Race Fault Research Articles

A hybrid LSTM random forest model with grey wolf optimization for enhanced detection of multiple bearing faults

Bearing degradation is the primary cause of electrical machine failures, making reliable condition monitoring essential to prevent breakdowns. This paper presents a novel hybrid model for the detection of multiple faults in bearings, combining Long Short-Term Memory (LSTM) networks with random forest (RF) classifiers, further enhanced by the Grey Wolf Optimization (GWO) algorithm. The proposed approach is structured in three stages: first, time and frequency domain features are manually extracted from vibration signals; second, these features are processed by a dual-layer LSTM network, which is specifically designed to capture complex temporal relationships within the data; finally, the GWO algorithm is employed to optimize feature selection from the LSTM outputs, feeding the most relevant features into the RF classifier for fault classification. The model was rigorously evaluated using a dataset comprising six distinct bearing health conditions: healthy, outer race fault, ball fault, inner race fault, compounded fault, and generalized degradation. The hybrid LSTM-RF-GWO model achieved a remarkable classification accuracy of 98.97%, significantly outperforming standalone models such as LSTM (93.56%) and RF (98.44%). Furthermore, the inclusion of GWO led to an additional accuracy improvement of 0.39% compared to the hybrid LSTM-RF model without optimization. Other performance metrics, including precision, kappa coefficient, false negative rate (FNR), and false positive rate (FPR), were also improved, with precision reaching 99.28% and the kappa coefficient achieving 99.13%. The FNR and FPR were reduced to 0.0071 and 0.0015, respectively, underscoring the model’s effectiveness in minimizing misclassifications. The experimental results demonstrate that the proposed hybrid LSTM-RF-GWO framework not only enhances fault detection accuracy but also provides a robust solution for distinguishing between closely related fault conditions, making it a valuable tool for predictive maintenance in industrial applications.

Diagnosing multiple faults in rotating machinery using empirical mode decomposition and blind source separation methods

It is a challenging task to accurately diagnose multiple faults in a rotating machinery. In this paper, the authors have used empirical mode decomposition (EMD) and blind source separation (BSS) methods for this purpose. A portable rotating machinery setup is made in the laboratory and faults such as bearing inner and outer race faults, angular misalignment are created into it. The vibration data is collected using SPM made LEONOVA DIAMOND(r) FFT analyzer and processed using CONDMASTER RUBY(r) and MATLAB(r) softwares. Firstly, wavelet transform is applied to the signal to denoise it. After this, EMD is implemented to decompose the signal into different intrinsic mode functions (IMFs). After preprocessing these IMFs using the principal component analysis (PCA), BSS is used to extract fault-related information from the IMFs. A comparison of the results obtained from EMD and PCA-BSS is also presented in the paper. Keywords: Rotating machinery, Multiple faults, Vibration data, Wavelet transform, Empirical mode decomposition, Blind source separation, Principal component analysis.

Characterization of bearing health in a noisy environment using vibroacoustic approach

The bearing fault detection localized at outer race on a rotating machine operating in the same environment with another machine (whose wind tunnel generates a different noise) is investigated using acoustic and vibration analysis. The impact of increased acoustic and vibratory background noise on outer race defect detection is studied through spectral kurtogram representation and envelope analysis. The spectral power densities of these two types of noise clearly show that the nature of their associated radiation (airborne or solid-borne) has a different impact. The presence of a strongly amplified background noise demonstrates that the acoustic modality can detect the outer race defect peak at higher levels than the vibratory modality.

Analysis on the vibration signals of a novel double-disc crack rotor-bearing system with single defect in inner race

Previous studies have paid little attention to the dynamic modeling of multi-fault systems with both bearing defect and cracked rotor. In the current research, a double-disc rotor model with a breathing crack and fault bearing was established using the finite element method (FEM). The dual-impulse phenomenon associated with inner race defect was addressed as a time-varying external excitation. The effects of varying crack depths and inner race spall lengths were further investigated. The maximum error between simulation and experiment was found to be less than 5% for the dual-impulse time spacing. The calculated results indicate that the inner race defect causes a slight increase in the rotational frequency frotor and 2frotor, followed by a decrease in the 3frotor at low speeds, as observed using Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT). The influence of changes in crack depth is greater than the bearing defect in the system, a finding that is also confirmed at 1/2 critical speed. The phenomenon of frequency modulation observed in the simulation was also verified experimentally. The ball passing frequency on inner raceway fBPFI, frotor and mixed frequency components can be used to distinguish whether there is inner race defect.

