Outer Race Fault Research Articles

A comprehensive study on developing an intelligent framework for identification and quantitative evaluation of the bearing defect size

An intelligent framework is necessary to detect and analyze bearing defects in rotating machinery to prevent unexpected downtime and achieve performance per Industry 4.0 standards. This study presents a framework to detect faults and precisely quantify their size. The framework triggers an AI model to identify the defect and another model for quantitative evaluation of defect size. After analyzing the various classification and regression models, it has been found that The k-nearest neighbor (KNN) algorithm is suggested as the most effective AI model for identifying bearing defects. The ensemble tree is the most effective AI model for defect quantification. The results showed that the proposed algorithm can estimate the defect width reasonably. The maximum error in estimating the inner race, outer race, and roller defect widths was 2.474%, 14.534%, and 5.517%, respectively. The AI model's capacity to identify bearing defects of different sizes, which were not included in the training dataset, was also tested. The test yielded successful results. By using this framework, industries can prevent unexpected downtime, reduce maintenance costs, and enhance the performance and reliability of rotating machinery.

Comparison Study On Pinch-Hitting Vibration Signal Analysis for Automotive Bearing: I-KazTM and I-Kaz 3D

Rotating machines are now an essential part of the automotive industry. Meanwhile, a bearing is playing the most important component of rotating machinery. To sustain the system's smooth running, maintenance methods such as preventive maintenance, breakdown maintenance, and predictive maintenance are used. Under preventive maintenance, vibration analysis is used to diagnose machines bearing faults. The main objective is to recognize bearing defects in a mechanical device by acquiring signals from the bearing using data acquisition hardware. This analysis is conducted under various load torque conditions, speeds, and defect types. A modular hardware configuration consisting of an accelerometer acquires the vibration signal. The signals are analyzed by using I-kazTM and I-kaz 3D signal analysis and its main objective is to observe the degree of dispersion data from its mean point. This analysis resolves the issues associated with time domain analysis. This pinch-hitting analysis research was conducted in two stages. The first stage is an experimental process that uses 3 types of bearings, the healthy (BL), inner race fault (IRF), and defect at outer race (ORF) bearing on the Machine Fault Simulator and forces with a different type of speed (1000, 1500 and 2500 rpm) and load variation (0.0564, 0.564 and 1.1298 N-m). In the second stage, computing the coefficient value and plots of signal’s I-kazTM and I-kaz 3D based on the bearings type were done accordingly. As a result, the analysis for detecting inner race fault, the deviation percentage averages calculation obtained the I-kazTM coefficient shows a better result with 96.86% by comparing to the I-kaz 3D that achieves 94.20%. Similarly, for the outer race defect, I-kazTM lead with 65.40% compared to I-kaz 3D with only 54.82%.

Analysis of contact behaviours and vibrations in a defective deep groove ball bearing

Rolling element bearings are vital parts for rotating machinery. When a defect occurs on the surface of the bearing raceway or rolling element, giving rise to the abnormal vibration of the bearing and even the rotating machine. Thus, it is of great significance to understand the vibration characteristics of the bearing for fault diagnosis and machine condition monitoring. In recent decades, researches on defect-induced bearing vibration in machine condition monitoring have attracted increasing attention. Model-based investigation is a highly useful method for enhancing our understanding of defect-induced vibration and fault excitation mechanism of the bearing. It also serves as a basis for establishing the theory of bearing failure. With this in mind, a novel dynamic model of deep groove ball bearing (DGBB) with a localized outer race defect is proposed in this paper. In this model, the additional displacement excitation which is modelled as a piecewise function based on kinematics is considered, and the angle-based contact force is developed and modelled as a piecewise function taking into account the angular position, collision velocity, and impact force. In addition, an analytical formulation of the angle-varying effective stiffness matrix of the DGBB with a localized raceway defect of varying size is established. The proposed dynamic model is used to study the influences of different defect scenarios (i.e. angular positions and widths) on the time-varying contact stiffness, contact force, spectral characteristics, and bearing vibrations. The comparisons between the simulations and the experiments show that the proposed model is effective.

