Vibration response mechanism of faulty outer race rolling element bearings for quantitative analysis

: Rolling element bearings play a vital role in rotating machines such as motors, pumps, turbines, and gearboxes. Any malfunction in these components can cause sudden equipment failure, unplanned downtime, loss of production, and increased repair expenses. Therefore, timely and reliable fault detection is necessary to maintain operational safety and enhance system dependability. This study proposes an intelligent fault diagnosis method for rolling element bearings based on transient Finite Element Analysis (FEA). A transient FEA model is constructed to capture the time-dependent dynamic behavior of the bearing under varying loads, rotational speeds, and defect conditions. The model allows detailed investigation of parameters such as stress distribution, deformation, contact interactions, and vibration characteristics throughout the operating cycle. Different fault types, including defects in the inner race, outer race, and rolling elements, are introduced into the simulation to obtain distinctive dynamic patterns that assist In accurate fault identification. The dynamic responses obtained from the simulations are further analyzed using advanced signal processing methods and intelligent computational techniques. Relevant features are extracted from the time-domain and frequency-domain signals and supplied to machine learning models for automatic identification of fault categories and their severity levels. This approach achieves reliable diagnostic performance while minimizing the need for extensive experimental testing and physical trials. In addition, it supports predictive maintenance by enabling early detection of potential defects before serious damage occurs. The findings confirm that the integration of transient Finite Element Analysis with intelligent data-driven analysis forms a robust and efficient framework for condition monitoring and timely fault diagnosis of rolling element bearings in industrial environments.

PurposeThe purpose of this article is to present a new application of pursuit‐based analysis for diagnosing rolling element bearing faults.Design/methodology/approachIntelligent diagnosis of rolling element bearing faults in rotating machinery involves the procedure of feature extraction using modern signal processing techniques and artificial intelligence technique‐based fault detection and identification. This paper presents a comparative study of both the basis and matching pursuits when applied to fault diagnosis of rolling element bearings using vibration analysis.FindingsFault features were extracted from vibration acceleration signals and subsequently fed to a feed forward neural network (FFNN) for classification. The classification rate and mean square error (MSE) were calculated to evaluate the performance of the intelligent diagnostic procedure. Results from the basis pursuit fault diagnosis procedure were compared with the classification result of a matching pursuit feature‐based diagnostic procedure. The comparison clearly illustrates that basis pursuit feature‐based fault diagnosis is significantly more accurate than matching pursuit feature‐based fault diagnosis in detecting these faults.Practical implicationsIntelligent diagnosis can reduce the reliance on experienced personnel to make expert judgements on the state of the integrity of machines. The proposed method has the potential to be extensively applied in various industrial scenarios, although this application concerned rolling element bearings only. The principles of the application are directly translatable to other parts of complex machinery.Originality/valueThis work presents a novel intelligent diagnosis strategy using pursuit features and feed forward neural networks. The value of the work is to ease the burden of making decisions on the integrity of plant through a manual program in condition monitoring and diagnostics particularly of complex pieces of plant.

Fault diagnosis of rolling element bearings (REBs), one type of essential mechanical element, has been actively researched; recent research has focused on the use of deep-learning-based approaches. However, conventional deep-learning-based fault-diagnosis approaches are vulnerable to various operating speeds, which greatly affect the vibration characteristics of the system studied. To solve this problem, previous deep-learning-based studies have usually been carried out by increasing the complexity of the model or diversifying the task of the model. Still, limitations remain because the reason of increasing complexity is unclear and the roles of multiple tasks are not well-defined. Therefore, this study proposes a multi-head de-noising autoencoder-based multi-task model for robust diagnosis of REBs under various speed conditions. The proposed model employs a multi-head de-noising autoencoder and multi-task learning strategy to robustly extract features under various speed conditions, while effectively disentangling the speed- and fault-related information. In this research, we evaluate the proposed method using the signals measured from bearing experiments under various speed conditions. The results of the evaluation study show that the proposed method outperformed conventional methods, especially when the training and test datasets have large discrepancies in their operating conditions.

Rolling element bearings are widely used in various industrial machines. Fault diagnosis of rolling element bearings is a necessary tool to prevent any unexpected accidents and improve industrial efficiency. Although proved to be a powerful method in detecting the resonance band excited by faults, the spectral kurtosis (SK) exposes an obvious weakness in the case of impulsive background noise. To well process the bearing fault signal in the presence of impulsive noise, this paper proposes a fault diagnosis method based on the cyclic correntropy (CCE) function and its spectrum. Furthermore, an important parameter of CCE function, namely kernel size, is analyzed to emphasize its critical influence on the fault diagnosis performance. Finally, comparisons with the SK-based Fast Kurtogram are conducted to highlight the superiority of the proposed method. The experimental results show that the proposed method not only largely suppresses the impulsive noise, but also has a robust self-adaptation ability. The application of the proposed method is validated on a simulated signal and real data, including rolling element bearing data of a train axle.