Thrust ball bearing fault identification from visualized data using optimized deep network

Bearings are the most common components employed in machine parts. The bearings' movement facilitates the smooth motion. They also help with friction reduction. The faults in bearings are often caused by tribological parameters. Various methods have been developed to identify faults in bearings, but they often fail to predict these accurately. This work has concentrated on designing an effective fault-recognizing model. Therefore, a framework, the Zebra-based Radial Basis Prediction Mechanism (ZbRBPM), was proposed in this work. Initially, the bearing datasets are collected, and mineral oil (MO) lubrication is added to minimize wear and friction. The primary goal of the work is to detect and classify the fault in the thrust ball bearing. The bearing vibration data included a normal vibration signal, a ball fault, and inner and outer race faults. Hence, Zebra fitness is enhanced for the optimization of tribological parameters and fault identification. The proposed model is executed in the MATLAB system. Finally, performance criteria like accuracy, precision, recall, F-score, error rate, computation time, speed, specific wear rate, friction torque, and energy consumption are validated. The performance of the proposed ZbRBPM gains better accuracy rate as 99.5 %, 99 % of precision and f-score and 99.4 % of recall. Also it significantly reduced the prediction error rate into 0.5 % with lower computation time and very low wear and friction.

Rolling bearing fault detection method based on teager energy operator

Abstract This study presents a method for detecting subtle faults in rolling bearings by integrating singular value decomposition (SVD) and local modal decomposition (LMD). Initially, the original vibration signals undergo reconstruction in phase space and subsequent denoising using SVD. Following this, LMD is employed to generate multiple local modal functions (IMFs), with the most sensitive IMF components selected based on kurtosis-mean square deviation criteria. The Teager energy operator (TEO) is then computed for these selected IMFs. Ultimately, Fourier transform analysis of TEO is utilized to identify faults in the inner and outer races of rolling bearings. Test results conducted on publicly available data from Case Western Reserve University validate the accurate identification of inner and outer race faults using this approach.

Time-Series Feature Extraction by Return Map Analysis and Its Application to Bearing-Fault Detection

Developing new features for time-series characterization is a current challenge in data science and machine learning. In this paper, we propose a new metric based on a simple and efficient algorithm, namely, the return map. The return map analysis is well established in the field of non-linear dynamics, in particular, for fitting the parameters of a chaotic system from a waveform, or to attack a chaotic communication channel. We show that our metrics work well for both non-linear dynamics and time-series feature extraction problems in the field of machine learning. In an experiment aiming to classify vibration signals of normal and damaged bearings, we compare our method with two other methods that reported to have excellent accuracy, based on entropy and statistical feature distribution, respectively. We show that our method achieves higher accuracy with almost the lowest time costs, which was confirmed in experiments with two different datasets containing three main classes of bearings: normal, with inner race faults, and with outer race faults, having different damage origins and recorded in various conditions. In particular, for the dataset supplied by Case Western Reserve University, our method reached an accuracy of 100% at signals of 5000 sample points length, with a total time of 0.4 s required for feature estimation, while the entropy-based method reached an accuracy of 95% with a time of 100 s, and a statistical feature distribution method reached an accuracy of 93% with a total time of 1.9 s. Results show that the developed method is better suited to real-time bearing condition monitoring applications than most of the methods reported to date.