Experimental investigation for fault diagnosis of a single stage worm gearbox using response surface methodology

Gearbox transmission system is a fundamental factor in the industrial applications. If gearbox stop working due to different faults, it may break normal machine operation and cause a production loss. This research paper focused on fault analysis of combination of worm wheel and bearing by using root mean square (RMS) and response surface method (RSM). Faults considered on outer race and inner race of worm wheel bearing, on worm wheel tooth. These faults and load were considered as an important independent factor to understand their effects on RMS response of worm gearbox. Worm gearbox experimental setup is for laboratory experimentation and RSM for analysis. Twenty-seven experimental trial conducted for three level of parameters based on design of experiment (DOE). Vibration based response measured in frequency domain and root mean square parameter extracted for the fault analysis. Box behnken design RSM is implemented to investigate independent parameters effect on output parameter i.e. RMS. The result shows that effect of faulty inner race bearing is more on worm wheel as compared to faulty outer race bearing and fault parameter influence RMS than load parameter.

A Dynamic Model of Outer Race Defective Bearing Considering the Unbalanced Shaft-Bearing System With Experimental Simulation

Abstract In this study, the effects of the evolution of bearing outer race defect size and increase in speed on the vibration characteristics of a shaft-bearing system under unbalanced conditions are simulated and analyzed. A two degrees-of-freedom mathematical model is presented for a ball bearing used in an unbalanced shaft-bearing system. The contact stiffness between the races and the balls is considered as a series of springs is incorporated in the model. Hertzian contact deformation theory is used to obtain the contact stiffness. This model considers the contact deformation between the balls and the races, the additional displacement between the balls and the inner race due to radial clearance and due to defect geometry. The maximum possible radial displacement of the ball into the defect is calculated analytically using the groove radius, ball radius, and defect diameter. The rectangular function is used for modeling the defect. matlab codes are developed for modeling the bearing and for solving the differential equations of motion using the Runge–Kutta method. The vibration responses (peak and root-mean-square (RMS) values) obtained by modeling and by experimentation show similar vibration characteristics. The investigation shows that the values of statistical parameters initially increase with the increase in defect size and then decrease with a further increase in defect size. While peak and RMS increase with the increase in speed, crest factor and kurtosis decrease with the increase in speed. Peak is more sensitive for diagnosing spalls on outer race and its evolution. This study helps as an effective diagnosis of antifriction bearings having spalls on the outer race under unbalanced conditions.

Early rolling bearing fault diagnosis in induction motors based on on-rotor sensing vibrations

The traditional on-house sensing (OHS) accelerometer for vibration measurements causes poor signal-to-noise ratio (SNR) and complicated fault modulations, which increases the difficulty and complexity for early bearing fault diagnosis. To overcome these challenges, this paper develops a wireless triaxial on-rotor sensing (ORS) system to largely improve the SNR and deduces fast Fourier transform (FFT) and Hilbert envelope analysis for accurate early rolling bearing fault diagnosis, which largely improves accuracy and efficiency for early fault diagnosis. First, the development of the ORS system for wireless vibration measurements is given. Second, the theoretical diagnostic relationships between dynamic ORS signals and rolling bearing faults are derived for FFT and Hilbert envelope analysis for the first time. Finally, the induction motor tests with outer and inner race faults successfully validate that both simple FFT and Hilbert envelope analysis can achieve more robust early rolling bearing fault diagnosis compared to OHS measurements.

Precision fault prediction in motor bearings with feature selection and deep learning

In the disciplines of industrial machinery, mechanical engineering is beneficial to recognize motor performance for motors with HP power, torque transducer, dynamometer, and control electronics. The motivation is to address the need for more accurate and efficient fault prediction in machinery to prevent breakdowns, reduce maintenance costs, and improve overall reliability. In this work, deep learning classifiers used to classify ball defect inner race fault, outer race fault and normal motor performance in testing. With the aid of three distinct classifiers CNN, FFNN, and RBN; these suggested relative characteristics are assessed. In comparison to other current algorithms, the suggested methodology for classifying motor performance achieved maximum accuracy in each CNN test at 95.4% and 97.7%. The correlation and chi-square algorithms are used to find out the added characteristics and rank of features. The correlation technique provides relations between attributes, and the chi-square offers the optimal balance between precision and feature space. We discovered that the performance is enhanced overall by relative power characteristics. The suggested models might offer rapid responses with less complexity.

