Fault Diagnosis Of Rolling Element Research Articles

Fault diagnosis of rolling element bearing based on the empirical wavelet transform technique and correlation coefficient

Fault diagnosis of rolling element bearing based on the empirical wavelet transform technique and correlation coefficient

Fault diagnosis of rolling element bearing using continuous wavelet transform and K- nearest neighbour

Rotating machines are a common class for machineries in industries and one of the root cause of failure of these machines are faults in rolling element bearing (REB). Therefore, to prevent machinery performance degradation and malfunctioning, an effective bearing fault diagnosis technique is vital. So, the use of different machine learning techniques proposes a new method of diagnosing REB defects. Machine learning (ML) method namely K-nearest neighbour (KNN) is used to classify different types of REB faults. In this article, vibration signals are acquired from a customized test rig for three conditions of deep groove ball bearings: Normal (N), defect on Inner Race (IR) and defect on Outer Race (OR) at different loads and maximum speed. Continuous Wavelet Transform (CWT) has been applied to the acquired signal and Seventeen Time Frequency Domain (TFD) statistical features were extracted from CWT coefficients. KNN classifier is trained using these TFD. The classification accuracy on test data i.e., 83.3% reveals that KNN can be effectively used to classify different conditions of REB.

Fault diagnosis of rolling element bearing based on spatiotemporal intrinsic mode decomposition

In view of the problem that source signals cannot be effectively separated in the process of blind source separation of similar nonstationary nonlinear signals, a spatiotemporal intrinsic mode decomposition method was proposed for bearing fault diagnosis. Spatiotemporal intrinsic mode decomposition can separate source signals and construct Fourier basis dictionaries and nonlinear signal models. The fault components can be separated by using this method in fault diagnosis. The proper initial phase function is selected for blind source separation of signals and signal decomposition components are obtained. By simulation analysis, spatiotemporal intrinsic mode decomposition than the fast independent component analysis method can more intuitive clearly separate the signals of admixed with large modulation components of correlation coefficient, and through the impact of component kurtosis index to judge fault, inherent modal decomposition method better prove time restore original bearing vibration signals and fault impact. Through the analysis and comparison of experimental data, the spatiotemporal intrinsic mode decomposition method has a significant effect on the fault diagnosis and analysis of rolling bearing outer ring, inner ring, and can intuitively express the fault characteristic frequency and frequency doubling through the analysis of envelope spectrum. In processing the industrial data, the spatiotemporal intrinsic mode decomposition method can also find the frequency characteristics of inner ring fault more clearly and accurately. Therefore, the spatiotemporal intrinsic mode decomposition method can solve the problem of blind source separation and realize fault diagnosis in rolling bearing fault field.

Fault diagnosis of rolling element bearing using ACYCBD based cross correlation spectrum

Rolling element bearings are crucial components in all kinds of rotating machinery. Its fault detection is of great importance, as it ensures the performance of the whole machine. Periodic transient impulses caused by bearing defects are usually submerged in strong background noise which poses a challenge for effective fault feature extraction. To detect bearing faults reliably, a new fault feature extraction method is presented. First, the adaptive maximum second-order cyclostationary blind deconvolution is utilized to recover bearing fault-related impulses, while the optimal filter length is chosen based on the harmonic significance index which quantifies the diagnostic information contained in a deconvoluted signal. Second, cross-correlation is calculated between the teager energy operator and the envelope of the deconvoluted signal to further eliminate the irrelevant noise. Finally, fast fourier transform is employed to acquire the cross-correlation spectrum and the fault features can be extracted successfully. The performance of the proposed method is verified on both simulation signals and experimental signals acquired from a test rig. The superior abilities of noise reduction and fault detection are shown clearly when compared with some traditional method.