Efficient vibration analysis system using empirical mode decomposition residual signal and multi-axis data

The empirical mode decomposition (EMD) method is a technique that recursively decomposes an input signal into intrinsic mode functions (IMFs) by residual signals, primarily for identifying desirable features. The suggested algorithm observes the residual signal instead of the IMF, which lowers the computing load. The study introduces a new method for detecting bearing faults by enhancing signal extraction from sensor data using EMD and multi-axis feature extraction. This method streamlines the process by filtering out high-frequency noise and correlating residual signal information with analysis. The approach also enhances the signal-to-noise ratio (SNR) and feature signature identification using digital signal processing (DSP) techniques. The algorithm for vibration data analysis is tested for bearing failures, identifying shaft frequency and inner race bearing faults, which can be implemented in parallel. For the inner race fault bearing analysis, two-level EMD with a residual signal generates output similar to five-iteration EMD, saving 60% of computations. The use of spectral multiplication to multi-axis data processing produced a rise in the SNR of 18.32 dB to 20.92 dB for Y-axis and X-axis input, respectively. When compared to the single-axis IMF data computation, 20% fewer iterations are needed overall. A single-level EMD is adequate for calculating the rotational frequency of a healthy bearing. For the Y- and X-axis input, multi-axis analysis increases SNR by 10.68 dB and 13.14 dB, accordingly. This comprehensive strategy reduces computational complexity, improves fault detection accuracy, and minimizes noise impact, making it a promising solution for bearing fault detection.

Bearing race fault detection using an optomechanical micro-resonator.

Bearing fault detection plays a crucial role in ensuring machinery reliability and safety. However, the existing bearing-fault-detection sensors are commonly too large to be embedded in narrow areas of bearings and too vulnerable to work in complex environment. Here, we demonstrate an approach to distinguish the presence of race faults in bearings and their types by using an optomechanical micro-resonator. The principle of the amplitude-frequency modulation model mixing fault frequency with mechanical frequency is raised to explain the asymmetrical sideband phenomena detected by the optical microtoroidal sensor. Kurtosis estimation used in this work can distinguish normal and faulty bearings in the time domain with the maximum accuracy rate of 91.72% exceeding the industry standard rate of 90%, while the amplitude-frequency modulation of the fault signal and mechanical mode is introduced to identify the types of the bearing faults, including, e.g., outer race fault and inner race fault. The fault-detection methods have been applied to the bearing on a mimic unmanned aerial vehicle (UAV), and correctly confirmed the presence of fault and the type of outer or inner race fault. Our study gives new perspectives for precise measurements on early fault warning of bearings, and may find applications in other fields such as vibration sensing.

Methodology for the Detection of Contamination and Gradual Outer Race Faults in Bearings by Fusion of Statistical Vibration–Current Features and SVM Classifier

Bearings are one of the main components of induction motors, machines widely employed in today’s industries, making their monitoring a primordial task; however, most systems focus on measuring one physical magnitude to detect one kind of fault at a time. This research tackles the combination of two common faults, grease contamination and outer race damage, as lubricant contamination significantly impacts the life of the bearing and the emergence of other defects; as a contribution, this paper proposes a methodology for the diagnosis of this combination of faults based on a proprietary data acquisition system measuring vibration and current signals, from which time domain statistical and fractal features are computed and then fused using LDA for dimensionality reduction, ending with an SVM model for classification, achieving 97.1% accuracy, correctly diagnosing the combination of the contamination with different severities of the outer race damage, improving the classification results achieved when using vibration and current signals individually by 7.8% and 27.2%, respectively.

An Enhanced Modeling Framework for Bearing Fault Simulation and Machine Learning-based Identification with Bayesian-optimized Hyperparameter Tuning

Abstract Condition monitoring of rotating machinery offers a salient tool for predictive maintenance on rolling elements, subjected to continuous working loads, wear, fatigue, and degradation. In this study, an enhanced computational tool for bearing fault simulation and feature extraction is proposed. A subsequent identification scheme is realized, through Bayesian optimization of hyperparameters, including support vector classifier (SVC), gradient boosting (GBoost), random forest (RF), extreme gradient boosting (XBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). The proposed hyperparameter optimization technique stands out from traditional methods by offering a more informed and efficient pathway to optimal performance in predictive maintenance. Utilizing Bayesian optimization for hyperparameter tuning of machine learning models, which has not been extensively explored in this field, our approach demonstrates significant advancements. Typical instances of bearing faults, like inner race, outer race, and ball faults are considered. The analysis relies on extraction of statistical and engineering features form collected response signals, including kurtosis, root mean square, peak, and ridge factor. Highly influential variables are highlighted based on feature selection and importance algorithms, allowing bearing fault classification. We demonstrate that SVC and LightGBM produce over 97% of accuracy at low computational cost. This approach constitutes a scalable and robust framework for similar applications in engineering diagnostics.

