Early Bearing Fault Diagnosis Research Articles

An early fault warning and diagnosis model based on a two-step analysis strategy for wind turbine bearings

Early bearing fault diagnosis plays a crucial role in ensuring the reliability and safety of wind turbines. This process encounters two primary challenges, that is, weak fault characteristics and strong background noise. Consequently, they restrict the effectiveness of conventional diagnosis methods. To address these difficulties, a two-step early fault diagnosis model is proposed based on an anomaly monitoring index [Formula: see text] and improved successive variational mode decomposition-fast spectral correlation (ISVMD-FSC). Initially, [Formula: see text] enables early anomaly detection. Subsequently, the fault type is further identified using ISVMD-FSC. The ISVMD adaptive decomposition of early warning signals can effectively reduce the noise and isolate the fault impact characteristics. Then, the fault characteristic frequency is extracted using spectral coherence analysis to achieve the purpose of fault diagnosis. This model is tested and validated on simulated data, laboratory accelerated bearing life span data, and real high-speed bearing fault data of wind turbines. Notably, the comparative study shows that [Formula: see text] is capable of detecting the fault earlier than the spectral L2/L1 norm, the sum of the weighted normalized square envelope, index 8, and index 9. Additionally, the fault feature extraction effect of ISVMD-FSC is better than many more advanced time-frequency analysis methods. These results emphasize the model’s excellent performance in early bearing fault diagnosis and its good generalization ability.

Early Fault Diagnosis of Bearings Based on Symplectic Geometry Mode Decomposition Guided by Optimal Weight Spectrum Index

When the rotating machinery fails, the signal generated by the faulty component often no longer maintains the original symmetry, which makes the vibration signal with nonlinear and non-stationary characteristics, and is easily affected by background noise and other equipment excitation sources. In the early stage of fault occurrence, the fault signal is weak and difficult to extract. Traditional fault diagnosis methods are not able to easily diagnose fault information. To address this issue, this paper proposes an early fault diagnosis method for symplectic geometry mode decomposition (SGMD) based on the optimal weight spectrum index (OWSI). Firstly, using normal and fault signals, the optimal weight spectrum is derived through convex optimization. Secondly, SGMD is used to decompose the fault signal, obtaining a series of symplectic geometric modal components (SGCs) and calculating the optimal weight index of each component signal. Finally, using the principle of maximizing the OWSI, sensitive components reflecting fault characteristics are selected, and the signal is reconstructed based on this index. Then, envelope analysis is performed on the sensitive components to extract early fault characteristics of rolling bearings. OWSI can effectively distinguish the interference components in fault signals using normal signals, while SGMD has the characteristic of unchanged phase space structure, which can effectively ensure the integrity of internal features in data. Using actual fault data of rolling bearings for verification, the results show that the proposed method can effectively extract sensitive components that reflect fault characteristics. Compared with existing methods such as Variational Mode Decomposition (VMD), Feature Mode Decomposition (FMD), and Spectral Kurtosis (SK), this method has better performance.

Ensefgram: An optimal demodulation band selection method for the early fault diagnosis of high-speed train bearings

Early fault diagnosis of high-speed train bearings under road spectrum shocks is challenging. As a conventional narrowband demodulation method, the Fast Kurtogram (FK) class of algorithms have some drawbacks: the fixed-band segmentation strategy makes it difficult for the method to find the optimal demodulation frequency band (ODFB); other major limitations are the poor robustness of the statistical index of the blind algorithm and the poor adaptiveness of the target algorithm given the need to the know the bearing operating state in advance. To address these issues, this paper proposes Ensefgram, an early fault detection method for high-speed train bearings with the acoustic emission signal as an example. First, a filter bank is designed by combining the spectral trend with the median dichotomy strategy, which adaptively segments the frequency band and retains the integrity of the sub-band fault components to the maximum extent. In addition, a novel statistical index, termed the maximum weighted spectral energy frequency factor, is proposed, and it is combined with the envelope spectrum negentropy (ES-negentropy) to form a composite index ENSEF, which can select the ODFB blindly. Finally, the proposed method was validated by simulation and applying test signals close to the real working conditions of a high-speed train. Compared with FK, Infogram, and other methods, Ensefgram provides more accurate detection results for the early fault diagnosis of bearings.

