock Exchange (SZSE) and 16 shares from National Association of Securities Dealers Automated Quotations (NASDAQ) are applied to perform stock trend prediction. The results show that the status box method not only have the better classification accuracy but also effectively solve the unbalance problem of the stock turning points classification. In addition, the new ensemble classifier achieves preferable profitability in simulation of stock investment and remarkably improves the classification performance compared with the approach that only uses the PSVM or back-propagation artificial neural network (BPN).     

医学影像库的一种智能分类方法

针对医学影像库信息量大、关联信息多、对象复杂的特点,将粗糙集算法与一种近似的支撑矢量机算法相结合实现了对医学影像库的正常、异常分类.粗糙集算法有效地降低了医学影像库的维度,而非线性的近似支撑矢量机算法则克服了标准支撑矢量机在实际应用中表现出来的算法速度慢、算法过于复杂而难于实现以及检测阶段运算量大等缺陷.实践证明了该方法的确具备简单、快速、高效的特点.     

Soft computing approach to fault diagnosis of centrifugal pump

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study.     

一种基于近似支撑矢量机(PSVM)的交通目标分类方法

本文介绍了支撑向量机的特点,给出了实际应用中传统支撑矢量机存在的问题。为了克服支撑矢量机算法的不足,引入了一种近似支撑矢量机（PSVM）算法,并将此算法用于交通目标的分类识别。实验结果表明此算法比BP神经网络法准确率高,比传统的SVM法的效率高。     

Artificial neural networks and genetic algorithm for bearing fault detection

A study is presented to compare the performance of three types of artificial neural network (ANN), namely, multi layer perceptron (MLP), radial basis function (RBF) network and probabilistic neural network (PNN), for bearing fault detection. Features are extracted from time domain vibration signals, without and with preprocessing, of a rotating machine with normal and defective bearings. The extracted features are used as inputs to all three ANN classifiers: MLP, RBF and PNN for two- class (normal or fault) recognition. Genetic algorithms (GAs) have been used to select the characteristic parameters of the classifiers and the input features. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine. The roles of different vibration signals and preprocessing techniques are investigated. The results show the effectiveness of the features and the classifiers in detection of machine condition.     

A Hybrid Genetic Algorithm and Back-Propagation Classifier for Gearbox Fault Diagnosis

An Artificial Neural Network (ANN) classifier trained by a hybrid GA-BP method for diagnosis of gear faults is presented here that can be incorporated in an online fault diagnostic system of vital gearboxes. The distinctive features obtained from vibration signals of a running gearbox; that was operated in normal and with faults induced conditions were used to feed the GA-BP hybrid classifier. Time domain vibration signals were divided in 40segments. From each segment features such as magnitude of peaks in time domain and spectrum along with statistical features such as central moments and standard deviations were extracted to feed the classifier. Based on the experimental results it was shown that the GA-BP hybrid classifier can successfully identify gear condition. It was also shown that the network trained by GA-BP hybrid method performs much better than ANN that is trained by standard BP or GA individually. Further, it was also shown that if prior to extraction of features; the vibration signals are pre-processed by Discrete Wavelet Transform (DWT) then efficacy of the GA-BP hybrid is significantly enhanced.     

Extreme learning machine for regression and multiclass classification

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the "generalized" single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.     

增量密度加权近似支持向量机

近似支持向量机((PSVM)是一个正则化最小二乘问题,有解析解,但是它失去了支持向量机(SVM)的稀疏 性,使得所有的训练样例都成为支持向量。为了有效地控制近似支持向量机的稀疏性,提出了增量密度加权近似支持 向量机(mWPSVM),它在训练集中选取最基本的支持向量。实验表明,IvWPSVM方法与SVM, PSVM和DWPS- VM方法相比,其精度相似,收敛速度快,可有效地控制近似支持向量机的稀疏性。     

PSVM: a preference-enhanced SVM model using preference data for classification

Classification is an essential task in data mining, machine learning and pattern recognition areas. Conventional classification models focus on distinctive samples from different categories. There are fine-grained differences between data instances within a particular category. These differences form the preference information that is essential for human learning, and, in our view, could also be helpful for classification models. In this paper, we propose a preference-enhanced support vector machine (PSVM), that incorporates preference-pair data as a specific type of supplementary information into SVM. Additionally, we propose a two-layer heuristic sampling method to obtain effective preference-pairs, and an extended sequential minimal optimization (SMO) algorithm to fit PSVM. To evaluate our model, we use the task of knowledge base acceleration-cumulative citation recommendation (KBA-CCR) on the TREC-KBA-2012 dataset and seven other datasets from UCI, StatLib and mldata.org. The experimental results show that our proposed PSVM exhibits high performance with official evaluation metrics.     

