Race Fault Research Articles

Detekcija otkaza kotrljajnih ležajeva primenom analize signala vibracija i kontrolne karte kumulativnih suma

Monitoring the condition of rotating machines is essential for the systems' safety, reducing maintenance costs, and increasing reliability. In this research, a fault detection system for bearings was developed using the vibration analysis technique with the statistical control chart approach. A test rig was first designed and constructed; then, various bearing faults, such as inner race and outer race faults, were simulated and examined in the test rig. After capturing the vibration signals at different bearing health conditions, the time-domain signal analysis technique was employed for extracting different indicative features. The obtained time domain features were then analyzed to find out the most fault-significant feature. Then, only one feature was selected to design the control chart for bearing health condition monitoring. The cumulative sum control chart (CUSUM was utilized since it can detect the small changes in bearing health states. The results showed the effectiveness of utilizing this method, and it was found that the percentage of the out-of-control points in the event of the combined cage and ball fault to the number of tested samples is greater than the other fault types.

Fault Characteristic Extraction by Fractional Lower-Order Bispectrum Methods

The generated signals generally contain a large amount of background noise when the mechanical bearing fails, and the fault signals present nonlinear and non-Gaussian feature, which have heavy tail and belong to α -stable distribution ( 1 < α < 2 ); even the background noises are also α -stable distribution process. Then it is difficult to obtain reliable conclusion by using the traditional bispectral analysis method under α -stable distribution environment. Two improved bispectrum methods are proposed based on fractional lower-order covariation in this paper, including fractional low-order direct bispectrum (FLODB) method, fractional low-order indirect bispectrum (FLOIDB) method. In order to decrease the estimate variance and increase the bispectral flatness, the fractional lower-order autoregression (FLOAR) model bispectrum and fractional lower-order autoregressive moving average (FLOARMA) model bispectrum methods are presented, and their calculation steps are summarized. We compare the improved bispectrum methods with the conventional methods employing second-order statistics in Gaussian and S α S distribution environments; the simulation results show that the improved bispectrum methods have performance advantages compared to the traditional methods. Finally, we use the improved methods to estimate the bispectrum of the normal and outer race fault signal; the result indicates that they are feasible and effective for fault diagnosis.

Experimental investigation of cylindrical bearing fault diagnosis with SVM

Abstract Cylindrical roller bearing is a basic principal component of so many rotating machineries. Continuous rotation of the various parts may lead to failure and identification of that in an early stage can prevent failure. Some of the Soft computing techniques like Classifier algorithms or neural networks are always helpful in finding out the various defects occurring when bearings are in operation. Here in this research, Wavelet packet decomposition with the use of mother wavelet ‘sym2′is used for finding out the most useful features for auto defining the condition of bearing. Experimental data are taken for the cylindrical bearing NJ305 and four different conditions like inner race defect, outer race defect, roller defect, and healthy bearing are taken for the consideration. Features like kurtosis, crest factor, energy, skewness, etc. are calculated for all the database of 900 signals. For auto-identification of the condition, initially, ANN is trained and tested, and after that various classifiers are also analyzed here for finding out the best method. It was observed that the Support Vector Machine technique as classifier stands the best among all with almost 96% efficiency.

Numerical model of a single stage gearbox under variable regime

Condition monitoring of gearboxes running under nonstationary regimes continue to be an important source of interest for industrialists and researchers. Despite the diversity of models in the literature, none allows simulating all cases of machine operations and failures modes in nonstationary regimes. This paper will discuss through a modeling approach how speed and load variations will influence the dynamic response of a single-stage gearbox supported by four rolling-element bearings, in the healthy case and in the case of gear and/or bearing defects. Combining 72 degrees of freedom and taking into consideration meshing and bearing nonlinear excitations, the proposed model allowed to compute the dynamic behavior of the real system for the case of gear tooth crack defect, pinion eccentricity defect, and the three localized bearing defects (outer race, inner race, and ball defects). Fast Fourier transform, Squared envelope spectrum, Short-time Fourier transform, and Kurtogram was used to reveal the vibration signature of the introduced defects under the different operating modes. A special envelope spectrogram was also designed to reveal the bearing defect frequencies variations in the case of a variable-driven speed.

Intelligent cross-condition fault recognition of rolling bearings based on normalized resampled characteristic power and self-organizing map

Intelligent bearing fault recognition under nonstationary conditions is still a challenge. This paper presents a novel intelligent cross-condition bearing fault recognition scheme. In this scheme, we propose a normalized resampled characteristic power (NRCP) feature, which is constructed based on the pulse-based order spectrums. Based on NRCP feature, the whole fault recognition strategy is developed. First, the resampled signals are obtained by pulse-based order tracking technique, and the order spectrums are produced by the joint application of Hilbert transform and fast Fourier transform. Second, the NRCP feature space is constructed based on the order spectrums. Then, the Laplacian score (LS) algorithm is applied to refine the NRCP features. Finally, the new features are fed into self-organizing map (SOM) to identify the health conditions of rolling bearings. The proposed method is experimentally validated to be able to differentiate health, outer race fault, inner race fault, and multiple fault bearings.

A comparative study of the vibration characteristics of railway vehicle axlebox bearings with inner/outer race faults

Increasing service time makes the axlebox bearing of railway vehicle vulnerable to develop a fault in inner or outer races, which can cause some serious adverse effects on a railway vehicle’s safe operation. To tackle this problem, we established a railway vehicle vertical-longitudinal dynamic model with inner/outer races faults of axlebox bearing and validated it by experimental data. We utilized the time-synchronous average (TSA) technology to filter the raw signals and studied their vibration features. The results show that the longitudinal vibration features are more sensitive for inner race fault identification, while the vertical vibration features are more suitable for outer race fault identification. For inner race fault identification, the indicator peak-to-peak value (PPV) that increases 1056% relative to the healthy state at the most severe fault performs the best sensitivity. For outer race fault identification, the indicator skewness value (SV) that increases 518% relative to the healthy state at the most severe fault exhibits the best performance. The research work can provide meaningful guidance for accurate diagnosis of axlebox bearing faults of railway vehicles.

Experimental investigation of cylindrical roller bearing for inner race defect under varying load

This paper presents an experimental investigation of inner race defect on cylindrical roller bearing under various speeds and radial load conditions. The several statistical features like RMS, crest factor, skewness, kurtosis, and frequency domain techniques like FFT, envelope spectrum have been extracted and compared it for defective and healthy bearing at various speeds and radial load to understand the performance of bearing. The experimental results indicate that increment in rotational speed and variation in radial load has a remarkable influence on various features of the signals.

Wind Turbine Bearing Fault Detection Using Adaptive Resampling and Order Tracking

Wind energy is growing increasingly popular in the United States, so it is imperative to make it as cost competitive as possible. Operations and Maintenance (O&M) make up 20-25% of the total cost of onshore wind projects. Unplanned maintenance contributes approximately 75% of the total maintenance costs (WWEA, 2012). Condition-based maintenance strategies intend to maximize the uptime by reducing to the amounts of unplanned maintenance. This should result in an overall decrease in the cost of maintenance. Wind turbines produce an interesting challenge, because their main shaft rotation is both slow and nonstationary. Through the use of adaptive resampling and order tracking, both of these challenges were combated as the bearing fault was identified in the order spectrum then tracked as it progressed. The fault was identified as an outer race defect on the main bearing that initiated sometime during or before installation. The total energy in the order spectrum around the bearing fault rate was identified as a potential front-runner for a prognostic parameter. This paper presents a case study application to operational wind turbine bearing data to demonstrate the ease and intuitiveness of combining adaptive resampling and order tracking to diagnose faults for slow, nonstationary bearings. Prognosis of remaining useful life is proposed with features extracted from the order spectrum, but additional data are needed to develop and demonstrate this analysis.

Spectrum analysis of rolling element bearing faults

The health status of these bearings efficiently and effectively can be monitor using the spectrum analysis. The rolling element bearings are of two types rolling and sliding rolling element bearings, and these Sliding rolling bearings are of two types, linear and journal rolling bearings. The combination of both roller and ball can be referred to as rolling element bearings and can be widely used in many applications. The usage can be wide a range because of the permit rotary motion of shafts, starting from aircraft gas turbines, power transmissions, electric motors, bicycles, roller skates and many more. As the rolling element bearings have many advantages, compared with other types of bearings. As they are commonly referred to as antifriction bearings because the amount of lubrication required is less. In this paper, a new dimension is added for the spectrum analysis of rolling element bearing faults detection, analysis and diagnosis. Her the rolling element bearing faults are studied in comparison with three major factors like the time domain, frequency domain and spectrum plot obtained for the rolling element faults. The spectrum obtained from the normal and inner, outer race faults are consider mapping the spectrum analysis in more effective way.

Cross‐domain bearing fault diagnosis with refined composite multiscale fuzzy entropy and the self organizing fuzzy classifier

AbstractIn this article, the use of refined composite multiscale fuzzy entropy (RCMFE) for cross‐domain diagnosis of bearings is introduced and verified with two publicly available datasets of varying operating conditions, a factor that challenges the diagnostic ability of trained models. For classification, the self organizing fuzzy (SOF) classifier is used. The diagnostic framework which primarily only involves extracting RCMFE feature and training the SOF classifier, is able to detect and isolate faults with over 97% accuracy when the classes are comprised of a single fault type and size. Compared to related works, the proposed approach does not require deep learning for feature extraction nor any domain adaptation technique as the RCMFE feature is robust against changing operating conditions. Furthermore, the method does not need target domain data during training. With regard to fault isolation, when the classes in the training data contain all the available fault sizes instead of a single size, the classifier can distinguish inner race faults from outer race and ball fault with an average accuracy of 96%. However, the accuracy for differentiating ball and outer race faults falls slightly to an average of 86%. Thus even for the latter arrangement which poses a tougher transfer learning problem, the proposed approach still performs very well.

Novel Technology Based on the Spectral Kurtosis and Wavelet Transform for Rolling Bearing Diagnosis

A novel diagnosis technology combining the benefits of spectral kurtosis and wavelet transform is proposed and validated for early defect diagnosis of rolling element bearings. A systematic procedure for feature calculation is proposed and rules for selection of technology parameters are explained. Experimental validation of the proposed method carried out for early detection of the inner race defect. A comparison between frequency band selection through wavelets and spectral kurtosis is also presented. It has been observed that the frequency band selected using spectral kurtosis provide better separation between healthy and defective bearings compared to the frequency band selection using wavelet. In terms of Fisher criterion the use of spectral kurtosis has a gain of 2.75 times compared to the wavelet.

Impulse excitation analysis of material defects in ball bearing

Bearing is an important element in the modern manufacturing rotating elements, almost all the rolling parts are supported by the bearing. The good maintainability of the bearing is one of the successful productivity balancing strategies. In this work made an attempt to diagnose the fault detection of bearings under the static condition; an effective bearing fault diagnostic technique is critically needed for the industries for early detection of bearing defects. Preventing breakdown/malfunction of machinery reduces the performance of the machine and productivity. The most used rotating element is the ball bearing in the industry 62xx series, in this work the ball bearing HCH 6201-2RS is considered for evaluation for inner race defect and outer race defects. An indigenous workbench was developed and validated it with the new and good working bearings. The amplitude and power spectrum wave formation was observed and recorded with the help of Lab View; a standard wave format of both good working conditions and brand-new bearings of the HCH 6201-2RS was identified.

Sparse representation of parametric dictionary based on fault impact matching for wheelset bearing fault diagnosis

Wheelset bearing is one of the crucial rotating elements in the train bogie. Detection of wheelset bearing defect comes with many challenges due to complex wheel/rail excitation and the horrible working condition. The parametric dictionary sparse representation provides a practical path to detect the weak fault of wheelset bearing. However, the parametric dictionary obtained by the classical correlation filtering algorithm (CFA) is hard to match the analyzed signal’s underlying fault impact characteristic. A novel parametric dictionary design algorithm named fault impact matching algorithm (FIMA) combining Orthogonal matching pursuit (OMP) is proposed to address the problem in this paper. The core of the FIMA mainly comprises two stages: partial segmentation and global analysis. Two indexes, correlation function (CF) and kurtosis, are used to comprehensively evaluate the partial and global matching degree between the Laplace wavelet and the signal. The proposed method’s effectiveness is verified by the fault simulation analysis and the practical wheelset bearing fault signals (outer and inner race fault). Some comparison studies demonstrate that the proposed method outperforms the CFA–OMP, the K-SVD–OMP and some time–frequency decomposition methods, such as EWT and VMD, in detecting the bearing weak defects.

Damping characteristics of integral squeeze film dampers on vibration of deep groove ball bearing with localized defects

Purpose This work aims to demonstrate the vibration suppression of the rotor system with localized defects on bearing using an integral squeeze film damper (ISFD). Design/methodology/approach Experiments were carried out to study the vibration characteristics of the rotor system with ISFD mounted on fault deep groove ball bearings. Three fault bearings including bearing with outer race defect, inner race defect and ball defect have been used in this paper. The results were compared by use of vibration acceleration level, continuous wavelet transform and envelope spectrum. Findings It was found that ISFD shows excellent damping and vibration attenuation characteristics of the rotor system with defective bearing. The fault bearing rotor system with external ISFD considerably reduces the vibration energy and amplitude compared with the system without ISFD. Originality/value There is a dearth of experimental research pertaining to vibration characteristics of rotor system support by defective bearings with ISFD. Besides, the test provides evidence for the application of ISFD in vibration control of the rotor system with incipient defects on bearing. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2020-0144/

Bearing fault diagnostic using machine learning algorithms

This study aims to enhance the condition monitoring of external ball bearings using the raw data provided by Paderborn University which provided sufficient data for motor current signal MCS. Three classes of bearings have been used: healthy bearings, bearings with an inner race defect, and bearings with outer race defect. Online data at different operating conditions, bearings, and faults extent of artificial and real damages have been chosen to provide the generalization and robustness of the model. After proper preprocessing to the raw data of vibration and MCS, time, frequency, and time–frequency domain features have been extracted. Then, optimal features have been selected using genetic algorithm. Artificial neural network with optimized structure using genetic algorithm has been implemented. A comparison between the performance of vibration and motor current signal has been presented. Moreover, our results are compared to previous work by using the same raw data. Results showed the potential of motor current signal in bearing fault diagnosis with high classification accuracy. Moreover, the results showed the possibility to provide a promised diagnostic model that can diagnose bearings of real faults with different fault severities using MCS.

Automated bearing fault classification based on discrete wavelet transform method

The prediction of the current state of the rotatory mechanical components like bearing needs to be of utmost accuracy for the efficient operation of the machine. As the bearing is normally employed at the supporting ends of the power transmission line, manual monitoring by the disengagement of the bearing is not a feasible option. Thus, there is a need for highly accurate machine learning algorithms that can successfully detect the abnormalities by capturing the running characteristics of the system. One such characteristic can be a vibration signal. In this article, various autonomous methods for condition monitoring are compared by employing machine learning and ensemble learning methods for fault classification in the bearings. Three different types of faults namely inner race fault, outer race fault, and ball fault have been considered. Discrete wavelet transform is applied to the raw signals obtained for healthy and these faulty conditions. The feature vector is extracted by decomposition and reconstruction of signals for ten different levels. The extracted feature vector is fed to three different classifiers: XGBoost, decision tree, and support vector machine. The results reveal that XGBoost outperforms the other two classifiers. The proposed manuscript signifies the capability of XGBoost classifier even if treated with readily fewer instances of raw signals.

A new methodology for detection of bearings faults in three-phase induction motor

This study shows a methodology for the detection, classification, and location of bearings that presented ball faults, cage faults, and outer race faults. For this study, a three-phase induction motor was used, in which the stator current and voltage signals were measured. By calculating the total harmonic distortion and using the Stockwell Transform, different characteristics were obtained in the electrical signals that allowed defining fault conditions in the bearing, classification of the type of fault, and the location of the defective bearing (fan side or load side). By calculating the difference between the total harmonic distortion of the current and voltage signal, it is possible to identify a threshold value of 0.004 that separates a healthy condition and a fault condition. The joint use of the Stockwell Transform and the Fisher Scoring Algorithm allows us to classify the fault conditions with an average precision of 92.5%. The location of a bearing with defects on the load side generates a greater amplitude in the signal compared to those located on the fan side. This behavior allows establishing a threshold value of 1.6 for ball faults and 0.001 for cage faults and outer race. Due to the results obtained, the algorithm proposed in the study is considered to be a tool with a high degree of reliability for the diagnosis of bearings in induction motors.

Mechanism and method for the full-scale quantitative diagnosis of ball bearings with an inner race fault

A full-scale quantitative diagnostic method is proposed in this study for inner-race fault-size estimation of ball bearings. The fault size is generally estimated using time interval between double impacts if the fault size is smaller than ball angular spacing. This method may be inapplicable when fault sizes become larger than integral multiples of ball angular spacing, which means they have the same time intervals. The analysis of dynamic modelling reveals the existence of a zone where load distribution is unaffected by the inner-race fault and the range of the zone varies monotonously with the fault size. The monotonic relationship between the proportion of fault-free components in axis-orbit time history and fault size can be explored using this mechanism. Hence, a novel evaluation indicator called the axis-orbit deterioration degree (AODD) is proposed in this study. The expression of this indicator is derived from theoretical analysis and signal processing methods. AODD in the simulation and experimental studies can be used to distinguish between faults that differ in size by integral multiples of the ball angular spacing. A new idea is proposed for the quantitative diagnosis method of ball bearings to improve the accuracy of fault size estimation.

Infrared Thermography-Based Fault Diagnosis of Induction Motor Bearings Using Machine Learning

Bearing is one of the most crucial parts in induction motor (IM) as a result there is a constant call for effective diagnosis of bearing faults for reliable operation. Infrared thermography (IRT) is appreciably used as a non-destructive and non-contact method to detect the bearing defects in a rotary machine. However, its performance is limited by insignificant information and string noise present in the infrared thermal image. To address this issue, an emergent two dimensional discrete wavelet transform (2D-DWT) based IRT method has been proposed in this article for diagnosing the different bearing faults in IM, namely, inner and outer race defects, and lack of lubrication. The dimensionality of the extracted features was reduced using principal component analysis (PCA) and thereafter the selected features were ranked in the order of most relevant features using the Mahalanobis distance (MD) method to achieve the optimal feature set. Finally these selected features have been passed to the complex decision tree (CDT), linear discriminant analysis (LDA) and support vector machine (SVM) for fault classification and performance evaluation. The classification results reveal that the SVM outperformed CDT and LDA. The proposed strategy can be used for self-adaptive recognition of bearing faults in IM which helps to avoid the unplanned and unwanted system shutdowns due to the bearing failure.

Applications of Fractional Lower Order Synchrosqueezing Transform Time Frequency Technology to Machine Fault Diagnosis

Synchrosqueezing transform (SST) is a high resolution time frequency representation technology for nonstationary signal analysis. The short time Fourier transform-based synchrosqueezing transform (FSST) and the S transform-based synchrosqueezing transform (SSST) time frequency methods are effective tools for bearing fault signal analysis. The fault signals belong to a non-Gaussian and nonstationary alpha (α) stable distribution with 1<α<2 and even the noises being also α stable distribution. The conventional FSST and SSST methods degenerate and even fail under α stable distribution noisy environment. Motivated by the fact that fractional low order STFT and fractional low order S-transform work better than the traditional STFT and S-transform methods under α stable distribution noise environment, we propose in this paper the fractional lower order FSST (FLOFSST) and the fractional lower order SSST (FLOSSST). In addition, we derive the corresponding inverse FLOSST and inverse FLOSSST. The simulation results show that both FLOFSST and FLOSSST perform better than the conventional FSSST and SSST under α stable distribution noise in instantaneous frequency estimation and signal reconstruction. Finally, FLOFSST and FLOSSST are applied to analyze the time frequency distribution of the outer race fault signal. Our results show that FLOFSST and FLOSSST extract the fault features well under symmetric stable (SαS) distribution noise.