Bearing fault feature extraction based on MOMEDA and CS-Wood–Saxon stochastic resonance

Since rolling bearing is of great significance to ensure the safe and stable operation of rotating machinery, bearing fault feature extraction then demonstrates a hot topic of general interest in industry. In this work, we applied Multipoint Optimal Minimum Entroy Deconvolution Adjusted preprocessing algorithm to deal with the large amount of background noise containing in the collected bearing fault original signal. Then, the Wood–Saxon stochastic resonance nonlinear system model is adopted to solve the bearing fault feature extraction problem, which avoids the frequency interval and system parameters disadvantages in bistable stochastic resonance system. Furthermore, the parameter step and scale transform factor in the Wood–Saxon stochastic resonance nonlinear system is optimized adaptively by Cuckoo Search algorithm, in which way the output signal-to-noise of bearing fault signal is improved significantly. Therefore, the bearing fault feature can be extracted more effectively compared with the classical bistable stochastic resonance system model. Simulation and examples demonstrated that the proposed method can effectively reduce the noise in the signal and enhance the weak feature in bearing fault signal, so as to realize the accurate early bearing fault diagnosis.

A Bearing Fault Diagnosis Method Based on Dilated Convolution and Multi-Head Self-Attention Mechanism

Rolling bearings serve as the fundamental components of rotating machinery. Failure to detect damage early in these components can result in equipment shutdown, leading not only to economic losses but also to a threat to worker safety. Given the diverse range of rotating parts, it is crucial to promptly identify and accurately diagnose early bearing failures during the maintenance of large-scale machinery. To achieve quick and precise fault diagnosis, this study proposes a method based on dilated convolution, a Bidirectional Gated Recurrent Unit (BiGRU), and a multi-head self-attention mechanism. The key advantage lies in its ability to directly process raw 1D sampled data without requiring complex time–frequency domain conversion. To validate the model’s accuracy and stability, we conducted empirical studies using both the HUST bearing dataset proposed by Thuan, Nguyen et al. and the CWRU bearing dataset from Western Reserve University. The results demonstrate that our model achieves an impressive accuracy rate of 99.94%, along with an f1 value for the test set when dealing with multiple operating conditions for all five types of bearings in the HUST dataset. Moreover, when applied to the CWRU dataset, these two metrics even reached 99.95%. Furthermore, the proposed model achieves a significant prediction accuracy of more than 98.5% on two datasets containing different types of noise and different levels of white Gaussian noise, highlighting its great potential in practical applications of early bearing fault diagnosis.

Feature extraction of multi-sensors for early bearing fault diagnosis using deep learning based on minimum unscented kalman filter

Bearing fault diagnosis is vital for ensuring reliability and safety of high-speed trains and wind turbines. Therefore, a minimum unscented Kalman filter-aided deep belief network is proposed to extract invariant features from vibration signals collected by multiple sensors. This particularly crucial due to the dynamic nature of environmental noise and internal bearing degradation, which pose challenges to accurate diagnosis. Firstly, the Gramian angular summation field is employed to transform the multi-sensor signals into 2-D feature maps. This transformation retains the absolute temporal relation within the time-series signals, mitigating feature distortion and enhancing noise elimination for early detection. Secondly, a deep belief network is utilized to construct a robust deep learning framework capable of analysing the translated 2-D feature maps for effective diagnosis. In addition, a minimum unscented transform technique and an adaptive scaling process for noise are integrated into the diagnostic model. These components exhibit exceptional dynamic tracking capabilities, allowing for adjustment of key parameters in response to the prolonged and evolving bearing degradation process. The proposed methodology was rigorously evaluated through a comprehensive analysis involving nine distinct methods, utilizing two diverse bearing datasets. The results obtained underscore the superior attributes. Notably, the proposed diagnostic scheme achieved accuracy rates exceeding 98% and 99% for the two datasets, respectively. This achievement underscores the establishment of an intelligent diagnosis model characterized by high precision and exceptional generalisation capabilities for bearings within rotating machinery. Consequently, this work lays a robust foundation for future research endeavours, particularly in the realm of transfer learning.

