Showing papers on "Bearing (mechanical) published in 2015"

Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression

Abstract: The detection, diagnostic, and prognostic of bearing degradation play a key role in increasing the reliability and safety of electrical machines, especially in key industrial sectors. This paper presents a new approach that combines the Hilbert-Huang transform (HHT), the support vector machine (SVM), and the support vector regression (SVR) for the monitoring of ball bearings. The proposed approach uses the HHT to extract new heath indicators from stationary/nonstationary vibration signals able to tack the degradation of the critical components of bearings. The degradation states are detected by a supervised classification technique called SVM, and the fault diagnostic is given by analyzing the extracted health indicators. The estimation of the remaining useful life is obtained by a one-step time-series prediction based on SVR. A set of experimental data collected from degraded bearings is used to validate the proposed approach. The experimental results show that the use of the HHT, the SVM, and the SVR is a suitable strategy to improve the detection, diagnostic, and prognostic of bearing degradation.

An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings

Abstract: The remaining useful life (RUL) prediction of rolling element bearings has attracted substantial attention recently due to its importance for the bearing health management. The exponential model is one of the most widely used methods for RUL prediction of rolling element bearings. However, two shortcomings exist in the exponential model: 1) the first predicting time (FPT) is selected subjectively; and 2) random errors of the stochastic process decrease the prediction accuracy. To deal with these two shortcomings, an improved exponential model is proposed in this paper. In the improved model, an adaptive FPT selection approach is established based on the $3\sigma$ interval, and particle filtering is utilized to reduce random errors of the stochastic process. In order to demonstrate the effectiveness of the improved model, a simulation and four tests of bearing degradation processes are utilized for the RUL prediction. The results show that the improved model is able to select an appropriate FPT and reduce random errors of the stochastic process. Consequently, it performs better in the RUL prediction of rolling element bearings than the original exponential model.

Rolling bearing fault diagnosis using an optimization deep belief network

Abstract: The vibration signals measured from a rolling bearing are usually affected by the variable operating conditions and background noise which lead to the diversity and complexity of the vibration signal characteristics, and it is a challenge to effectively identify the rolling bearing faults from such vibration signals with no further fault information. In this paper, a novel optimization deep belief network (DBN) is proposed for rolling bearing fault diagnosis. Stochastic gradient descent is used to efficiently fine-tune all the connection weights after the pre-training of restricted Boltzmann machines (RBMs) based on the energy functions, and the classification accuracy of the DBN is improved. Particle swarm is further used to decide the optimal structure of the trained DBN, and the optimization DBN is designed. The proposed method is applied to analyze the simulation signal and experimental signal of a rolling bearing. The results confirm that the proposed method is more accurate and robust than other intelligent methods.

Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings

Abstract: Condition-based maintenance, which includes both diagnosis and prognosis of faults, is a topic of growing interest for improving the reliability of electrical drives. Bearings constitute a large portion of failures in rotational machines. Although many techniques have been successfully applied for bearing fault diagnosis, prognosis of faults, particularly predicting the remaining useful life (RUL) of bearings, is a remaining challenge. The main reasons for this are a lack of accurate physical degradation models and limited labeled training data. In this paper, we introduce a data-driven methodology, which relies on both time and time–frequency domain features to track the evolution of bearing faults. Once features are extracted, an analytical function that best approximates the evolution of the fault is determined and used to learn the parameters of an extended Kalman filter (KF). The learned extended KF is applied to testing data to predict the RUL of bearing faults under different operating conditions. The performance of the proposed method is evaluated on PRONOSTIA experimental testbed data.

Detection of Localized Bearing Faults in Induction Machines by Spectral Kurtosis and Envelope Analysis of Stator Current

Abstract: Early detection of faults in electrical machines, particularly in induction motors, has become necessary and critical in reducing costs by avoiding unexpected and unnecessary maintenance and outages in industrial applications. Additionally, most of these faults are due to problems in bearings. Thus, in this paper, experimental bearing fault detection of a three-phase induction motor is performed by analyzing the squared envelope spectrum of the stator current. Spectral kurtosis-based algorithms, namely, the fast kurtogram and the wavelet kurtogram, are also applied to improve the envelope analysis. Experimental tests are performed, considering outer bearing faults at different stages, and the results are promising.

Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

Abstract: Rolling bearing faults are generally slowly progressive; therefore, the development of an effective diagnostic technique could be worth detecting such faults in their incipient phase and preventing complete failure of the motor. The methods proposed in the literature for this purpose are mainly based on measuring and analyzing vibration and current. Here, a novel technique based on the stray flux measurement in different positions around the electrical machine is proposed. The main advantages of this method are due to the simplicity and the flexibility of the custom flux probe with its amplification and filtering stage. The flux probe can be easily positioned on the machines and adapted to a wide range of power levels. This paper also reports an extensive survey on the stray-flux-based fault detection methods for induction motors, prior to introducing a novel sensor/diagnostic scheme.

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

Abstract: 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

Characterization of non-linear bearings using the Hilbert-Huang transform

Abstract: Changes in the performance of bearings can significantly vary the distribution of internal forces and moments in a structure as a result of environmental or operational loads. The response of a bearing has been traditionally idealized using a linear model but a non-linear representation produces a more accurate picture at the expense of modelling complexity and computational time. In this article, a lead rubber bearing is idealized using the hysteretic Bouc–Wen model. The Hilbert–Huang transform is then employed to characterize the features of the non-linear system from the instantaneous frequencies of the bearing response to a time-varying force. Instantaneous frequencies are also shown to be a useful tool in detecting sudden damage to the bearings simulated by a reduction in the effective stiffness of the force-deformation loop.

Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine

Abstract: Since roller bearings are the key components in rotating machinery, detecting incipient failure occurring in bearings is an essential attempt to assure machinery operational safety. With a view to design a well intelligent system that can effectively correlate multiple monitored variables with corresponding defect types, a novel intelligent fault diagnosis method with multivariable ensemble-based incremental support vector machine (MEISVM) is proposed, which is testified on a benchmark of roller bearing experiment in comparison with other methods. Moreover, the proposed method is applied in the intelligent fault diagnosis of locomotive roller bearings, which proves the capability of detecting multiple faults including complex compound faults and different severe degrees with the same fault. Both experimental and engineering test results illustrate that the proposed method is effective in intelligent fault diagnosis of roller bearings from vibration signals.

Characterizing friction in sliding isolation bearings

Abstract: SUMMARY The force–displacement behavior of the Friction Pendulum ™ (FP) bearing is a function of the coefficient of sliding friction, axial load on the bearing and effective radius of the sliding surface. The coefficient of friction varies during the course of an earthquake with sliding velocity, axial pressure and temperature at the sliding surface.Thevelocityandaxial pressure onthebearing dependonthe responseofthesuperstructuretotheearthquake shaking. The temperature at an instant in time during earthquake shaking is a function of the histories of the coefficient of friction, sliding velocity and axial pressure, and the travel path of the slider on the sliding surface. A unified framework accommodating the complex interdependence of the coefficient of friction, sliding velocity, axial pressure and temperature is presented for implementation in nonlinear response-history analysis. Expressions to define the relationship between the coefficient of friction and sliding velocity, axial pressure, and temperature are proposed, based on available experimental data. Response-history analyses are performed on FP bearings with a range of geometrical and liner mechanical properties and static axial pressure. Friction is described using five different models that consider the dependence of the coefficient of friction on axial pressure, sliding velocity and temperature. Frictional heating is the most important factor that influences themaximum displacementoftheisolation systemand floor spectraldemandsifthestaticaxialpressureishigh. Isolation system displacements are not significantly affected by considerations of the influence of axial pressure and velocity on the coefficient of friction. Copyright © 2014 John Wiley & Sons, Ltd.

