设备噪声监测中主分量的特征表示

研究了设备噪声监测中主分量特征表示的提取及应用。在设备噪声时域和频域统计模式特征基础上,通过主分量分析探讨了设备噪声模式的主分量特征表示方法,引入相关度的概念分析了主分量特征表示对设备状态的表征能力,提出了选择有效维数的主分量特征表示进行设备噪声监测的方案。通过在EQ6100 型发动机上预先模拟四种连杆轴承磨损故障,测取噪声信号,实例分析显示了低维主分量特征表示可以有效表征设备状态。实验最后对测试集样本进行状态监测得到了100%的准确率,表明了主分量特征表示用于设备噪声监测的有效性。     

Subspace-based gearbox condition monitoring by kernel principal component analysis

Feature extraction is a key step for gearbox condition monitoring. The statistical features of the measured vibrations can be used to characterise gearbox conditions; however, their regularity and sensitivity in pattern space are different and may vary considerably under different operating conditions. This paper addresses the non-linear feature extraction scheme from the time-domain features with wavelet packet preprocessing and frequency-domain features of the vibration signals using the kernel principal component analysis (KPCA). Then two different KPCA-based subspace structures are constructed for representing and classifying the gearbox conditions. The proposed methods can extract the non-linear features of gearbox conditions using KPCA effectively, and perform conveniently with low computational complexity based on subspace methods. Experimental analysis with a fatigue test of an automobile transmission gearbox shows that the KPCA features outperform PCA features in terms of clustering capability, and both the two KPCA-based subspace methods can be effectively applied to gearbox condition monitoring.     

A critical study of different dimensionality reduction methods for gear crack degradation assessment under different operating conditions

Gear cracks are some of the most common faults found in industrial machinery. Identification of different gear crack levels is beneficial to assessing gear crack degradation and preventing any unexpected machine breakdowns. In this paper, redundant statistical features are extracted from binary wavelet packet transform at different decomposition levels to describe different gear crack levels. Because the dimensionality of the extracted redundant statistical parameters is high to 620, it is necessary to reduce their dimensionality prior to the use of any statistical model for intelligently identifying different gear crack levels. The major idea of dimensionality reduction is that the extracted redundant statistical features in a high-dimensional space are mapped to a few significant features in a low-dimensional space, where these significant features are used to represent different gear crack levels. As of today, there are many popular linear and non-linear dimensionality reduction methods including principal component analysis, kernel principal components analysis, Isomap, Laplacian Eigenmaps and local linear embedding. Different dimensionality reduction methods have different performances in dimensionality reduction, which can be measured by prediction accuracies of some common statistical models, such as Naive Bayes classifier, linear discriminant analysis, quadratic discriminant analysis, and classification and regression tree. Gear crack level degradation data collected from a machine in a laboratory under different operating conditions including four different motor speeds and three different loads are used to investigate performances of the linear and non-linear dimensionality reduction methods. In our case study, the results show that principal component analysis has the best performance in dimensionality reduction and it results in the highest prediction accuracies in all of the aforementioned statistical models. In other words, the linear dimensionality reduction method is better than all of the non-linear dimensionality reduction methods investigated in this paper.     

A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery

To make further improvement in the diagnosis accuracy and efficiency, a mixed-domain state features data based hybrid fault diagnosis approach, which systematically blends both the statistical analysis approach and the artificial intelligence technology, is proposed in this work for rolling element bearings. For simplifying the fault diagnosis problems, the execution of the proposed method is divided into three steps, i.e., fault preliminary detection, fault type recognition and fault degree identification. In the first step, a preliminary judgment about the health status of the equipment can be evaluated by the statistical analysis method based on the permutation entropy theory. If fault exists, the following two processes based on the artificial intelligence approach are performed to further recognize the fault type and then identify the fault degree. For the two subsequent steps, mixed-domain state features containing time-domain, frequency-domain and multi-scale features are extracted to represent the fault peculiarity under different working conditions. As a powerful time-frequency analysis method, the fast EEMD method was employed to obtain multi-scale features. Furthermore, due to the information redundancy and the submergence of original feature space, a novel manifold learning method (modified LGPCA) is introduced to realize the low-dimensional representations for high-dimensional feature space. Finally, two cases with 12 working conditions respectively have been employed to evaluate the performance of the proposed method, where vibration signals were measured from an experimental bench of rolling element bearing. The analysis results showed the effectiveness and the superiority of the proposed method of which the diagnosis thought is more suitable for practical application.     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT,KPCA and Twin SVM