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.

A hybrid method of frequency-weighted energy operator and power spectrum fusion to detect bearing faults

The difficulties in early fault diagnosis of bearings mainly include two aspects: first, the initial damage size of the bearing is small, and the abnormal vibration caused by slight damage to the bearing is very weak. Second, vibration signals collected in actual industrial environments always contain strong noise interference. Therefore, traditional diagnostic procedures are not satisfactory. To address these challenges, this work provides a hybrid model combining frequency-weighted energy operator (FWEO) with power spectrum fusion (PSF) to identify weak fault features of bearings and detect different fault types. Different from traditional time-domain signal filtering, PSF is first used to reduce the interference of noise components in the power spectrum, which will not weaken the fault signal components during denoising. Second, the filtered signal is transformed into the time domain and FWEO is employed to further enhance the cyclic fault signal caused by the weak defect of the bearing. Finally, the existence of a fault is identified by observing the squared envelope spectrum of the signal. The effectiveness of the proposed hybrid model is demonstrated through two simulated fault signals and three different experimental fault signals. The results show that the proposed model has high anti-noise performance and robustness and can extract the fault frequency well.

RMA-CNN: A Residual Mixed-Domain Attention CNN for Bearings Fault Diagnosis and its Time-Frequency Domain Interpretability

Early fault diagnosis of bearings is crucial for ensuring safe and reliable operations. Convolutional Neural Networks (CNNs) have achieved significant breakthroughs in machinery fault diagnosis. However, complex and varying working conditions can lead to inter-class similarity and intra-class variability in datasets, making it more challenging for CNNs to learn discriminative features. Furthermore, CNNs are often considered "black boxes" and lack sufficient interpretability in the fault diagnosis field. To address these issues, this paper introduces a Residual Mixed-Domain AttentionCNN method, referred to as RMA-CNN. This method comprises multiple ResidualMixed Domain Attention Modules (RMAMs), each employing one attention mechanism to emphasize meaningful features in both time and channel domains. This significantly enhances the network's ability to learn fault-related features. Moreover, we conduct an in-depth analysis of the inherent feature learning mechanism of the attention module RMAM to improve the interpretability of CNNs in fault diagnosis applications. Experiments conducted on two datasets—a high-speed aeronautical bearing dataset and a motor bearing dataset—demonstrate that the RMA-CNN achieves remarkable results in diagnostic tasks.

Fault Feature Enhanced Extraction and Fault Diagnosis Method of Vibrating Screen Bearings

For mechanical equipment, bearings have a high incidence area of faults. A problem for bearings is that their fault characteristics include a vibrating screen exciter which is weak and thus easily covered in strong background noise, hence making the noise difficult to remove. In this paper, a noise reduction method based on singular value decomposition, improved by singular value’s unilateral ascent method (SSVD), and a fault feature enhancement method, i.e., variational mode decomposition, improved by revised whale algorithm optimization (RWOA-VMD), are proposed. These two methods are used in vibration signal processing with early faults of bearings having a vibrating screen and they have achieved significant application results. This paper also aims to construct a multi-modal feature matrix composed of energy entropy, singular value entropy, and power spectrum entropy, and then the early fault diagnosis of bearings of a vibrating screen exciter bearing is realized by using the proposed support vector machine, improved by the aquila optimizer algorithm (AO-SVM).

Application of Teager-Kaiser Energy Operator in the Early Fault Diagnosis of Rolling Bearings.