Early weak fault diagnosis of rolling element bearing based on resonance sparse decomposition and multi-objective information frequency band selection method

Vibration signals of rolling element bearing’s early weak fault are often submerged by some interference components. To extract early weak fault features accurately, a weak fault feature enhancement method of rolling element bearing based on resonance sparse decompositionand multi-objective information frequency band selection is proposed. This method makes full use of resonance sparse decomposition in filtering the interferences and multi-objective information frequency band selection in enhancing impulsive and cyclostationary features of rolling element bearing’s early weak fault simultaneously. First, resonance sparse decomposition is used as the preprocessing program of multi-objective information frequency band selection to filter the interferences (such as rotating frequency with its harmonics) of rolling element bearing’s early weak fault characteristic components. Then, the filtered components containing main fault information are analyzed by MIFBS to establish the best band-pass filter. Finally, the envelope spectrum analysis is applied on the filtered vibration signal, and the fault characteristic frequency with its harmonics is extracted. To achieve the optimal output parameters of multi-objective information frequency band selection, fusion indexes based on time- and frequency-domain estimators are proposed and used to balance the enhancement of impulsive and cyclostationary characteristics of rolling element bearing’s early weak fault signals. Compared with most of the existing methods mainly based on single time-domain estimators or frequency-domain estimators to improve performance spectral kurtosis, the proposed fusion indexes overcome their defects and could enhance the impulsive and cyclostationary features of rolling element bearing’s early weak fault simultaneously. Effectiveness of the proposed method is verified through simulation and experiment. Besides, its advantage over the other related methods is also presented.

Fault diagnosis of rolling element bearing using autonomous harmonic product spectrum method

Maintenance planning plays a critical role in the process industry, where any unplanned maintenance may lead to a significant loss. Condition monitoring happens to aid maintenance planning and has become an inherent part of the maintenance activity. Physical parameters such as vibration, acoustic emission, current, etc., are used for condition monitoring, out of which vibration is the most preferred parameter and is widely used in the industry. Vibration data is measured near to bearings, which themselves are monitored for their condition, and hence rolling element bearing (REB) is the focus of this study. REBs are monitored for the presence of a fault in them as well as for their severity. Fault diagnosis of REB using harmonic product spectrum (HPS) is proposed in this study. The proposed methodology's novelty lies in the signal pre-processing step, whose output is fed to the HPS method, which is used for defective raceway identification. The efficacy of HPS is assessed with parameter optimized Variational mode decomposition (VMD) and classical bandpass filtering method as pre-processors. It is observed that the HPS delivers better diagnostic results with the VMD method than the bandpass filtering method. Non-dominated sorting particle swarm optimization algorithm is deployed for parameter optimization of VMD. HPS combined with VMD as pre-processor forms an autonomous HPS(AHPS) algorithm, whose input is measured signal and output is defect frequency. The process is so designed that a raw signal, when fed to the algorithm, delivers the result as identification of a defective raceway. Unlike previously developed methods, the proposed method needs no manual intervention. Results obtained from simulated signals and signals recorded through experiments validate that the proposed methodology can be used effectively for fault diagnosis of REB.

Fast bearing fault diagnosis of rolling element using Lévy Moth-Flame optimization algorithm and Naive Bayes

Fault diagnosis is part of the maintenance system, which can reduce maintenance costs, increase productivity, and ensure the reliability of the machine system. In the fault diagnosis system, the analysis and extraction of fault signal characteristics are very important, which directly affects the accuracy of fault diagnosis. In the paper, a fast bearing fault diagnosis method based on the ensemble empirical mode decomposition (EEMD), the moth-flame optimization algorithm based on Lévy flight (LMFO) and the naive Bayes (NB) is proposed, which combines traditional pattern recognition methods meta-heuristic search can overcome the difficulty of selecting classifier parameters while solving small sample classification under reasonable time cost. The article uses a typical rolling bearing system to test the actual performance of the method. Meanwhile, in comparison with the known algorithms and methods was also displayed in detail. The results manifest the efficiency and accuracy of signal sparse representation and fault type classification has been enhanced.

Fault diagnosis of rolling element bearing based on symmetric cross entropy of neutrosophic sets

A novel symmetric single-valued neutrosophic cross entropy (SVNCE) measure based upon a newly developed symmetric measure of fuzzy cross entropy is established and then applied it for identifying defects of bearings installed in a test rig and axial piston pump. A 3-level wavelet packet transformation (WPT) is used for extracting and decomposing the fault features associated with the vibrational signals. The new evaluated SVNCE values between knowledge of various faults types and real testing samples provide useful evaluation information of fault types. The concluded results reveal that the proposed SVNCE measure furnishes better fault identification accuracy when compared with the existing model based upon correlation coefficient of simplified neutrosophic sets (SNSs). Furthermore, the proposed SVNCE measure offers consistent and feasible results and is capable for holding the optimal fault type selection under sensitive analysis.