This paper proposes a hybrid intelligent method for multi-fault detection of rotating machinery, in which three methods, i.e. including the redundant second generation wavelet package transform (RSGWPT), the kernel principal component analysis (KPCA) and the twin support vector machine (TWSVM), are combined. Firstly, RSGWPT is used to extract feature vectors from representative statistical characteristics in the decomposition frequency band, and then the KPCA in the feature space is performed to reduce the dimension of features and to extract the dominant features for the following classification. Finally, a novel support vector machine, called twin support vector machine is used to construct a multi-class classifier. Inputting superior features to this classifier, the condition of the monitored machine component can be determined. Experimental results demonstrate that the proposed hybrid method is effective for multi-fault detection of rotating machinery. The TWSVM is also indicated that has better classification performance and faster convergence speed than the normal SVM.     

Discriminant diffusion maps analysis: A robust manifold learner for dimensionality reduction and its applications in machine condition monitoring and fault diagnosis

Various features extracted from raw signals usually contain a large amount of redundant information which may impede the practical applications of machine condition monitoring and fault diagnosis. Hence, as a solution, dimensionality reduction is vital for machine condition monitoring. This paper presents a new technique for dimensionality reduction called the discriminant diffusion maps analysis (DDMA), which is implemented by integrating a discriminant kernel scheme into the framework of the diffusion maps. The effectiveness and robustness of DDMA are verified in three different experiments, including a pneumatic pressure regulator experiment, a rolling element bearing test, and an artificial noisy nonlinear test system, with empirical comparisons with both the linear and nonlinear methods of dimensionality reduction, such as principle components analysis (PCA), independent components analysis (ICA), linear discriminant analysis (LDA), kernel PCA, self-organizing maps (SOM), ISOMAP, diffusion maps (DM), Laplacian eigenmaps (LE), locally linear embedding (LLE) analysis, Hessian-based LLE analysis, and local tangent space alignment analysis (LTSA). Results show that DDMA is capable of effectively representing the high-dimensional data in a lower dimensional space while retaining most useful information. In addition, the low-dimensional features generated by DDMA are much better than those generated by most of other state-of-the-art techniques in different situations.     

Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models

The sensitivity of various features that are characteristics of machine performance may vary significantly under different working conditions. Thus it is critical to devise a systematic feature extraction (FE) approach that provides a useful and automatic guidance on using the most effective features for machine performance prediction without human intervention. This paper proposes a locality preserving projections (LPP)-based FE approach. Different from principal component analysis (PCA) that aims to discover the global structure of the Euclidean space, LPP is capable to discover local structure of the data manifold. This may enable LPP to find more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. The effectiveness of the proposed approach for bearing defect and severity classification is evaluated experimentally on bearing test-beds. Furthermore, a novel health assessment indication, Gaussian mixture model (GMM)-based negative log likelihood probability (NLLP) is developed to provide a comprehensible indication for quantifying bearing performance degradation. The proposed approach has shown to provide better performance than using regular features (e.g., root mean square (RMS)). The experimental results indicate potential applications of LPP-based FE and GMM as effective tools for bearing performance degradation assessment.     

基于核最优K-L变换的故障特征提取方法研究

核函数主元分析KPCA(kernel princ ipal component analysis)能够提取机械故障信号的非线性特征,可以应用于机械故障状态识别。但是KPCA是一种无监督的特征提取方法,不能利用故障信号中的类别信息。本文介绍了一种核最优K-L变换,它可以充分利用类别信息,它能够提取类平均向量和方差向量中的判别信息,使提取的特征分类效果更好。在齿轮故障诊断实验中,采用核最优K-L变换提取故障信号的非线性特征,实验结果表明核最优K-L变换相比KPCA故障识别结果更为理想。     

主元分析方法的故障可检测性研究

主元分析（PCA）是一种有效的多元统计过程监测方法，PCA监测方法不依赖于过程的精确数学模型，这使得其难以对故障的可检测性问题进行系统的研究，基于故障子空间的描述方式，本文在主要元子空间的残差子空间中分别讨论了PCA故障可检测性的充分和必要条件，并提出了临界故障值的概念，通过对双效蒸发过程的仿真故障检测，表明所获得的结果能较好地刻画PCA的故障检测行为。     