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。     

一种基于PSVM的多类分类方法

为克服传统支持向量机不能处理交叉数据分类问题,Mangasarian等人提出一种新的分类方法PSVM,该方法可有效解决交叉数据两分类问题,但用PSVM解决多分类问题还报道不多。为此,提出一种基于PSVM的多分类方法（M-PSVM）,并探讨训练样本比例与分类精度之间关系。在UCI数据集上的测试结果表明,M-PSVM与传统SVM分类性能相当,且当训练样本比例小时,效果更优;此外,在入侵检测数据集上的初步实验表明,M-PSVM可有效改进少数类的分类精度,因而为求解数据不平衡下的分类问题提供了新的思路,进一步的实验验证正在进行。     

一种过程支持向量机及其在动态模式分类中的应用

针对一般SVM在机制上难以直接对动态模式进行分类的问题,提出了一种基于函数正交基展开的过程支持向量机.该模型的输入为时变函数,输出为模式类别.在输入函数空间中选择一组适当的正交函数基,将输入函数在该组函数基下进行有限项展开,把展开式系数作为核函数的输入.由于时变函数在基函数映射下与展开式系数一一对应,从而可利用SVM的变换机制实现动态模式分类.给出了基于SMO的求解算法,实验结果验证了模型和算法的有效性.     

An SVM—ANN Hybrid Classifier for Diagnosis of Gear Fault

A hybrid classifier obtained by hybridizing Support Vector Machines (SVM) and Artificial Neural Network (ANN) classifiers is presented here for diagnosis of gear faults. The distinctive features obtained from vibration signals of a running gearbox, which was operated in normal and fault-induced conditions, were used to feed the SVM-ANN hybrid classifier. Time-domain vibration signals were divided in segments. Features such as peaks in time domain and in spectrum, central moments, and standard deviations were obtained from signal segments. Based on the experimental results, it was shown that SVM-ANN hybrid classifier can successfully identify gear condition and that the hybrid SVM-ANN classifier performs much better than standard versions of ANNs and SVM. The effectiveness of the hybrid classifier under noise was also investigated. It was shown that if vibration signals are preprocessed by Discrete Wavelet Transform (DWT), efficacy of the SVM-ANN hybrid is significantly enhanced.     

一种基于拟牛顿法的大类别分类算法

支持向量机利用接近边界的少数向量来构造一个最优分类面。然而当两类中的样本数量差别悬殊时，PSVM算法则会过度拟合样本量大的那一类，而对样本量很小的那一类的错分率相当高。为解决此问题，本文提出了一种改进的支持向量机算于拟牛顿法的大类别分类算法。同时，这个问题也是大类别分类问题所采用的留一法面临的问题，在DFP-PSVM的基础上，提出了基于拟牛顿法的大类别分类算法。通过仿真实验证实了此算法在精度上优于PSVM算法。     

Error compensation in machine tools: a neural network approach

For a non-idealized machine tool, each point in the workspace is associated with a tool point positioning error vector. If this error map can be determined, then it is possible to substantially improve the positioning performance of the machine by introducing suitable compensation into the control loop. This paper explores the possibility of using an artifical neural network (ANN) to compute this mapping. The training set for the ANN is obtained by mounting a physical artifact whose dimensions are precisely known in the machine's workspace. The machine, equipped with a touch trigger probe, measures the positions of features on the artifact. The difference between the machine reading and the known dimension is the machine error at that point in the workspace. Using standard modeling techniques, the kinematic error model for a CNC turning center was developed. This model was parameterized by measurement of the parametric error functions using a laser interferometer, electronic levels and a precision square. The kinematic model was then used to simulate the artifact-measuring process and develop the ANN training set. The effect of changing artifact geometry was explored and a machining operation was simulated using the ANN output to provide compensation. The results show that the ANN is capable of learning the error map of a real machine, and that ANN-based compensation can significantly reduce part-dimensional errors.     

Multicategory Proximal Support Vector Machine Classifiers

Given a dataset, each element of which labeled by one of k labels, we construct by a very fast algorithm, a k-category proximal support vector machine (PSVM) classifier. Proximal support vector machines and related approaches (Fung & Mangasarian, 2001; Suykens & Vandewalle, 1999) can be interpreted as ridge regression applied to classification problems (Evgeniou, Pontil, & Poggio, 2000). Extensive computational results have shown the effectiveness of PSVM for two-class classification problems where the separating plane is constructed in time that can be as little as two orders of magnitude shorter than that of conventional support vector machines. When PSVM is applied to problems with more than two classes, the well known one-from-the-rest approach is a natural choice in order to take advantage of its fast performance. However, there is a drawback associated with this one-from-the-rest approach. The resulting two-class problems are often very unbalanced, leading in some cases to poor performance. We propose balancing the k classes and a novel Newton refinement modification to PSVM in order to deal with this problem. Computational results indicate that these two modifications preserve the speed of PSVM while often leading to significant test set improvement over a plain PSVM one-from-the-rest application. The modified approach is considerably faster than other one-from-the-rest methods that use conventional SVM formulations, while still giving comparable test set correctness.Editor Shai Ben-David     

Iterative support vector machine with guaranteed accuracy and run time

Abstract: Using a conjugate gradient method, a novel iterative support vector machine (FISVM) is proposed, which is capable of generating a new non‐linear classifier. We attempt to solve a modified primal problem of proximal support vector machine (PSVM) and show that the solution of the modified primal problem reduces to solving just a system of linear equations as opposed to a quadratic programming problem in SVM. This algorithm not only has no requirement for special optimization solvers, such as linear or quadratic programming tools, but also guarantees fast convergence. The full algorithm merely needs four lines of MATLAB codes, which gives results that are similar to or better than that of several new learning algorithms, in terms of classification accuracy. Besides, the proposed stand‐alone approach is capable of dealing with instability of classification performance of smooth support vector machine, generalized proximal support vector machine, PSVM and reduced support vector machine. Experiments carried out on UCI datasets show the effectiveness of our approach.