IDENTIFYING INNER RACE FAULTS IN DEEP GROOVE BALL BEARING USING NONLINEAR MODE DECOMPOSITION AND HILBERT TRANSFORM

Abstract This study focuses on the analysis of vibration-based signatures obtained from deep groove ball bearings with faults on the inner race. Various time-frequency-based methods are commonly used to diagnose faults in bearings. However, due to the nonself-adaptive nature of these methods and the nonlinear and nonstationary signals produced by the faults, mode decomposition techniques are seen as promising methods. This paper presents a novel approach based on Nonlinear Mode Decomposition (NMD), which decomposes the complex signal into nonlinear modes. The data is taken from an online database of deep groove ball bearing with inner race faults of different sizes. This data is then subjected to NMD to extract nonlinear modes. Statistical parameters are applied to select a subset of significant nonlinear modes from the complete set. Finally, the Fast Fourier Transform is applied to the Hilbert Transform (HT) of the selected modes to see fault frequency and its higher harmonics resulting from the nonlinearity. Additionally, the instantaneous frequency and instantaneous phase, two key parameters acquired from the HT are also plotted for normal and faulty bearings, and the results are discussed in the paper. The proposed method offers a valuable approach for accurately detecting and diagnosing deep groove ball-bearing faults.

Comparison of non-linear vibration of outer race defective ball bearing using two defect functions

The aim of this article is to study the effects of evolution in defect size on vibration of a ball bearing by simulation of a ball bearing by developing a 2-DOF mathematical model and to compare the vibration responses of defective bearings obtained for two widely used defect functions, viz., rectangular function and half-sine wave function. MATLAB codes are developed to prepare a mathematical model of a ball bearing and to solve the differential equations of the model using the Runge-Kutta method. In the model, the mass supported by the bearing is considered as a lumped mass, and the contact between the races and the balls is considered as a series of springs, whose spring stiffness is obtained by using Hertz’s contact deformation theory. This model considers the contact deformation between the balls and the races and the additional displacement between the balls and the inner race due to radial clearance and defect geometry. The maximum possible radial displacement of the ball into the defect is obtained analytically and graphically from the race-ball-defect geometry. First, the impulses generated due to an outer race defect in the ball bearing are modeled using two different defect functions separately and their vibration responses are compared. Secondly, the effects of increase in defect length on vibration of the bearing are simulated separately for two defect functions, and then their responses are compared and analyzed. The results show that when the defect is modeled with a rectangular defect function, the vibration responses obtained are greater than when the defect is modeled with a half-sine wave defect function. And, vibration responses increase rapidly up to a certain level of defect length and then decrease with a further increase in defect length. The vibration analysis performed for different defect lengths can provide good support to vibration analysts and researchers.

A feature vector with insensitivity to the position of the outer race defect and its application in rolling bearing fault diagnosis

The fault diagnosis of rolling bearings is very important in industrial applications, which can avoid accidents and reduce operation and maintenance costs. Although the position of the bearing outer race defect has a significant impact on rolling bearing vibration response, most existing intelligent bearing fault diagnosis methods do not take this into account. In this paper, we establish a dynamic model of rolling bearing to clarify the influence of the outer race defect position on the dynamic response, and propose a feature vector that is insensitive to the outer race defect positions. First, the vibration characteristics of the outer race faults with different defect positions are analyzed, and the impact is evaluated using six indicators. Second, three indicators of insensitivity to the bearing outer race defect positions are constructed as the feature vector for bearing fault diagnosis. Finally, a bearing fault diagnosis method considering the positions of outer race defect is proposed based on the constructed feature vector and K nearest neighbor classifier. The diagnosis results of three datasets formed by experimental signals show that the constructed feature vector can separate different bearing states. Compared with the existing two diagnosis methods, the proposed diagnosis method obtains higher recognition accuracy, in the case of different outer race defect positions of the training set and the testing set. The above research results are expected to provide a reference for rolling bearing fault diagnosis, especially when considering the influence of the outer race defect positions.