Tool wear predictive model based on least squares support vector machines

The development of tool wear monitoring system for machining processes has been well recognised in industry due to the ever-increased demand for product quality and productivity improvement. This paper presents a new tool wear predictive model by combination of least squares support vector machines (LS-SVM) and principal component analysis (PCA) technique. The corresponding tool wear monitoring system is developed based on the platform of PXI and LabVIEW. PCA is firstly proposed to extract features from multiple sensory signals acquired from machining processes. Then, LS-SVM-based tool wear prediction model is constructed by learning correlation between extracted features and actual tool wear. The effectiveness of proposed predictive model and corresponding tool wear monitoring system is demonstrated by experimental results from broaching trials.     

Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump

Mono-block centrifugal pumps are widely used in a variety of applications. In many applications the role of mono-block centrifugal pump is critical and condition monitoring is essential. Vibration based continuous monitoring and analysis using machine learning approach is gaining momentum. Particularly, artificial neural networks, fuzzy logic have been employed for continuous monitoring and fault diagnosis. This paper presents the use of decision tree and rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a mono-block centrifugal pump. A fuzzy classifier is built using decision tree and rough set rules and tested using test data. The results obtained using decision tree rules and those obtained using rough set rules are compared. Finally, the accuracy of a principle component analysis based decision tree-fuzzy system is also evaluated. The study reveals that overall classification accuracy obtained by the decision tree-fuzzy hybrid system is to some extent better than the rough set-fuzzy hybrid system.     

核主元分析在透平机械状态监测中的应用

讨论了核主元分析(K erne l P rinc ipa l Com ponen t A na lys is,简称KPCA)原理,提出了基于KPCA的透平机械状态监测方法。该方法在低维特征空间利用内积核函数,实现原始空间到高维空间的非线性映射以及对高维映像数据的主元分析,从而在低维空间得到原始特征的非线性主元,并根据非线性主元构建特征子空间,实现特征提取和对透平机械状态的分类识别并监测其状态变化。对仿真数据及透平机械在正常、重负荷状态下试验数据的研究表明,KPCA分类效果比主元分析好,能有效地识别出透平机械的不同状态,并能及时监测到状态发生的变化。     

Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks

This paper proposes a new approach for rotating machinery which integrates wavelet transform (WT), principal component analysis (PCA), and artificial neural networks (ANN) to classify the fault and predict the conditions of components, equipment, and machines. The standard deviation of wavelet coefficients are extracted from processed historical signals of manufacturing equipment as features. Then, the features are analyzed by PCA and several new principal features obtained from original features can be used as inputs to train ANN. After training, the conditions and degradations of components and machines can be predicted, and the fault of them can be classified if it exists, by the trained ANN using the same kinds of principal features extracted from real time signals. A case study is used to evaluate the proposed method and the result indicates its higher efficiency and effectiveness comparing to traditional methods.     

Multiple manifolds analysis and its application to fault diagnosis

A novel approach to fault diagnosis is proposed using multiple manifolds analysis (MMA) to extract manifold information from the vibration signals collected from a mechanical system. The basic idea of MMA is to reconstruct a manifold by embedding time series into a high-dimensional phase space. The tangent direction of the neighborhood for each point is then used to approximate its local geometry. The variation of the multiple manifolds representing different states of the mechanical system can be revealed by performing multi-way principal component analysis. The vibration signals acquired from roller bearings are employed to validate the proposed algorithms. Test results show that the proposed MMA-based approach can interpret different machine conditions and is effective to the fault diagnosis, and the MMA-based fault clustering and trend analysis algorithms have outperformed the conventional fault diagnosis methods.     

基于WTPCA-MSVMs过程监控方法

提出了基于小波变换主元分析和多支持向量机(wavelet transform PCA-Multiple support vector machines,WTPCA-MSVMs)的过程监控方法,该方法首先利用小波变换(wavelet transform,WT)对采样数据进行预处理,以有效抑制过程数据中所含的噪声和干扰信号;然后利用主元分析(principal component analysis,PCA)对预处理后的数据建立主元监控模型;考虑到实际工业过程故障数据的数量较少,而支持向量机(support vector Machine,SVM)在小样本学习方面具有良好的泛化能力的特性,最后提出了基于多支持向量机(multiple support vector machines,MSVMs)的故障诊断方法。对TE(tennessee eastman,TE)过程的监控应用表明了所提出方法的有效性。     

An integrated framework for statistical change detection in running status of industrial machinery under transient conditions

Early detection of changes in machine running status from sensor signals attracts increasing attention for the monitoring and assessment of complex industrial machineries under transient conditions. This paper presents a detection method that integrates one-class SVM with a pre-defined Autoregressive Integrated Moving Average (ARIMA) regression process. Meanwhile, an automatic cyclic-analysis method is also developed as a preprocessing to suppress temporal non-stationarity in condition signal before feeding it to the monitoring process. As such, a novel framework of continuous monitoring of condition signal is finally presented to inspect whether an unexpected running status change occurs or not during continuous machine operations. The proposed framework is applied to three representative condition monitoring applications: external loading condition monitoring, bearing health condition assessment, and rotational speed condition monitoring. Comparisons with existing methods are also provided, where the proposed method demonstrates its significant improvements over others.     

ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES

A procedure is presented for fault diagnosis of rolling element bearings through artificial neural network (ANN). The characteristic features of time-domain vibration signals of the rotating machinery with normal and defective bearings have been used as inputs to the ANN consisting of input, hidden and output layers. The features are obtained from direct processing of the signal segments using very simple preprocessing. The input layer consists of five nodes, one each for root mean square, variance, skewness, kurtosis and normalised sixth central moment of the time-domain vibration signals. The inputs are normalised in the range of 0.0 and 1.0 except for the skewness which is normalised between −1.0 and 1.0. The output layer consists of two binary nodes indicating the status of the machine—normal or defective bearings. Two hidden layers with different number of neurons have been used. The ANN is trained using backpropagation algorithm with a subset of the experimental data for known machine conditions. The ANN is tested using the remaining set of data. The effects of some preprocessing techniques like high-pass, band-pass filtration, envelope detection (demodulation) and wavelet transform of the vibration signals, prior to feature extraction, are also studied. The results show the effectiveness of the ANN in diagnosis of the machine condition. The proposed procedure requires only a few features extracted from the measured vibration data either directly or with simple preprocessing. The reduced number of inputs leads to faster training requiring far less iterations making the procedure suitable for on-line condition monitoring and diagnostics of machines.     

A multi-step progressive fault diagnosis method for rolling element bearing based on energy entropy theory and hybrid ensemble auto-encoder

It is meaningful to efficiently identify the health status of bearing and automatically learn the effective features from the original vibration signals. In this paper, a multi-step progressive method based on energy entropy (EE) theory and hybrid ensemble auto-encoder (HEAE), systematically blending the statistical analysis approach with the deep learning technology, is proposed for rolling element bearing (REB) fault diagnosis. Firstly, a preliminary detection about the REB health status is performed by the statistical analysis technique integrated with the EE theory. Secondly, if fault exists in REB, a new HEAE is constructed based on denoising auto-encoder and contractive auto-encoder to strengthen the feature learning ability and automatically extract the deep state features from the raw data. Subsequently, a modified t-distributed stochastic neighbor embedding (M-tSNE) algorithm is developed to achieve the features reduction to further improve the diagnosis efficiency. Finally, the low-dimensional representations after features reduction are as the inputs of softmax classifier to recognize the fault conditions. The proposed method is applied to the fault diagnosis of REB. The results confirm the effectiveness and superiority of the proposed method, and it is more suitable for the actual engineering applications compared with other existing methods.     

支持向量数据描述用于机械设备状态评估研究

本文提出了对机械设备运行状态进行评估的新方法———支持向量数据描述方法。该方法应用在机械故障诊断和状态监测中,仅仅依靠正常运行时的数据信号,而不需要故障数据,就可以监测机器的运行状态。给出了机组运行状态优劣的定量指标,从而为设备管理和预知维修提供科学的决策依据。将该方法应用于某炼油厂关键设备的运行状态评估中,及时、正确地评价出设备状态异常,为成功诊断出螺栓裂纹的早期故障提供帮助。