A comparison study of support vector machines and hidden Markov models in machinery condition monitoring

Condition classification is an important step in machinery fault detection, which is a problem of pattern recognition. Currently, there are a lot of techniques in this area and the purpose of this paper is to investigate two popular recognition techniques, namely hidden Markov model and support vector machine. At the beginning, we briefly introduced the procedure of feature extraction and the theoretical background of this paper. The comparison experiment was conducted for gearbox fault detection and the analysis results from this work showed that support vector machine has better classification performance in this area.     

Support vector machine and optimised feature extraction in integrated eddy current instrument

Eddy current, especially pulsed eddy current (PEC), is known as an effective tool to detect defects in aircraft structures. Current PEC defect classification methods require highly trained personnel and the results are usually influenced by human subjectivity. Therefore, automated defect classification is desirable in a PEC instrument. In this work, five eddy current based methods are integrated into an instrument using a universal model and modular structure. Then, a Support Vector Machine (SVM) is used to build the classifier model and predict the type of defect. Principal component analysis (PCA) and independent component analysis (ICA) are investigated for feature extraction and compared for classification results using SVM. Two-layer Al–Mn alloy specimens with four kinds of defects are used for classification. The experimental results show that the proposed methods have great potential for in-situ defect inspection of multi-layer aircraft structures.     

A novel optimal support vector machine ensemble model for NOX emissions prediction of a diesel engine

A novel ensemble method based on principal component analysis (PCA), genetic algorithm (GA) and support vector machine (SVM) implemented in MATLAB® is presented for establishing the NO_X emissions prediction model for a diesel engine for both steady and transient operating states. The different stages of data preprocessing, modeling, optimization and prediction were discussed in detail. Normalization and PCA were used to reduce differences and redundancy of the datasets respectively. Subsequently, the SVM model was trained with 1/3 of the equi-spaced data samples (a simple DoE) selected after preprocessing. A grid search and GA were then applied as the combination strategy with the fitness function being the cross-validated root mean square error (RMSE) for optimizing the model parameters to improve the prediction accuracy. The optimal model was finally tested using the rest 2/3 data samples. Compared with other three methods, the proposed model exhibited superior accuracy both on training and testing datasets.     

Support vector machine based data processing algorithm for wear degree classification of slurry pump systems

Classification is a useful tool in identifying fault patterns. Generally, a good classification implementation is closely related to the effectiveness of data used. The word “effectiveness” implies that the data should be clean and the features indicating fault patterns should be properly selected. Unfortunately, data cleaning is not often implemented in reported work of fault pattern classifications. In this paper, a data processing algorithm is developed to achieve the effectiveness, which includes data cleaning followed by feature selection. A data cleaning algorithm is developed based on support vector machine and random sub-sampling validation. Candidate outliers are selected based on fraction values provided by the proposed data cleaning algorithm and final outliers are determined based on their removal impacts on classification performance. The feature selection algorithm adopts the classical sequential backward feature selection. The performance of the data cleaning algorithm is tested using three benchmark datasets. The tests show good capability of the data cleaning algorithm in identifying outliers for all datasets. The proposed data processing algorithm is adopted in the classification of the wear degree of pump impellers in a slurry pump system. The results show good effectiveness of sequentially using data cleaning and feature selection in addressing fault pattern classification problems.     

Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines

This paper studies the application of support vector machines (SVMs) to the detection and classification of rolling-element bearing faults. The training of the SVMs is carried out using the sequential minimal optimization (SMO) algorithm. In this paper, a mechanism for selecting adequate training parameters is proposed. This proposal makes the classification procedure fast and effective. Various scenarios are examined using two sets of vibration data, and the results are compared with those available in the literature that are relevant to this investigation.     

Classification of fault location and performance degradation of a roller bearing

Effective fault location classification and especially performance degradation assessment of a roller bearing have been the subject extensive research, which can reduce costs and the nonscheduled down time. In this paper, a new fault diagnosis method based on multiple features, kernel principal component analysis (KPCA) and particle swarm optimization-support vector machine (PSO-SVM) is put forward. First, traditional features of the vibration signals in time-domain and frequency-domain are calculated, and then two types of features referred to as singular values and AR model parameters based on ensemble empirical mode decomposition (EEMD) are introduced. After that, the original feature vectors are mapped into higher dimensional space and the kernel principal components are extracted as new feature vectors, which are used as inputs to PSO-SVM. The experimental results show that the new diagnosis approach proposed in this paper can identify not only the fault locations but also the performance degradation of the roller bearing.     

基于改进的快速独立分量分析与支持向量机的ECG分类诊断

提出了一种改进快速独立分量分析与支持向量机相结合的新型心电图分类方法.利用埃特金加速法对快速独立分量分析算法的核心迭代过程进行改造,得到改进的快速独立分量分析算法,减少了迭代次数,提高了算法的收敛速度.新方法运用改进的快速独立分量分析算法提取心电图数据的特征向量,并通过支持向量机实现心电图信号的分类.对取自MIT/BH数据库的7种不同心脏状况的心电图数据进行实验,结果表明该方法整体识别率达到98.8%,改进的快速独立分量分析算法所需迭代时间比现有的快速独立分量分析算法减少48%.     

小波包变换和支持向量机相结合在发动机气门间隙故障诊断中的应用

为了对发动机气门间隙进行故障诊断,在对振动信号进行采集和预处理的基础上,运用小波包频带能量分解技术提取发动机故障的特征向量,以此作为支持向量机分类器（SVM）的训练样本,用经训练的SVM多分类器对发动机不同故障进行自动识别和诊断,实现了信号特征向量提取与故障模式识别的有机结合。实验结果表明,该方法能在机械故障样本少的情况下准确的识别和诊断出发动机气门间隙的故障类型,具有实际的工程应用价值。     

A novel scheme for fault detection of reciprocating compressor valves based on basis pursuit,wave matching and support vector machine

A scheme for fault detection of compressor valves based on basis pursuit (BP), wave matching and support vector machine (SVM) is presented. BP is applied to extract the main vibration component in the signal and suppress background noise. Wave matching is a new feature extraction method proposed in this paper. Instead of extracting features through commonly used indicators such as statistic measures or information entropy, wave matching extracts features by matching the vibration signal with parameterized waveform optimized by differential evolution (DE) algorithm. It only produces a small number of features and the features have clear physical meaning. SVM is employed in the fault classification because of its superiority in dealing with small sample problems. The results of real compressor valve signal analysis confirm that the proposed scheme can differentiate compressor valve faults with high accuracy and reliability.