A novel performance degradation assessment method for rotating machinery based on the fault information and the dynamic simulation

The performance degradation assessment (PDA) of key components such as gears and rolling bearings is the core technology of prognostics and health management for rotating machinery. Most existing PDA methods may have two deficiencies: (1) the assessment indicator constructed does not consider capturing fault characteristics directly; (2) feature selection is generally based on the measured data of different fault levels, which is difficult to obtain in actual processes; moreover, the selection results lack universality and are difficult to extend to other equipment. To address these issues, this paper proposes a novel PDA method based on fault information and dynamic simulation. First, anomaly detection is performed using four well-known indicators in combination with Mahalanobis distance. Secondly, fault identification is performed using envelope spectrum analysis on anomaly signals to determine the fault type, e.g. gear fault or outer race fault. Thirdly, based on the fault type information, the candidate feature set including fault-domain indicators is selected based on the established dynamic simulation signals to obtain a preliminary assessment vector for the first stage. The stability of the fault domain indicators which capture fault characteristics directly is tested through actual measured normal data. It is used as the second stage of selecting to obtain the assessment vector. Finally, the PDA indicator is calculated based on the assessment vector and Mahalanobis distance. Four experiment case studies demonstrate the proposed PDA method can effectively isolate faults with different defect sizes as well as track the whole performance degradation. The above analysis indicates that the proposed PDA method is expected to be used for the actual rotating machinery.

Dynamic modeling and analysis of rolling bearing faults under time-varying excitations considering defect deformation

Fault mechanism analysis is one of the methods in fault diagnosis, and the dynamic modeling of rolling bearing faults plays a crucial role in studying fault mechanisms. Existing dynamic fault models only consider the impact of fault size and bearing speed on the impact force, providing an incomplete description of the impact force. In order to more accurately describe the dynamic fault model of impact forces, this paper focuses on the deep groove ball bearing with outer race faults. Factors such as defect deformation, speed, and fault size are considered, and an instantaneous impact force excitation function is proposed. Based on this proposed excitation function, a dynamic model for the outer race fault of deep groove ball bearings is established. Finally, through simulation and experimental comparison, the results indicate that the fault characteristic frequencies and their harmonics of the model in this paper are closer to the actual fault characteristic frequencies, reducing the error by 1 to 2 Hz. Therefore, the model proposed in this paper is more effective and accurate, providing a more precise rolling bearing fault model for the study of fault mechanisms.

Research on bearing fault diagnosis technology based on machine learning

As industrial equipment complexity continues to rise, the importance of bearings within these systems has become more critical, given their pivotal role in equipment functionality. Bearing faults can result in severe production accidents and safety issues. Hence, there is an urgent need for advanced bearing fault diagnosis technology. This study concentrates on rolling bearings, analyzing their structural characteristics and key parameters to classify fault types—inner race faults, rolling element faults, and outer race faults. Utilizing a dataset of 80 sets of bearing factory data, time and frequency domain analyses are conducted, establishing seven feature parameters (five in the time domain and two in the frequency domain). This data is organized into a 7-dimensional matrix for subsequent analysis and model development. The K-Means algorithm is chosen for its effectiveness in automatically recognizing fault patterns in rolling bearings. Training on the 7-dimensional matrix identifies four clustering centers corresponding to normal conditions, inner race faults, rolling element faults, and outer race faults. The fault diagnosis system is implemented using Python, and algorithm optimization improves efficiency. The study concludes with insights drawn from the analysis and proposes optimization methods, which contributing to advancing bearing fault diagnosis technology, particularly addressing industrial equipment reliability and safety concerns.

New Approaches of Stochastic Models to Examine the Vibration Features in Roller Bearings

Machinery components undergo wear and tear over time due to regular usage, necessitating the establishment of a robust prognosis framework to enhance machinery health and avert catastrophic failures. This study focuses on the collection and analysis of vibration data obtained from roller bearings experiencing various fault conditions. By employing a combination of techniques sourced from existing literature, distinct configurations within vibration datasets were examined to pinpoint the primary defects in roller bearings. The significant features identified through this analysis were utilized to formulate optimized stochastic model equations. These models, developed separately for inner and outer race fault features in comparison to healthy bearing features under random conditions, offer valuable insights into machinery prognosis. The application of these models aids in effective maintenance management, optimization of machinery performance, and the minimization of catastrophic failures and downtime, thereby contributing to overall machinery reliability.

A Novel Wind Turbine Rolling Element Bearing Fault Diagnosis Method Based on CEEMDAN and Improved TFR Demodulation Analysis

Among renewable energy sources, wind energy is regarded as one of the fastest-growing segments, which plays a key role in enhancing environmental quality. Wind turbines are generally located in remote and harsh environments. Bearings are a crucial component in wind turbines, and their failure is one of the most frequent reasons for system breakdown. Wind turbine bearing faults are usually very localized during their early stages which is precisely when they need to be detected. Hence, the early diagnosis of bearing faults holds paramount practical significance. In order to solve the problem of weak pulses being masked by noise in early failure signals of rolling element bearings, a novel fault diagnosis method is proposed based on the combination of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and an improved TFR demodulation method. Initially, the decomposition of vibration signals using CEEMDAN is carried out to obtain several intrinsic mode functions (IMFs). Subsequently, a novel KC indicator that combines kurtosis and the correlation function is designed to select the effective components for signal reconstruction. Finally, an innovative approach based on the continuous wavelet transform (CWT) for multi-scale demodulation analysis in the domain of time–frequency representation (TFR) is also introduced to extract the envelope spectrum. Further fault diagnosis can be achieved by the identification of the fault characteristic frequency (FCF). This study focuses on the theoretical exploration of bearing faults diagnosis algorithms, employing modeling and simulation techniques. The effectiveness and feasibility of the proposed method are validated through the analysis of simulated signals and experimental signals provided by the Center for Intelligent Maintenance Systems (IMS) of the University of Cincinnati and the Case Western Reserve University (CWRU) Bearing Data Center. The method demonstrates the capability to identify various types of bearing faults, including outer race and inner race faults, with a high degree of computational efficiency. Comparative analysis indicates a significant enhancement in fault diagnostic performance when compared to existing methods. This research contributes to the advancement of effective bearing fault diagnosis methodologies for wind turbines, thereby ensuring their reliable operation.

Vibration behavior of a 3D-printed hybrid polymer bearing with outer race defect

PurposeIn industry applications, polymer hybrid bearings have become widespread in recent years owing to the lack of lubricant requirements, particularly in areas requiring hygiene. The additive manufacturing method gives significant advantages to have complex machinery parts, and it has become popular in the industry in recent years. However, it has some inherent disadvantages caused by layered deposition/addition of the materials, and the probability of the localized defect is much higher than in the conventional manufacturing methods. This study aims to investigate the effect of the outer race defect on the characteristics of vibration and service lifetime of hybrid polymer ball bearings produced with the stereolithography (SLA) additive manufacturing method.Design/methodology/approachIn this study, polymer bearings’ races were produced with the additive manufacturing SLA method, and the outer race defect was analyzed with measured vibrations.FindingsThe results show that the additive manufacturing method suggests a practical solution for producing a polymer hybrid ball bearing. On the other hand, the hybrid three-dimensional-printed bearing, which has an outer race defect, worked for approximately 8 h without any problems under a 1 kg load and a shaft speed of around 1,000 rpm. In addition, when there is a defect in the outer and/or inner race of the ball bearing, the crest factor and kurtosis of the vibration are higher than faultless ball bearing, as expected.Originality/valueThis paper provides valuable information on the lifetime and vibration characteristics of polymer hybrid ball bearing produced by means of additive manufacturing.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2023-0183/