Improved Fault Diagnosis of Ball Bearings Based on the Global Spectrum of Vibration Signals

Abstract: This research deals with the discrimination between conditions of faults in rolling element bearings based on a global spectral analysis This global spectral analysis allows to obtain spectral features with significant discriminatory power These features are extracted from the envelope spectra of vibration signals without prior knowledge of the bearings specific parameters and the characteristic frequencies These extracted spectral features will then be the global spectral signature produced by the bearing faults Since the signature produced by the faults in bearing balls is very weak, and hard to be detected and identified, this paper proposes the linear discriminant analysis as part of the global spectral analysis method in order to improve the diagnosis of ball faults The application on experimental vibration data acquired from bearings containing different types of faults with different small sizes shows the proficiency of the overall method The Bhattacharyya distance is used to confirm the efficiency of the obtained results

Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine

Abstract: Roller bearings are widely used in rotating machinery and one of the major reasons for machine breakdown is their failure. Vibration based condition monitoring is the most common method for extracting some important information to identify bearing defects. However, acquired acceleration signals are usually noisy, which significantly affects the results of fault diagnosis. Wavelet packet decomposition (WPD) is a powerful method utilized effectively for the denoising of the signals acquired. Furthermore, Ensemble empirical mode decomposition (EEMD) is a newly developed decomposition method to solve the mode mixing problem of empirical mode decomposition (EMD), which is a consequence of signal intermittence. In this study a combined automatic method is proposed to detect very small defects on roller bearings. WPD is applied to clean the noisy signals acquired, then informative feature vectors are extracted using the EEMD technique. Finally, the states of the bearings are examined by labeling the samples using the hyperplane constructed by the support vector machine algorithm. The data were generated by means of a test rig assembled in the labs of the Dynamics and Identification Research Group in the mechanical and aerospace engineering department, Politecnico di Torino. Various operating conditions (three shaft speeds, three external loads and a very small damage size on a roller) were considered to obtain reliable results. It is shown that the combined method proposed is able to identify the states of the bearings effectively.

Adaptive Multiscale Noise Tuning Stochastic Resonance for Health Diagnosis of Rolling Element Bearings

Abstract: The analysis of vibration or acoustic signals is most widely used in the health diagnosis of rolling element bearings. One of the main challenges for vibration or acoustic bearing diagnosis is that the weak signature of incipient defects is generally swamped by severe surrounding noise in the acquired signals. This problem can be solved by the stochastic resonance (SR) approach, which is to enhance the desired signal by the aid of noise. This paper presents an adaptive multiscale noise tuning SR (AMSTSR) for effective and efficient fault identification of rolling element bearings. A new criterion, called weighted power spectrum kurtosis (WPSK), is proposed as the optimization index without prior knowledge of the bearing fault condition. The WPSK concerns both the kurtosis in signal power spectrum and the similarity to a sinusoidal signal in signal waveform, thus it can balance the enhancement of possible characteristic frequency in the frequency domain and the regularity of the signal in the time domain for the SR performance. Two parameters in the AMSTSR, including the cutoff wavelet decomposition level and the tuning parameter, are simultaneously optimized based on the WPSK index through the artificial fish swarm algorithm. The AMSTSR is further applied to the health diagnosis of rolling element bearings and four experimental case studies verify the effectiveness of the proposed method in adaptive identification of the bearing characteristic frequencies.

Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier

Abstract: The rolling element bearing is among the most frequently encountered component in a rotating machine. Bearing fault can cause machinery breakdown and lead to productivity loss. A bearing fault diagnosis method has been proposed based on multi-scale permutation entropy (MPE) and adaptive neuro fuzzy classifier (ANFC). In this paper, MPE is applied for feature extraction to reduce the complexity of the feature vector. Extracted features are given input to the ANFC for an automated fault diagnosis procedure. Vibration signals are captured for healthy and faulty bearings. Experiment results pointed out that proposed method is a reliable approach for automated fault diagnosis. Thus, this approach has potential in diagnosis of incipient bearing faults.

Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution.

