Derivative and enhanced discrete analytic wavelet algorithm for rolling bearing fault diagnosis

A key problem of a rolling bearing's fault diagnosis is the feature extraction of the fault signal. Wavelet analysis can accomplish local analysis in the time-domain and frequency-domain, which is widely used in many different fields, such as signal processing, image processing and fault diagnosis. In this paper, we analyze the fault signal of a rolling bearing under different rotation rates, and analyze the vibration signal with damage in the inner ring, with damage in the outer ring and with damage in the ball. Basing on the wavelet analysis, signals are separated and reconstructed and a new method to figure out the characteristic parameter of the AE signal of rolling bearings is studied.

The state monitoring and fault diagnosis of rolling bearings are of great significance. The fault signal of rolling bearing contains periodic impact components, and the impact frequency reflects the location information of bearing fault. In order to effectively diagnose the early and weak fault of rolling bearing, the method of morphological filtering was used to pretreat the fault signal of rolling bearing in this paper. Then, the independent component analysis (ICA) was applied to separate the signal after morphological filtering. The results of experimental study on the compound fault signals of rolling bearing inner and outer ring show that this method can effectively identify and separate the fault characteristics of rolling bearing.

Fault diagnosis of rolling bearings is still a very important and difficult research task on engineering. After analyzing the shortcomings of current bearing fault diagnosis technologies, a new approach based on Empirical Mode Decomposition (EMD) and blind equalization eigenvector algorithm (EVA) for rolling bearings fault diagnosis is proposed. In this approach, the characteristic high-frequency signal with amplitude and channel modulation of a rolling bearing with local damage is first separated from the mechanical vibration signal as an Intrinsic Mode Function (IMF) by using EMD, then the source impact vibration signal yielded by local damage is extracted by means of a EVA model and algorithm. Finally, the presented approach is used to analyze an impacting experiment and two real signals collected from rolling bearings with outer race damage or inner race damage. The results show that the EMD and EVA based approach can effectively detect rolling bearing fault.

With the automation and complexity of industry, the production planning and scheduling of the smart factory depends on the proper functioning of each part. As a key component of rotating machinery, the faults of rolling bearing is prone to effects under complex and changing working conditions and coupled with other components. Therefore, it is necessary to carry out efficient and accurate fault diagnosis for rolling bearings to ensure the reliability and stability of mechanical equipment operation. This paper introduces a multi-scale rolling bearing fault diagnosis method (MSF-CNN) based on dilated convolution which is able to automatically capture complementary data and feature information of different scales of the original vibration signal through multi-scale convolution to achieve end-to-end fault diagnosis. The experimental results show that the proposed fault diagnosis method achieves an average accuracy of 99.35% under three load conditions. In addition, this method can provide the cross domain data solution to reduce the dependence on missing label data, which achieves the fault diagnosis accuracy of 89+%. It verifies the validity of our proposal.

The rolling bearing is a critical part of rotating machinery and its condition determines the performance of industrial equipment; it is necessary to detect rolling bearing faults as early as possible. The traditional methods of fault diagnosis are not efficient and are time-consuming. With the help of deep learning, the convolution neural network (CNN) plays a huge role in the data-driven methods of bearing fault diagnosis. However, the vibration signal is non-stationary, contains high noise, and is one-dimensional, which is difficult to analyze directly by the CNN model. Considering the multi-domain learning as an advantage of deep learning, this paper proposes a novel rolling bearing fault diagnosis approach using an improved one-dimensional (1D) and two-dimensional (2D) convolution neural network (CNN) of two-domain information learning. The constructed fault diagnosis model combining 1D and 2D CNN extracts the fault features from the two-domain information of bearing fault samples. The padding and dropout technology are utilized to fully extract features from the raw data and reduce over-fitting. To prove the validity of the proposed method, this paper performs two tests with two bearing datasets, the Case Western Reserve University (CWRU) bearing dataset and the Dalian University of Technology (DUT) vibration laboratory dataset. The experimental results show that our proposed method achieves high recognition accuracy of rolling bearing fault states via two-domain learning of monitoring data, and there is no manual experience necessary. Vibration data under strong noise were also used to test the method, and the results show the superiority and robustness of the proposed method.

In view of the fact that the random noise interferes with the characteristic extraction of a rolling bearing fault signal, a new method of fault feature extraction is proposed based on the combination of the generalized S transform and singular value decomposition (SVD). Firstly, the 2D time–frequency spectrum bearing fault signal is obtained by applying the generalized S transform, and the time–frequency spectrum matrix is used as the objective matrix of SVD to solve the singular values. Then the K-means clustering algorithm is used to classify the singular value sequence, and the singular values for reconstruction are determined. Finally, the de-noised matrix is carried out the generalized S inversion transform to get the de-noised fault signal, and the power spectrum is calculated to finish the fault diagnosis. By analyzing the simulated signal and the actual bearing fault data, results show that the proposed method can effectively identify typical faults of rolling bearings and improve the diagnosis effect of rolling bearing faults. And it provides a new way to realize the fault diagnosis of rolling bearings under noise.

For the hassle that the fault of the rolling bearing is challenging to identify, a method for identifying the damage degree of rolling bearing based on the combination of Variational Mode Decomposition (VMD) and Gath-Geva (GG) fuzzy clustering is proposed. The method decomposes the known rolling bearing fault signal by VMD, determines the number of decomposed modes based on the magnitude of the component frequency center, and forms the obtained eigenmode components into an initial eigenmatrix for singular value decomposition. The three largest singular values are selected as the input of the GG clustering algorithm, and the membership matrix and cluster center of the known fault signals are obtained. The damage degree of the rolling bearing is identified by the Hamming closeness between the initial membership matrix of the signal to be measured and the cluster centers with clear fault signal. Rolling bearings vibration data verify the effectiveness of the method. The experimental results show that the method in this paper can accurately extract the fault information contained in the vibration signal, realize the correct fault type identification, and provide a reliable basis for the rolling bearing fault diagnosis method.

In the fault diagnosis of rolling bearings, compared to using vibration signals as the diagnostic target, utilising motor stator current signals can effectively reduce diagnostic costs and improve diagnostic efficiency. However, current signals often contain abundant noise that tends to obscure the characteristic signals related to bearing faults. Furthermore, the manifestation in signals of fault features for complex faults in rolling bearings is more intricate than that for single-point faults. Therefore, this paper proposes a signal processing method that can effectively suppress noise in current signals and further enhance the weak bearing composite fault features. The main content of this study leverages the inherent characteristics of motor stator current signals to eliminate a substantial amount of noise interference unrelated to fault characteristic signals, enabling the expression of weak fault characteristic signals to be further emphasised, thus achieving an effective representation of bearing composite fault features in current signals. Finally, the processed fault signals are used for fault classification through convolutional neural networks (CNNs). Experimental results effectively demonstrate that this method also has certain advantages and reliability in detecting and extracting weak compound fault features of bearings.

To realize accurate fault diagnosis of rolling bearing under random noise, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and optimized Elman_AdaBoost is proposed in this paper. First, the EEMD method is used to decompose the original vibration signal into several intrinsic mode functions (IMFs). Then, the correlation coefficient and kurtosis of IMFs are used to remove any excess ingredients, and the signal is reconstructed by using the rest of IMFs. In addition, based on the analysis of the time–frequency characteristics of the reconstructed signal, the root-mean-square value and the power spectrum center are extracted as a 2-D feature vector from each reconstructed signal and regarded as the input characteristic vector of the Elman neural network. Combined with the AdaBoost classification algorithm, the Elman_AdaBoost algorithm is improved and proposed to establish the strong classifier. