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Fault Diagnosis of Rolling Bearing Based on Fast Nonlocal Means and Envelop Spectrum  [PDF]
Yong Lv,Qinglin Zhu,Rui Yuan
Sensors , 2015, DOI: 10.3390/s150101182
Abstract: The nonlocal means (NL-Means) method that has been widely used in the field of image processing in recent years effectively overcomes the limitations of the neighborhood filter and eliminates the artifact and edge problems caused by the traditional image denoising methods. Although NL-Means is very popular in the field of 2D image signal processing, it has not received enough attention in the field of 1D signal processing. This paper proposes a novel approach that diagnoses the fault of a rolling bearing based on fast NL-Means and the envelop spectrum. The parameters of the rolling bearing signals are optimized in the proposed method, which is the key contribution of this paper. This approach is applied to the fault diagnosis of rolling bearing, and the results have shown the efficiency at detecting roller bearing failures.
Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis  [PDF]
Jinde Zheng,Junsheng Cheng,Yu Yang
Shock and Vibration , 2014, DOI: 10.1155/2014/154291
Abstract: A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE), Laplacian score (LS), and support vector machines (SVMs) is proposed in this paper. Permutation entropy (PE) was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS) is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively. 1. Introduction The vibration signals of mechanical systems, especially for ones with fault, often show mutation, nonlinearity, and nonstationarity because of the strike, velocity chopping, structure transmutation, loading, and friction. Hence, it is very crucial for mechanical fault diagnosis to extract the fault feature information from the nonlinear and nonstationary signal. A primary method for dealing with the nonlinear and nonstationary signal is time-frequency analysis [1], which has been applied to the mechanical fault diagnosis field widely for its ability to provide local information both in time and frequency domains of vibration signals [2]. However, the time-frequency analysis method, such as wavelet transform or Hilbert-Huang transform [3, 4], which decomposes the vibration signal into several stationary monocomponent signals, cannot reflect the subtle dynamic changes of vibration signal effectively and, therefore, inevitably will have some limitations [5]. With the development of nonlinear dynamic theories, especially in recent years, a number of nonlinear parameters and methods, such as chaos theory, fractal dimension, and information entropy, have been applied to machine condition monitoring and fault diagnosis. For instance, Logan and Mathew elaborated the application of the correlation dimension to vibration fault diagnosis of rolling element bearing
Evaluation of Effectiveness of Wavelet Based Denoising Schemes Using ANN and SVM for Bearing Condition Classification  [PDF]
Vijay G. S.,Kumar H. S.,Srinivasa Pai P.,Sriram N. S.,Raj B. K. N. Rao
Computational Intelligence and Neuroscience , 2012, DOI: 10.1155/2012/582453
Abstract: The wavelet based denoising has proven its ability to denoise the bearing vibration signals by improving the signal-to-noise ratio (SNR) and reducing the root-mean-square error (RMSE). In this paper seven wavelet based denoising schemes have been evaluated based on the performance of the Artificial Neural Network (ANN) and the Support Vector Machine (SVM), for the bearing condition classification. The work consists of two parts, the first part in which a synthetic signal simulating the defective bearing vibration signal with Gaussian noise was subjected to these denoising schemes. The best scheme based on the SNR and the RMSE was identified. In the second part, the vibration signals collected from a customized Rolling Element Bearing (REB) test rig for four bearing conditions were subjected to these denoising schemes. Several time and frequency domain features were extracted from the denoised signals, out of which a few sensitive features were selected using the Fisher’s Criterion (FC). Extracted features were used to train and test the ANN and the SVM. The best denoising scheme identified, based on the classification performances of the ANN and the SVM, was found to be the same as the one obtained using the synthetic signal. 1. Introduction The detection of fault in the machinery, in its incipient stage itself, has gained prime importance as it avoids machine down time, catastrophic failure of the machinery, threat to human life, high maintenance costs, and so forth. The fault diagnostic techniques based on the vibration signal analysis have become popular in recent times [1, 2]. The problem of the strong noise components masking the weak characteristic signals has always posed challenges to the condition monitoring expert. Several wavelet based signal processing techniques aiming at denoising the measured signal so as to increase the Signal-to-Noise Ratio (SNR) and reduce the Root-Mean-Square Error (RMSE) have been proposed and tried by several researchers [3–7]. The details of the techniques used by some of the researchers have been explained in Section 2.2. The wavelet based denoising technique has gained popularity due to its effectiveness and ease of application [8]. It overcomes the difficulty of determining the resonant frequency of the system. Therefore, the wavelet technique has been adopted in this work for denoising the bearing vibration signals. The detail coefficients, obtained from the Discrete Wavelet Transform (DWT), generally include a large proportion of the high-frequency noise components along with some of the characteristic
Resonance-Based Nonlinear Demodulation Analysis Method of Rolling Bearing Fault  [PDF]
Lingli Cui,Daiyi Mo,Huaqing Wang,Peng Chen
Advances in Mechanical Engineering , 2013, DOI: 10.1155/2013/420694
Abstract: Numerous mechanical nonstationary fault signals are a mixture of sustained oscillations and nonoscillatory transients, which are difficult to efficiently analyze using linear methods. We propose a nonlinear demodulation analysis method based on resonance and apply it to the fault diagnosis of rolling bearings. Unlike conventional demodulation methods that use frequency-based analysis and filtering techniques, our nonlinear demodulation analysis method is a decomposition demodulation of the signals according to different resonance based on Q-factors. When a local rolling bearing fault such as pitting is present, the fault vibration signals consist of the regular vibration signals and noise (a high resonance component containing multiple simultaneous sustained oscillations) and a transient impulse signal (a low resonance component being a signal containing nonoscillatory transients of faults). The regular vibration signal is a narrowband signal that has a high Q-factor, and the transient impulse signal is a wideband signal that has a low Q-factor. Using our resonance-based nonlinear demodulation analysis method, we decompose the signal into high resonance, low resonance, and residual components. Then, we perform a demodulation analysis on the low resonance component that includes the fault information. We have verified the feasibility and validity of the algorithm by analyzing the results of experimental and engineering signals. 1. Introduction The rolling bearing is one of the most widely used general mechanical components in rotating machines. Its running state directly affects the performance of the whole machine. Detecting and diagnosing its faults can prolong service life and reduce production costs. Therefore, it is important to monitor the bearing’s condition to ensure operational safety, prevent serious accidents, and reduce production costs [1]. The local fault vibration signal of a rolling bearing is a typical nonstationary signal. Compared with a stationary signal, the distribution parameters and regularities of the rolling bearing fault vibration signal are time dependent. Moreover, massive noise can be inevitably introduced into its vibration signal during the generation and transmission process, because of complex operational conditions and harsh environments. This is a major inconvenience to the analysis, handling, and use of the signals. When the inner ring, outer ring, or the rolling element of the rolling bearing is damaged, a periodic mechanical impact occurs in the contact between the surface of the fault and the surfaces of other
A Fault Detection Method of Rolling Bearing Based on Wavelet Packet-cepstrum  [cached]
Jun Ma,Jiande Wu,Xiaodong Wang,Yugang Fan
Research Journal of Applied Sciences, Engineering and Technology , 2013,
Abstract: In this study, we put forward a fault detection method of rolling bearing based on the wavelet packet-cepstrum. Firstly, the original signal is decomposed using the wavelet packet. Secondly, calculate the energy of the decomposed sub-band reconstruction signal and select the relatively band which is concentrated on the fault energy. Finally, calculate cepstrum of the reconstruction signal to detect fault. The actual normal and fault data of the rolling bearing's outer ring is analyzed in applying this method in the MATLAB simulation circumstance. The result shows that the outer ring's failure frequency measured by the experiment is consistent with the theoretical calculation result.
Automatic Fault Classification of Rolling Element Bearing using Wavelet Packet Decomposition and Artificial Neural Network  [PDF]
Manish Yadav,Sulochana Wadhwani
International Journal of Engineering and Technology , 2011,
Abstract: In this work an automatic fault classification system is developed for bearing fault classification of three phase induction motor. The system uses the wavelet packet decomposition using ‘db8’ motherwavelet function for feature extraction from the vibration signal, recorded for various bearing fault conditions. The selection of best node of wavelet packet tree is performed by using best tree algorithmalong with minimum Shannon entropy criteria. The ten statistical features such as peak value, root mean square value (RMS), kurtosis, skewness etc. are extracted from the wavelet packet coefficient of optimal node. The extracted feature then was used to train and test neural network fault classification. The artificial neural network system was designed to classify the rolling element bearing condition: healthy bearing (HB) rolling element fault (REF), inner race fault (IRF) and Outer race fault (ORF) for fault classification. The over all fault classification rate is 98.33% of the artificial neural network fault classifier.
基于流形-奇异值熵的滚动轴承故障特征提取
Fault Feature Extraction Method for Rolling Bearing Based on Manifold and Singular Values Entropy
 [PDF]

王奉涛,陈守海,闫达文,,,崔立明,,
- , 2016, DOI: 10.16450/j.cnki.issn.1004-6801.2016.02.013
Abstract: 提出一种基于流形 奇异值熵的滚动轴承时频故障特征提取方法。首先,在HHT(Hilbert Huang transform,简称HHT)时频分析基础上,应用二维流形方法提取信号流行成分以达到对轴承故障特征进行降维和提取敏感参量的目的;然后,定义了奇异值熵来定量衡量不同故障状态下流行成分的差异;最后,将流形奇异值向量与概率神经网络相结合,有效实现了轴承故障样本分类。与一般的考虑欧式空间全局范围最优值的主分量(principal component analysis,简称PCA)方法及以向量为研究对象的一维流形方法不同,该方法直接以二维信息为研究对象,避免了一维流形算法需将二维信息转化为向量带来的信息损失,与PCA方法相比更能发现隐藏在高维数据流形结构中的局部数据特征。工程信号分析验证了该方法的有效性,为准确提取滚动轴承故障特征提供了一种可靠手段。
This paper proposes a fault feature extraction method based on manifold and singular values entropy. First, on the basis of HHT time-frequency analysis, a two-dimensional manifold method was used to extract a signal manifold ingredient to reduce dimensions and extract the sensitive parameters of the bearing fault feature. Second, singular values entropy was defined to quantitatively measure the differences of the manifold ingredient under different fault statuses. This novel method differs from the general PCA method in terms of the global scope optimum value of European space, and from the one dimensional manifold method in terms of a vector as the research object. The method directly uses two-dimensional information as the research object and thus avoids information loss for a one-dimensional manifold algorithm in the necessary process that transforms two-dimensional information into a vector. Moreover, it can easily find more local data characteristics hidden in a high-dimensional data manifold structure compared with the PCA method. Finally, a manifold singular value vector combined with a probabilistic neural network was used to achieve bearing fault samples classification effectively. Engineering signal analysis verified the effectiveness of the proposed method. This paper provides a reliable method to accurately extract the rolling bearing fault feature.