Noise-boosted weak signal detection in fractional nonlinear systems enhanced by increasing potential-well width and its application to mechanical fault diagnosis

Noise is seen as the annoying something for weak signal detection, but noise is beneficial to enhance weak signals of interest embedded in noise in nonlinear systems. Moreover, the fractional-order derivative can reinforce noise-boosted weak signal detection. However, the role of varying potential-well depth and width individually on noise-boosted weak signal detection in overdamped and underdamped fractional-order nonlinear systems has not been investigated yet. For this purpose, this paper designs a special bistable potential with varying potential-well depth and width individually to study their influences on weak signal detection numerically. Then, based on simulated conclusions a noise-boosted weak fault diagnosis method enhanced by increasing potential-well width is proposed to enhance weak fault characteristics of machinery, where the amplitude amplification factor is seen as an indicator to quantify the performance of weak signal detection. Finally, some simulations and a bearing fault experiment with an outer race defect were performed to demonstrate the proposed method. Simulated conclusions are identical to experimental ones and indicate that increasing potential-well width would improve noise-boosted weak signal detection but increasing potential-well depth would weaken it regardless of underdamped or overdamped fractional-order nonlinear systems. Nonetheless, increasing the potential-well width endlessly would make the amplitude at the outer race fault characteristic frequency unchanged. That is because the signal with the constant energy would not accelerate the particles to jump across the wide potential wells adequately, where it is not an optimal SR. The compared results with a classical denoising method demonstrate the superiority of the proposed method further.

An enhanced cyclostationary method and its application on the incipient fault diagnosis of induction motors

The cyclostationary analysis techniques have been extensively explored for the purpose of fault detection in rotating machinery. However, there are still huge challenges because of both limited detection frequency range and low fault identification accuracy. This paper proposes an improved cyclostationary method to enhance incipient fault features. Firstly, the continuous wavelet transform is used to accurately locate important frequency bands, and the fault modulation mechanism or fast kurtogram can be adopted to design the optimal wavelet transform scale factor. Secondly, the Teager-Kaiser energy operator is improved to be used in the frequency domain for the weak fault feature enhancement. Finally, fault features are presented in the cyclic frequency domain through spectral coherence and enhanced envelope spectrum. The proposed method is verified through both numerical simulation and experiments, including incipient half-broken rotor bar, and rolling bearing outer race faults in induction motors.

Development of Rolling Element Bearing Test Bench Facility And Application of Wavelet Transform for Fault Diagnosis

The aim of this experimental study is to develop an efficient rolling element bearing fault detection technique which could be effectively utilized for easy interpretation of bearing signal data. This paper deals with a new scheme for the diagnosis of localized defects in rolling element bearing based on Wavelet Transform. Vibration signal for healthy bearing, bearing with outer race fault, inner race fault and ball fault were acquired from a bearing test bench fabricated at Naval Institute of Aeronautical Technology, Kochi. The vibration signal was acquired using an accelerometer, control card and Data acquisition card. The acquired signal was further processed using a time frequency analysis technique known as Wavelet Transform. The experiments were carried out using different mother wavelets. However, defect features were best obtained using Complex Gaussian Wavelet of order 8, thereby rendering easier interpretation of the analysed data.