Fault diagnosis of rolling element bearing weak fault based on sparse decomposition and broad learning network

Rolling element bearings are widely used in rotating machinery and, at the same time, they are easily damaged due to harsh operating environments and conditions. As a result, rolling element bearings are critical to the safe operation of the mechanical devices. The incipient fault information extraction of rolling bearings mainly faces the following difficulties: (1) The fault signal is too weak. (2) The fault mechanism and the dynamic model of the rolling bearing system are complex. (3) The oscillations caused by the fault shocks are overlapped due to the smaller impact between two adjacent faults. (4) The impact interval of the fault will change randomly. To overcome the aforementioned difficulties, a connection network constructed by resonance-based sparse signal decomposition (RSSD) and broad learning system (BLS) without the need for deep architecture, namely RSSD-BLS, is proposed for intelligent fault diagnosis. We construct RSSD-BLS by input layer, RSSD decomposition layer, feature layer and output layer. So, when the observed vibration signals are the input layer, the network first uses RSSD to decompose the raw vibration signal into high resonance components and low resonance components. Then, the network obtains energy spectrum features of high resonance components which decomposed by RSSD to extract the unique features in the feature. Finally, the network recognizes different fault conditions in the output layer. Through comparing with commonly used intelligent network diagnosis method, the superiority of the proposed RSSD-BLS is verified.

Fault diagnosis of rolling element bearing based on artificial neural network

This paper proposes the expert system for accurate fault detection of bearing. The study is based upon advanced signal processing method as wavelet transform and artificial intelligence technique as artificial neural network (ANN) and K-nearest neighbor (KNN), for fault classification of bearing. An adaptive algorithm based on wavelet transform is used to extract the fault classifying features of the bearing from time domain signal. These features have been used as inputs to proposed ANN models and the same features have also been used for KNN. Dedicated experimental setup was used to perform the test upon the bearing. Single data set for four fault conditions of bearing is collected to train ANN and KNN. The processed and normalized data was trained by using backpropagation multilayer perceptron neural network. The results obtained from ANN are compared with KNN, ANN results proved to be highly effective for classification of multiple faults.

Fault diagnosis of rolling element bearing based on a new noise-resistant time-frequency analysis method

When fault arises in the rolling element bearing, the time-domain waveform of fault vibration signal will take on the characteristic of cyclostationarity, and the spectral correlation (SC) or spectral correlation density (SCD) basing on second order cyclic statistic is an effective cyclostationarity signal processing method. However, when the fault signal is surrounded by strong background noise, the traditional signal processing methods such envelope demodulation analysis and SC would not work effectively. The paper improves the SC method and a new time-frequency analysis method naming improved spectral correlation (ISC) is proposed. The proposed method is much more noise-resistant than SC through the verification of simulation analysis results. Besides, it takes on modulation phenomenon usually when fault arises in the rolling element bearing and the aim of fault feature extraction is to extract the fault characteristic frequency only or cyclic modulation frequency and the modulated frequency or carrier frequency buried in the object vibration signal is neglected. So, the paper improves the ISC further and the improved ISC (IISC) is proposed. The IISC will extract the modulation frequency only and it has the advantages of much clearer expression effect and better extraction effect. The effectiveness and feasibility of the proposed method are verified through the three kinds of fault (inner race fault, outer race fault and rolling element fault) of rolling element bearing. Besides, the advantages of the proposed method over the other relative time-frequency analysis methods such as ensemble empirical mode decomposition (EEMD) and spectral kurtosis (SK) are also presented in the paper.

Fault diagnosis of rolling element bearing based on wavelet kernel principle component analysis-coupled hidden Markov model

Different description results will be obtained when apply hidden Markov model (HMM) to the two different channel signals from the same data collection point respectively. Besides, wrong fault diagnosis result might be obtained because fault feature information would not be described comprehensively by using only one single channel signal. In theory, two channel signals collected form the same data collection point will contain much more fault information than the single channel signal contain, but the coupled phenomenon might occur between the two channel signals. Coupled hidden Markov model (CHMM) is the improved method of HMM and it can fuse the information of two channel signals from the same data collection point efficiently, so much more reliable diagnosis result could be obtained by using CHMM than by using HMM. Stated thus, the fault diagnosis method of rolling element bearing based on wavelet kernel component analysis (WKPCA)-CHMM is proposed: Firstly, use WKPCA as fault feature vectors extraction method to increase the efficiency of the proposed method. Then apply CHMM to the extracted fault feature vectors and satisfactory fault diagnosis result is obtained at last. The feasibility and advantages of the proposed method are verified through experiment.