Application of genetic programming and soft morphological filters to motor rolling bearing fault diagnosis
柔性形态滤波和遗传规划在电机轴承故障诊断的应用

YU Xiang-tao,LU Wen-xiu,CHU Fu-lei,
于湘涛
,卢文秀,褚福磊

控制理论与应用 , 2009,
Abstract: Based on soft morphological filtering and genetic programming(GP), a motor rolling bearing fault diagnosis method is proposed. It is very difficult to filtrate the fault vibration signals from the strong noise background because the roller bearing fault diagnosis is a problem of multi-class classification of inner ring fault, outer ring fault and ball fault. Firstly, vibration signals are filtrated by soft morphological filters. Secondly, the normalized energy in different characteristic frequencies is utilized to identify the fault features of feature terminals of GP. An optimal motor rollingbearing fault classification model is obtained by reproducing, mutating and over-crossing. Experiment results demonstrate that this modeling is correct and precise.
A Signal Based Triangular Structuring Element for Mathematical Morphological Analysis and Its Application in Rolling Element Bearing Fault Diagnosis  [PDF]
Zhaowen Chen,Ning Gao,Wei Sun,Qiong Chen,Fengying Yan,Xinyu Zhang,Maria Iftikhar,Shiwei Liu,Zhongqi Ren
Shock and Vibration , 2014, DOI: 10.1155/2014/590875
Abstract: Mathematical morphology (MM) is an efficient nonlinear signal processing tool. It can be adopted to extract fault information from bearing signal according to a structuring element (SE). Since the bearing signal features differ for every unique cause of failure, the SEs should be well tailored to extract the fault feature from a particular signal. In the following, a signal based triangular SE according to the statistics of the magnitude of a vibration signal is proposed, together with associated methodology, which processes the bearing signal by MM analysis based on proposed SE to get the morphology spectrum of a signal. A correlation analysis on morphology spectrum is then employed to obtain the final classification of bearing faults. The classification performance of the proposed method is evaluated by a set of bearing vibration signals with inner race, ball, and outer race faults, respectively. Results show that all faults can be detected clearly and correctly. Compared with a commonly used flat SE, the correlation analysis on morphology spectrum with proposed SE gives better performance at fault diagnosis of bearing, especially the identification of the location of outer race fault and the level of fault severity. 1. Introduction Rolling element bearings are one of the most important and common components in rotating machinery. Their carrying capacity and reliability are essential for the overall machine performance. Therefore the fault diagnosis of rolling element bearing has been studied intensively for the security of mechanical systems [1]. When a fault in one surface of a bearing strikes another surface, a force impulse is generated which excites some vibration response in the bearing and machine system. The vibration response can be obtained and converted into vibration signal. As most information concerning the fault feature is contained in vibration signal, the vibration-based bearing fault diagnosis method has attracted extensive interests from both academia and industry [2, 3]. The vibration signals, usually indirect and nonlinear, are additionally masked by noise. Therefore an accurate signal processing and final diagnosis largely depend on the extraction of feature information from vibration signals. A number of studies have been conducted on vibration signal processing [4]. The most accepted approach for the demodulation and feature extraction of vibration signal, the envelope analysis (EA) technique [5, 6], has been widely used in the detection of mechanical failures since 1980s. However, a prior knowledge of the filtering band is
Rolling Bearing Diagnosis Based on LMD and Neural Network  [PDF]
Baoshan Huang,Baoshan Huang,Wei,Wei Xu
International Journal of Computer Science Issues , 2013,
Abstract: Inner ring pitting, the outer indentation and rolling element wear are typical faults of rolling bearing. In order to diagnose these faults rapidly and accurately, the paper proposes a novel diagnosis method of rolling bearing based on the energy characteristics of PF component and neural network by the vibration signal of local mean decomposition(Local mean decomposition, LMD). The vibration signal is decomposed into several PF components by the local mean decomposition, the calculated energy characteristics of the PF component are inputted to the neural network to identify the type of rolling bearing faults. At the same time, the genetic algorithm is introduced to optimize the structure parameters of neural network, which improves diagnostic rate and accuracy of faults. The results show that this method has a higher diagnosis and recognition rate for the typical faults of rolling bearing.
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