Enhancement in Bearing Fault Classification Parameters Using Gaussian Mixture Models and Mel Frequency Cepstral Coefficients Features

Last decades, rolling bearing faults assessment and their evolution with time have been receiving much interest due to their crucial role as part of the Conditional Based Maintenance (CBM) of rotating machinery. This paper investigates bearing faults diagnosis based on classification approach using Gaussian Mixture Model (GMM) and the Mel Frequency Cepstral Coefficients (MFCC) features. Throughout, only one criterion is defined for the evaluation of the performance during all the cycle of the classification process. This is the Average Classification Rate (ACR) obtained from the confusion matrix. In every test performed, the generated features vectors are considered along to discriminate between four fault conditions as normal bearings, bearings with inner and outer race faults and ball faults. Many configurations were tested in order to determinate the optimal values of input parameters, as the frame analysis length, the order of model, and others. The experimental application of the proposed method was based on vibration signals taken from the bearing datacenter website of Case Western Reserve University (CWRU). Results show that proposed method can reliably classify different fault conditions and have a highest classification performance under some conditions.

Efficient DCNN-LSTM Model for Fault Diagnosis of Raw Vibration Signals: Applications to Variable Speed Rotating Machines and Diverse Fault Depths Datasets

Bearings are the backbone of industrial machines that can shut down or damage the whole process when a fault occurs in them. Therefore, health diagnosis and fault identification in the bearings are essential to avoid a sudden shutdown. Vibration signals from the rotating bearings are extensively used to diagnose the health of industrial machines as well as to analyze their symmetrical behavior. When a fault occurs in the bearings, deviations from their symmetrical behavior can be indicative of potential faults. However, fault identification is challenging when (1) the vibration signals are recorded from variable speeds compared to the constant speed and (2) the vibration signals have diverse fault depths. In this work, we have proposed a highly accurate Deep Convolution Neural Network (DCNN)–Long Short-Term Memory (LSTM) model with a SoftMax classifier. The proposed model offers an innovative approach to fault diagnosis, as it obviates the need for preprocessing and digital signal processing techniques for feature computation. It demonstrates remarkable efficiency in accurately diagnosing fault conditions across variable speed vibration datasets encompassing diverse fault conditions, including but not limited to outer race fault, inner race fault, ball fault, and mixed faults, as well as constant speed datasets with varying fault depths. The proposed method can extract the features automatically from these vibration signals and, hence, are excellent to enhance the performance and efficiency to diagnose the machine’s health. For the experimental study, two different datasets—the constant speed with different fault depths and variable speed rotating machines—are considered to validate the performance of the proposed method. The accuracy achieved for the variable speed rotating machine dataset is 99.40%, while for the diverse fault dataset, the accuracy reaches 99.87%. Furthermore, the experimental results of the proposed method are compared with the existing methods in the literature as well as the artificial neural network (ANN) model.

Dynamics Modeling and Analysis of Rolling Bearings Variable Stiffness System with Local Faults

By analyzing the support of load-carrying rolling elements when the rolling elements fall into the fault position, the dynamics model of a rolling bearing variable stiffness system with local faults is proposed, considering the retention factor of the contact deformation. Then, this paper researches the change of effective contact stiffness, contact deformation, contact force, and the total effective stiffness of the rolling elements. The results show that the contact stiffness of the rolling elements abruptly decreases when the rolling elements fall into the fault position. The contact deformation and contact force of the load-carrying rolling elements in the load zone increase, rebalancing the external radial load while causing a sudden reduction in the total effective stiffness, resulting in the vibration of the system. When different rolling elements fall into the outer ring fault position, the change in total effective stiffness and the system response are equal in magnitude. Additionally, there is a significant outer race fault characteristic frequency accompanied by frequency multiplication in the fault characteristic spectrums. When different rolling elements fall into the inner race fault position, the total effective stiffness is modulated by the inner race rotation and varies dramatically, resulting in the amplitude of the system time domain vibration response also being modulated by the inner race rotation and varying dramatically. Additionally, there is a significant inner race rotational frequency accompanied by frequency multiplication, an inner race fault characteristic frequency accompanied by frequency multiplication, and a side frequency in the fault characteristic spectrums. The research can provide some reference for the effective diagnosis of the rolling bearing fault.