In the fault diagnosis of rolling element bearings (REBs), spall size is a typical indicator of fault severity. Conventionally, spall size estimation relies on expert-knowledge-based or data-driven approaches. Expert-knowledge-based approaches require accurate assumptions about spall-excited events, making them challenging to apply in field environments. In contrast, data-driven approaches often struggle with insufficient training data and limited generalization across various operating conditions. To address this challenge, this paper proposes a frequency-enhanced neural network (FENN) with a hybrid spall-size estimator (HSSE). The proposed FENN employs both one-dimensional convolution in the time domain and Fourier convolution on the frequency magnitude, while preserving phase information in the frequency domain to enhance frequency components that are associated with spall in REBs. The novel HSSE proposed here integrates a data-driven spall-size estimator and an expert-knowledge-guided spall-size estimator to capture spall entry and exit events between rolling elements and raceways. Model validation results, which analyzed both simulation and experimental data from roller and ball bearings, demonstrate that the proposed approach provides accurate predictions of spall size, even with limited training data. Additionally, it is confirmed that the proposed model identifies the mechanical frequencies associated with spall events, providing interpretable results from raw vibration signals without requiring further processing.

Spectrum auto-correlation analysis and its application to fault diagnosis of rolling element bearings

The condition monitoring and fault diagnosis of rolling element bearings play an important role in the safe and reliable operation of rotating machinery. Feature extraction based on vibration signals is an effective means to identify the operating condition of rolling element bearings. Methods based on multi-scale mathematical morphology (MM) have recently been developed to extract features from one-dimensional signals. In this paper, a new double-dot structuring element (SE) is constructed for multi-scale MM. A pattern spectrum, obtained from the multi-scale MM, is used as a feature extraction index. A correlation analysis gives the final identification result by utilizing information over a whole pattern spectrum. Compared with the most commonly used flat SE, the double-dot SE can extract more features of original signals at different scales. Vibration signals, measured from defective bearings with outer race faults, inner race faults and ball faults, are used to evaluate the fault detection ability of the proposed SE and bearing fault diagnosis method. Results show that faults at different levels can be identified, including ball fault; and the location of outer race fault can also be differentiated.

This article develops and compares health indices using different approaches namely singular value decomposition, average value of the cumulative feature and Mahalanobis distance for assessing the rolling element bearing condition. The vibration signals for four conditions of rolling element bearing are acquired from a customized bearing test rig under variable load conditions. Seventeen statistical features are extracted from wavelet coefficients of the denoised signals. Feature selection is performed using singular value decomposition and kernel Fisher discriminant analysis. These selected features are used in these three approaches to develop health indices. Finally, a comparison of the three proposed approaches is made to select the best approach which can be effectively used for fault diagnosis of rolling element bearings.

Fault diagnosis of ball bearings using machine learning methods

Defect diagnosis of rolling element bearings operating under time-varying rotational speeds entails order tracking and analysis techniques that convert a vibration signal from the time domain to the angle domain to eliminate the effect of speed variations. When a signal is resampled at a constant angular increment, the amount of data padded into each data segment will vary, depending on the rate of change in the rotational speeds. This leads to changes in the distance between the adjacent impulse peaks, and consequently, the result of order analysis. This paper presents a quantitative analysis of key factors affecting the accuracy of order analysis on rolling element bearings under variable speeds. An analytical model is established and simulated. The effects of speed variation, instantaneous speed, angular interval between impulses, and the rising time of impulse are specified. It is concluded that the results of order analysis will be smaller as the rotational speed increases, and becomes larger when the speed decreases. Furthermore, the error is larger under low speeds than high speed.

The coupling of multiple impulse response modes is a fundamental characteristic of vibration signals in rolling element bearing compound faults. The adaptive extraction and separation of periodic impulse response features have been recognized as a significant challenge in the compound fault diagnosis of rolling element bearings. To address this issue, an adaptive multiple periodic impulse response mode extraction method is proposed. The purpose of this method is to adaptively extract the periodic impulse response consistent with the fault characteristic frequency through the convolution of a periodic weight vector with the original signal. Periodicity-constrained kurtosis is used as the optimization objective to obtain the period of the weight vector; thus, the periodicity and impulsiveness of the fault feature are simultaneously considered. It is proved theoretically that the periodic impulse response mode extraction process is essentially complete fault harmonic extraction within the full frequency band. Adaptive mode selection is realized based on cross-correlation to extract multiple impulse response modes. The effectiveness of the proposed method is verified through theoretical analysis, as well as simulated and experimental signal analysis, and exhibits obvious superiority in bearing fault feature extraction.

Fault diagnosis of rolling element bearings is a critical aspect of machine maintenance and reliability. Bearings are extensively used in various industrial applications, and their failure can lead to costly downtime and equipment damage. Rotating machinery under continuous overload conditions can indeed significantly degrade bearing life and lead to various other issues. To identify issues in rolling element bearings (REB), several techniques and methods are employed. Diagnosing faults in ball bearings while simultaneously estimating the Remaining Useful Life (RUL) of the bearing is a crucial aspect of predictive maintenance. This can be achieved through a combination of signal processing techniques, machine learning methods, and RUL prediction models. The estimation of a bearing Remaining Useful Life (RUL) is of significant importance in predictive maintenance strategies to avoid unexpected failures, reduce downtime, and optimize maintenance costs. This literature review aims to explore the methodologies, techniques, and advancements in predicting the remaining useful life of bearings.

Rolling element bearings are very important components in electrical machines. Almost 50% of the faults that occur in the electrical machines occur in the bearings. This makes bearings as one of the most critical components in electrical machinery. Bearing fault diagnosis has drawn the attention of many researchers. Generally, vibration signals from the machine’s accelerometer are used for the diagnosis of bearing faults. In literature, application of Deep Learning algorithms on these vibration signals has resulted in the fault detection accuracy that is close to 100%. Although, fault detection using vibration signals from the machine is ideal but measurement of vibration signals requires an additional sensor, which is absent in many machines, especially low voltage machines as it significantly adds to its cost. Alternatively, bearing fault diagnosis with the help of the stator current or Motor Current Signal (MCS) is also gaining popularity. This paper uses MCS for the diagnosis of bearing inner raceway and outer raceway fault. Diagnosis using MCS is difficult as the fault signatures are buried beneath the noise in the current signal. Hence, signal-processing techniques are employed for the extraction of the fault features. The paper uses the Paderborn University damaged bearing dataset, which contains stator current data from healthy, real damaged inner raceway, and real damaged outer raceway bearings with different fault severity. Fault features are extracted from MCS by first filtering out the redundant frequencies from the signal and then extracting eight features from the filtered signal, which include three features from time domain and five features from time–frequency domain by using the Wavelet Packet Decomposition (WPD). After the extraction of these eight features, the well-known Deep Learning algorithm Long Short-Term Memory (LSTM) is used for bearing fault classification. The Deep Learning LSTM algorithm is mostly used in speech recognition due to its time coherence, but in this paper, the ability of LSTM is also demonstrated with the fault classification accuracy of 97%. A comparison of the proposed algorithm is done with the traditional Machine Learning techniques, and it is shown that the proposed methodology outperforms all the traditional algorithms which are used for the classification of bearing faults using MCS. The method developed is independent of the speed and the loading conditions.

Accurate fault diagnosis of machine components is quite important for normal operation of equipment. Nowadays, artificial intelligent methods have been widely researched in fault diagnosis of rolling element bearings (REB). However, due to the variation of machine working conditions, the diagnosis accuracy always degrade seriously. Besides, as it is really hard to achieve large amounts of labeled health condition signals from real equipment, data deficiency is another trouble. Both issues impede the practical application of data-driven fault diagnosis. So as to solve the problems, a data augmentation method SEflow based on squeeze-and-excitation networks (SEnet) and flow-generative model is proposed. Proposed SEflow can learn the data distributions from limited data, then generate augmented signals among different machine working conditions. The experiments applied on bearing datasets and ball screw signals verify the effectiveness of proposed method on solving domain adaption and data deficiency.

Narrowband amplitude demodulation is an effective tool for extracting characteristic features in the fault diagnosis of rolling element bearings. The quality of demodulation largely depends on the frequency band selected for the demodulation. Numerous criteria have been constructed to determine the optimal frequency band. However, independent frequency interferences and in-band noises in narrowband signals can greatly affect the values of the criteria, which may lead to an inaccurate result in locating the optimal band, in demodulating fault features, and finally in the fault detection. Inspired by the nonlocal means (NL-means) denoising method that has been widely used in image processing, this paper proposes a narrowband envelope spectra fusion (NESF) method to enhance fault features and suppress in-band noises before criterion calculation. The method suppresses in-band noises by averaging envelope spectra at neighboring narrow bands. Meanwhile, some minor improvements are made to the conventional narrowband envelope spectrum calculation method to enhance the similarity of the narrowband envelope spectra containing fault features, and finally optimize the fusion process. Then, sparsity values of these denoised envelope spectra, which can lower the impact of independent frequency interferences, are utilized to determine the optimal band and select the optimal envelope spectrum. Frequency signatures of the extracted envelope spectrum can be utilized to indicate the status and fault types of rolling element bearings. A simulated bearing fault signal and three real bearing fault signals are used to validate the effectiveness of the proposed method through comparison studies with protrugram and sparsogram. The results show that the proposed method can effectively extract fault characteristics, even in a harsh environment.