Rolling bearings are key components that support the rotation of motor shafts, operating with a quite high failure rate among all the motor components. Early bearing fault diagnosis has great significance to the operation security of motors. The main contribution of this paper is to illustrate Gaussian white noise in bearing vibration signals seriously masks the weak fault characteristics in the diagnosis based on the Teager–Kaiser energy operator envelope, and to propose improved TKEO taking both accuracy and calculation speed into account. Improved TKEO can attenuate noise in consideration of computational efficiency while preserving information about the possible fault. The proposed method can be characterized as follows: a series of band-pass filters were set up to extract several component signals from the original vibration signals; then a denoised target signal including fault information was reconstructed by weighted summation of these component signals; finally, the Fourier spectrum of TKEO energy of the resulting target signal was used for bearing fault diagnosis. The improved TKEO was applied to a vibration signal dataset of run-to-failure rolling bearings and compared with two advanced diagnosis methods. The experimental results verify the effectiveness and superiority of the proposed method in early bearing fault detection.

Early Fault Diagnosis Technology for Bearings Based on Quantile Multiscale Permutation Entropy

Early fault diagnosis of bearings is the basis of condition-based maintenance. To overcome the difficulty of early fault diagnosis for the mechanical system, a new conception named quantile multiscale permutation entropy (QMPE) is defined, and a new feature extraction method based on QMPE is proposed. On the basis of the multiscale entropy, the multiscale permutation entropy for the gathered vibration signal of equipment is obtained, and the sample quantile is calculated, which is employed to analyze the weak change of the variation signal. The proposed method is verified with the full lifetime datasets of a certain bearing, which proves that signal features extracted by the QMPE method can not only truly express the bearing detailed condition changing from normal to fault but also duly detect the early fault of the bearing. Comparing with other methods for early fault diagnosis, the proposed method can advance the finding time of the early fault obviously.

Research on standard three-well stochastic resonance system and its application in early bearing fault diagnosis

Research on standard three-well stochastic resonance system and its application in early bearing fault diagnosis

Modulated Gabor filter based deep convolutional network for electrical motor bearing fault classification and diagnosis

The high applicability of the electrical motor has led to gain attention in condition monitoring to diagnosis the most common type of fault in this machine, bearing element. The emergence of deep neural networks (DNN) provides the opportunity to design a network for early bearing fault diagnosis with high speed and without any additional feature extraction technique. However, robustness against the noise and some deficiencies in fully capturing features are still challenging issues. To resolve this problem, this paper proposes a one module Gabor filter based convolutional neural networks (CNN), namely Gabor convolutional neural network (GCNN), for bearing fault detection and classification. GCNN is a modulated Gabor filter to enhance the ability in capturing temporal features as well as enhance understanding spatial features with fewer parameters and higher robustness against noises and can be considered as a computational efficient deep structure. The simulation results of the bearing fault detection/classification are studied on two different experimental prototypes, including case Western Reserve University (CWRU) and Paderborn University (Paderborn) datasets. The superiority of this method is shown by comparison with accelerated CNN (ACNN), adaptive CNN, standard CNN, support vector machine (SVM), learning vector quantisation (LVQ), and feedforward neural network (FFNN).

Early Bearing Fault Diagnosis of Rotating Machinery by 1D Self-Organized Operational Neural Networks

Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs. Recently many studies investigated machine learning monitoring methods especially based on Deep Learning networks focusing mostly on detecting bearing faults; however, none of them addressed bearing fault severity classification for early fault diagnosis with high enough accuracy. 1D Convolutional Neural Networks (CNNs) have indeed achieved good performance for detecting RM bearing faults from raw vibration and current signals but did not classify fault severity. Furthermore, recent studies have demonstrated the limitation in terms of learning capability of conventional CNNs attributed to the basic underlying linear neuron model. Recently, Operational Neural Networks (ONNs) were proposed to enhance the learning capability of CNN by introducing non-linear neuron models and further heterogeneity in the network configuration. In this study, we propose 1D Self-organized ONNs (Self-ONNs) with generative neurons for bearing fault severity classification and providing continuous condition monitoring. Experimental results over the benchmark NSF/IMS bearing vibration dataset using both x- and y-axis vibration signals for inner race and rolling element faults demonstrate that the proposed 1D Self-ONNs achieve significant performance gap against the state-of-the-art (1D CNNs) with similar computational complexity.