Enhancement of fault diagnosis of rolling element bearing using maximum kurtosis fast nonlocal means denoising

Abstract In this paper, a modified nonlocal means denoising (NL-means) algorithm is proposed for rolling element bearing fault diagnosis. Although, nonlocal means denoising is widely used in image processing, this algorithm is rarely used in 1-D signal processing. The present work deals with application of 1-D nonlocal means denoising method for enhancement of fault signature in rolling element bearings. The parameters for the NL-means method are obtained by maximizing kurtosis value of bearing vibration signal. The proposed method is compared with minimum entropy deconvolution (MED) technique and the results indicate that the proposed method performs better for bearing fault diagnosis. The method is shown to be robust against various noise levels. Further, envelope spectrum of bearing vibration signal is also used to obtain characteristic bearing defect frequencies.

Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings

This paper proposes a hybrid system named as HGSA-ELM for fault diagnosis of rolling element bearings, in which real-valued gravitational search algorithm (RGSA) is employed to optimize the input weights and bias of ELM, and the binary-valued of GSA (BGSA) is used to select important features from a compound feature set. Three types fault features, namely time and frequency features, energy features and singular value features, are extracted to compose the compound feature set by applying ensemble empirical mode decomposition (EEMD). For fault diagnosis of a typical rolling element bearing system with 56 working condition, comparative experiments were designed to evaluate the proposed method. And results show that HGSA-ELM achieves significant high classification accuracy compared with its original version and methods in literatures.

Numerical and experimental analysis of vibratory signals for rolling bearing fault diagnosis

The detecting technique of the mechanical anomaly has been a hot topic in the scientific and engineering community. Vibration analysis has been frequently applied in the condition monitoring and fault diagnosis of rolling element bearings. Unfortunately, the vibration signals collected from a faulty bearing are generally nonstationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method that combines the singular value decomposition (SVD) and local mean decomposition (LMD) is proposed. SVD is a non-parametric technique which has been widely used to eliminate the noise and enhance the impulsive features, then LMD decompose the purified signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. Finally, the proposed method is applied to experimental data and the results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing.DOI: http://dx.doi.org/10.5755/j01.mech.22.3.11962

Fault diagnosis of rolling element bearing compound faults based on sparse no-negative matrix factorization-support vector data description

The bispectrum of rolling element bearing compound faults contains abundant fault characteristic information, and how to extract the fault feature effectively is a key problem. The fault diagnosis method of rolling element bearing compound faults based on Sparse No-Negative Matrix Factorization (SNMF)-Support Vector Data Description (SVDD) is proposed in the paper. The figure handling method SNMF is used firstly in fault feature extraction of the bispectrums of rolling element bearing different kinds of compound faults and the sparse coefficient matrices of the bispectrums are obtained. The sparse coefficient matrices are used as training and test input vectors of SVDD. At last, the three kinds of rolling element bearing compound faults (inner race outer race compound faults, outer race rolling element compound faults and inner race outer race rolling element compound faults) are classified correctly. In order to verify the advantages of the proposed method, the diagnosis results of the same three kinds of rolling element bearing compound faults based on No-Negative Matrix Factorization (NMF)-SVDD is used as comparison. The proposed method provides a new idea for fault diagnosis of rolling element bearing compound faults.