MACHINE LEARNING BASED FAULTY BEARING DIAGNOSIS IN CNC MACHINE

The prediction of faulty bearing in rotating machineries like CNC machine, induction motor, wind turbine etc. is very important. Bearings are essential parts of such machines and mechanical systems to reduce friction between moving parts and to support the weight of rotating machineries. The noise produced by the machine can make it difficult to detect a fault or diagnose a problem. This is because the noise can mask or obscure the signal that would indicate a fault. To overcome this challenge, researchers may need to use advanced signal processing techniques to separate the signal of interest from the background noise. In this proposed work the vibration signal responses of CNC machine bearing was studied during faulty and normal bearing conditions. Early faulty bearing diagnosis was made using Support Vector Machines (SVM) to identify whether a bearing is faulty or not, what type of fault it has (inner race, outer race, or rolling element fault). This model is effective when there is a clear boundary between the classes by finding a hyper plane that separates the data into different classes. To decompose the signal Fourier transform is used to analyze signals in the frequency domain. It decomposes a signal into its constituent frequencies. Once the model is trained and tested, we can visualize the accuracy, precision, recall, and F1 score using confusion matrix to show how many normal and faulty behavior instances were correctly or incorrectly classified by the model.

Instantaneous-Angular-Speed-Based Synchronous Averaging Tool for Bearing Outer Race Fault Diagnosis

Synchronous averaging (SA) is a powerful signal processing tool to enhance the feature of interesting periodic events by suppressing non-synchronous components. However, the features related to the rolling element bearing (REB) fault may not be effectively enhanced by SA under random slip conditions. To address this issue, two instantaneous angular speed (IAS)-based synchronous averaging frameworks are proposed in this study. Then, an improved negentropy indicator is proposed to characterize the richness of the REB fault information. Additionally, based on the estimation feature of the IAS signal, the effects of the encoder resolution and structure damping factor on the waveform related to the faulty REB are studied, respectively. Simulation and experiment results show that the proposed schemes can be used to enhance the feature of the REB fault under random slip conditions.

Comparison of Signal Processing Methods for Bearing Fault Detection

Abstract: Bearings with rolling elements are a typical part of rotating machinery. Vibration analysis is one of them that is frequently used to gauge the general wellbeing and condition of rotating machinery. Finding bearing defects is important in the pursuit of extremely reliable operations. The main relationship between vibration signal-based features and measured signals is that they can be used in order to assess the condition of a bearing. In order to examine the effectiveness of non-parametric harmonic approaches for defect identification using real motor data, the study recommends utilizing Thomson's Multitaper Estimation and Welch Periodogram Estimation. The researches show that this approach can identify outer race faults and inner race fault. The conclusive evidence points to Thomson's Multitaper Periodogram Estimation method as a successful technique for bearing fault identification.

Vibration Signal for Bearing Fault Detection using Random Forest

Based on the chosen properties of an induction motor, a random forest (RF) classifier, a machine learning technique, is examined in this study for bearing failure detection. A time-varying actual dataset with four distinct bearing states was used to evaluate the suggested methodology. The primary objective of this research is to evaluate the bearing defect detection accuracy of the RF classifier. First, run four loops that cycle over each feature of the data frame corresponding to the daytime index to determine the bearing states. There were 465 repetitions of the inner race fault and the roller element fault in test 1, 218 repetitions of the outer race fault in test 2, and 6324 repetitions of the outer race in test 3. Secondly, the task is to find the data for the typical bearing data procedure to differentiate between normal and erroneous data. Out of 3 tests, (22-23) % normal data was obtained since every bearing beginning to degrade usually exhibits some form of a spike in many locations, or the bearing is not operating at its optimum speed. Thirdly, to display and comprehend the data in a 2D and 3D environment, Principal Component Analysis (PCA) is performed. Fourth, the RF algorithm classifier recognized the data frame’s actual predictions, which were 99% correct for normal bearings, 97% accurate for outer races, 94% accurate for inner races, and 97% accurate for roller element faults. It is thus concluded that the proposed algorithm is capable to identify the bearing faults.

Detecting wind turbine anomalies using nonlinear dynamic parameters-assisted machine learning with normal samples

Anomaly detection is critical for the reliability and safety of wind turbine. Toward this objective, this paper proposes an anomaly detection scheme for wind turbine using only normal samples. Specifically, a novel nonlinear tool, generalized multiscale Poincare plots (GMPOP), is firstly developed to capture the behavior changes of vibration signals through scales. Subsequently, support vector data description (SVDD) is established to learn the acceptance region defined for anomaly detection from the GMPOP information under normal conditions. The feasibility of GMPOP is initially studied on simulated signals. Further, two datasets, from an experimental bearing and a 2 MW wind turbine, are analyzed to illustrate the evolution process. Comparing to such the state-of-the-art indicators as root mean square, permutation entropy and dispersion entropy, the GMPOP-based method can successfully detect the state anomalies of before the occurrence of the outer race fault and inner race fault, and provide anomaly alarms in advance. The study suggests that the proposed GMPOP is an effective way to assess the dynamical alteration of wind turbine. Moreover, compared with other baselines: auto-encoder, principal components analysis, minimum covariance determinant and exponentially weighted moving average control chart, the proposed detection model GMPOP-SVDD produces favorable performance regarding multiple aspects.

FPGA-Flux Proprietary System for Online Detection of Outer Race Faults in Bearings

Online fault detection in industrial machinery, such as induction motors or their components (e.g., bearings), continues to be a priority. Most commercial equipment provides general measurements and not a diagnosis. On the other hand, commonly, research works that focus on fault detection are tested offline or over processors that do not comply with an online diagnosis. In this sense, the present work proposes a system based on a proprietary field programmable gate array (FPGA) platform with several developed intellectual property cores (IPcores) and tools. The FPGA platform together with a stray magnetic flux sensor are used for the online detection of faults in the outer race of bearings in induction motors. The integrated parts comprising the monitoring system are the stray magnetic flux triaxial sensor, several developed IPcores, an embedded processor for data processing, and a user interface where the diagnosis is visualized. The system performs the fault diagnosis through a statistical analysis as follows: First, a triaxial sensor measures the stray magnetic flux in the motor’s surroundings (this flux will vary as symptoms of the fault). Second, an embedded processor in an FPGA-based proprietary board drives the developed IPcores in calculating the statistical features. Third, a set of ranges is defined for the statistical features values, and it is used to indicate the condition of the bearing in the motor. Therefore, if the value of a statistical feature belongs to a specific range, the system will return a diagnosis of whether a fault is present and, if so, the severity of the damage in the outer race. The results demonstrate that the values of the root mean square (RMS) and kurtosis, extracted from the stray magnetic field from the motor, provide a reliable diagnostic of the analyzed bearing. The results are provided online and displayed for the user through interfaces developed on the FPGA platform, such as in a liquid crystal display or through serial communication by a Bluetooth module. The platform is based on an FPGA XC6SLX45 Spartan 6 of Xilinx, and the architecture of the modules used are described through hardware description language. This system aims to be an online tool that can help users of induction motors in maintenance tasks and for the early detection of faults related to bearings.

Ranked feature-based data-driven bearing fault diagnosis using support vector machine and artificial neural network

This article analyzes a data-driven fault diagnosis method for rolling element bearing under different operating conditions. The work is aimed to incorporate the filter-type feature selection algorithms, namely, neighborhood component analysis, minimum redundancy maximum relevance, chi-square, and ReliefF algorithms for the feature ranking of the extracted time-domain statistical features from the collected vibration data of the five different bearing defect conditions (normal, outer race defect, ball defect, inner race defect, and combined inner and ball defect). The vibration data have been recorded and an envelope analysis technique for pre-processing the signal is used to identify the fundamental defect frequencies of all the bearings. The artificial neural network and the support vector machine are used to predict the comparative performance of the feature selection algorithms. The results demonstrate that the neighborhood component analysis algorithm achieves the highest accuracies for the bearing fault detection with both the support vector machine and artificial neural network among all the feature selection methods. Therefore, the effective feature ranking before the fault classification by the machine learning algorithms can lead to an efficient and reliable bearing fault diagnosis.