Early diagnosis of bearing faults using decomposition and reconstruction stochastic resonance system

Weak vibration signals generated by faults in rolling element bearings are often masked by high levels of background noise, which leads to inaccurate fault detection. To overcome the masking, a stochastic resonance based on decomposition and reconstruction method is employed for early bearing fault diagnosis. First, theoretical analysis is performed to confirm the requirement for signal preprocessing. Subsequently, a novel cross correlation kurtosis is defined as an index to obtain an optimal intrinsic mode function after empirical mode decomposition. Finally, the preprocessed signal is clarified via a stochastic resonance method and reconstructed for bearing fault diagnosis. Combined with ensemble bagged trees-based machine learning, the decomposition and reconstruction stochastic resonance method was beneficial for detecting fault signals from incoming noisy vibration signals using simulated and actual vibration signals. According to the results, the proposed method was superior to traditional stochastic resonance method and variational mode decomposition with the k-number method.

An improved higher-order analytical energy operator with adaptive local iterative filtering for early fault diagnosis of bearings

Early fault diagnosis in rolling bearings is crucial to maintenance and safety in industry. To highlight the weak fault features from complex signals combined with multiple interferences and heavy background noise, a novel approach for bearing fault diagnosis based on higher-order analytic energy operator (HO-AEO) and adaptive local iterative filtering (ALIF) is put forward. HO-AEO has better effect in dealing with heavy noise. However, it is subjected to the limitation of mono-components. To solve this limitation, ALIF is adopted firstly to decompose the nonlinear, non-stationary signals into multiple mono-components adaptively. In the next, the resonance frequency band as the optimal intrinsic mode function (IMF) is selected according to the maximum kurtosis. In the following, HO-AEO is utilized to highlight weak fault characteristics of the selected IMF. Finally, the early bearing fault is diagnosed by the energy operator spectrum based on fast Fourier transform (FFT). Comparisons in the simulation indicate that the fourth order HO-AEO shows the best performance in fault diagnosis compared with Teager energy operator (TEO), analytic energy operator (AEO), the second and the third order HO-AEO. The simulated test and experimental results demonstrate that the proposed approach could effectively extract weak fault characteristics from contaminated vibration signals.

Retraction: Early bearing fault diagnosis based on improved SFLA and ELM network

In this paper, an extreme learning machine (ELM) network based on an improved shuffled frog leaping algorithm (CCSFLA) is applied in early bearing fault diagnosis. ELM is a new type of single layer...

Fault diagnosis of bearing based on Symbolic Aggregate approXimation and Lempel-Ziv

Aiming at the problem that the traditional Lempel-Ziv encoding method can’t accurately reflect the bearing modulation information in bearing fault diagnosis, an improved Lempel-Ziv method based on Symbolic Aggregate approXimation is proposed to improve the coding accuracy and the computational efficiency of Lempel-Ziv indicator. According to the Piecewise Aggregate Approximation, the normalized bearing signal is divided into w equal-sized segments. The equal-sized segments are symbolized by determined breakpoints. The complexity value of the symbol sequence is calculated by Lempel-Ziv indicator. The proposed method is verified by a single-point defect dataset and a run-to-failure dataset of bearing, respectively. The results show that the proposed method can effectively reflect the modulation information of bearings. While the proposed method has higher computational efficiency than the traditional Lempel-Ziv method and can realize the early fault diagnosis of bearings.

Corrigendum: Early bearing fault diagnosis based on improved SFLA and ELM network

Corrigendum: Early bearing fault diagnosis based on improved SFLA and ELM network