Comparison of denoising schemes and dimensionality reduction techniques for fault diagnosis of rolling element bearing using wavelet transform

This paper presents the evaluation of five wavelets-based denoising schemes in order to select the best possible scheme for denoising bearing vibration signals and dimensionality reduction techniques using artificial neural network (ANN). Vibration signals from four conditions of rolling element bearing (REB) namely normal (N), defect on inner race (IR), defect on ball (B) and defect on outer race (OR) have been denoised using interval-dependent denoising scheme, which is the best possible scheme. The denoised signal is subjected to discrete wavelet transform (DWT) to extract 17 statistical features. Principal component analysis (PCA)-based dimensionality reduction technique (DRT) namely PCA alone, Kernel-PCA (KPCA) alone, PCA using SVD and KPCA using SVD have been used for reducing the dimension of the features. It is found that KPCA using SVD resulted in highest prediction accuracy using ANN, making it suitable for effective REB fault diagnosis. [Received 30 November 2015; Revised 17 June 2016; Accepted 20 June 2016]

Fault feature extraction of rolling element bearings using sparse representation

Influenced by factors such as speed fluctuation, rolling element sliding and periodical variation of load distribution and impact force on the measuring direction of sensor, the impulse response signals caused by defective rolling bearing are non-stationary, and the amplitudes of the impulse may even drop to zero when the fault is out of load zone. The non-stationary characteristic and impulse missing phenomenon reduce the effectiveness of the commonly used demodulation method on rolling element bearing fault diagnosis. Based on sparse representation theories, a new approach for fault diagnosis of rolling element bearing is proposed. The over-complete dictionary is constructed by the unit impulse response function of damped second-order system, whose natural frequencies and relative damping ratios are directly identified from the fault signal by correlation filtering method. It leads to a high similarity between atoms and defect induced impulse, and also a sharply reduction of the redundancy of the dictionary. To improve the matching accuracy and calculation speed of sparse coefficient solving, the fault signal is divided into segments and the matching pursuit algorithm is carried out by segments. After splicing together all the reconstructed signals, the fault feature is extracted successfully. The simulation and experimental results show that the proposed method is effective for the fault diagnosis of rolling element bearing in large rolling element sliding and low signal to noise ratio circumstances.

Fault diagnosis of rolling element bearing based on S transform and gray level co-occurrence matrix

Time-frequency analysis is an effective tool to extract machinery health information contained in non-stationary vibration signals. Various time-frequency analysis methods have been proposed and successfully applied to machinery fault diagnosis. However, little research has been done on bearing fault diagnosis using texture features extracted from time-frequency representations (TFRs), although they may contain plenty of sensitive information highly related to fault pattern. Therefore, to make full use of the textural information contained in the TFRs, this paper proposes a novel fault diagnosis method based on S transform, gray level co-occurrence matrix (GLCM) and multi-class support vector machine (Multi-SVM). Firstly, S transform is chosen to generate the TFRs due to its advantages of providing frequency-dependent resolution while keeping a direct relationship with the Fourier spectrum. Secondly, the famous GLCM-based texture features are extracted for capturing fault pattern information. Finally, as a classifier which has good discrimination and generalization abilities, Multi-SVM is used for the classification. Experimental results indicate that the GLCM-based texture features extracted from TFRs can identify bearing fault patterns accurately, and provide higher accuracies than the traditional time-domain and frequency-domain features, wavelet packet node energy or two-direction 2D linear discriminant analysis based features of the same TFRs in most cases.

An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis

Ensemble empirical mode decomposition (EEMD) represents a significant improvement over the original empirical mode decomposition (EMD) method for eliminating the mode mixing problem. However, the added white noises generate some tough problems including the high computational cost, the determination of the two critical parameters (the amplitude of the added white noise and the number of ensemble trials), and the contamination of the residue noise in the signal reconstruction. To solve these problems, an adaptively fast EEMD (AFEEMD) method combined with complementary EEMD (CEEMD) is proposed in this paper. In the proposed method, the two critical parameters are respectively fixed as 0.01 times standard deviation of the original signal and two ensemble trials. Instead, the upper frequency limit of the added white noise is the key parameter which needs to be prescribed beforehand. Unlike the original EEMD method, only two high-frequency white noises are added to the signal to be investigated with anti-phase in AFEEMD. Furthermore, an index termed relative root-mean-square error is employed for the adaptive selection of the proper upper frequency limit of the added white noises. Simulation test and vibration signals based fault diagnosis of rolling element bearing under different fault types are utilized to demonstrate the feasibility and effectiveness of the proposed method. The analysis results indicate that the AFEEMD method represents a sound improvement over the original EEMD method, and has strong practicability.