Abstract: The early fault characteristics of rolling element bearings carried by vibration signals are quite weak because the signals are generally masked by heavy background noise. To extract the weak fault characteristics of bearings from the signals, an improved spectral kurtosis (SK) method is proposed based on maximum correlated kurtosis deconvolution (MCKD). The proposed method combines the ability of MCKD in indicating the periodic fault transients and the ability of SK in locating these transients in the frequency domain. A simulation signal overwhelmed by heavy noise is used to demonstrate the effectiveness of the proposed method. The results show that MCKD is beneficial to clarify the periodic impulse components of the bearing signals, and the method is able to detect the resonant frequency band of the signal and extract its fault characteristic frequency. Through analyzing actual vibration signals collected from wind turbines and hot strip rolling mills, we confirm that by using the proposed method, it is possible to extract fault characteristics and diagnose early faults of rolling element bearings. Based on the comparisons with the SK method, it is verified that the proposed method is more suitable to diagnose early faults of rolling element bearings.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet Decomposition and Multi-Scale Permutation Entropy

Abstract: This paper presents a rolling bearing fault diagnosis approach by integrating wavelet packet decomposition (WPD) with multi-scale permutation entropy (MPE). The approach uses MPE values of the sub-frequency band signals to identify faults appearing in rolling bearings. Specifically, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of sub-frequency band signals by means of the WPD method. Then, each sub-frequency band signal is divided into a series of subsequences, and MPEs of all subsequences in corresponding sub-frequency band signal are calculated. After that, the average MPE value of all subsequences about each sub-frequency band is calculated, and is considered as the fault feature of the corresponding sub-frequency band. Subsequently, MPE values of all sub-frequency bands are considered as input feature vectors, and the hidden Markov model (HMM) is used to identify the fault pattern of the rolling bearing. Experimental study on a data set from the Case Western Reserve University bearing data center has shown that the presented approach can accurately identify faults in rolling bearings.

Critical Assessment 13: Elimination of white etching matter in bearing steels

Abstract: One form of damage due to rolling contact fatigue is the formation of localised regions of extremely hard material forming within the body of a bearing. These regions have a relatively homogeneous structure and hence etch mildly with respect to the surrounding unaffected matrix. They, therefore, appear white in a darker background when examined using optical microscopy. We assess here the cause of this damage and propose solutions that are backed by evidence that already is available. The issue is important because of the spate of unexpected failures in large wind turbine bearings and of generic importance in determining the life of more well behaved bearing applications.

Thermal characteristics analysis and experimental study on the high-speed spindle system

Abstract: In order to avoid the sudden failure of high-speed spindles in the actual machining process caused by an excessive temperature rise at the design stage, a three-dimensional (3D) finite element analysis (FEA) model was proposed to conduct transient thermal-structure interactive analysis of a high-speed spindle. The FEA model considered thermal contact resistance (TCR) at solid joints and bearing stiffness to improve the accuracy of traditional thermal models which ignored TCR. However, TCRs at solid joints and bearing stiffness were often ignored in traditional thermal models of high-speed spindles. This caused inaccuracies in traditional thermal models. The heat generation of the built-in motor was calculated based on the efficiency analysis method proposed by Bossmanns and Tu [1]. Based on the quasi-static mechanics analysis of rolling bearing, the heat generation and stiffness of bearings were calculated by applying the Newton-Raphson algorithm to improve the convergence. The Weierstrass-Mandelbrot (W-M) function, a function of fractal parameters, was used to characterize the rough surface morphology of bearing rings. The fractal parameters were identified by the structure function method and the measurement data of bearing ring’s surface morphology, and a contact mechanics model was developed to calculate the contact parameters used in the model of TCR. Then, a new predictive model for TCR was proposed based on M-T model. The above boundary conditions were applied to the FEA model, and thermal equilibrium experiments were conducted to validate the effectiveness of the model. The results showed that the FEA model was much more accurate than the traditional model which ignored TCRs at solid joints and bearing stiffness.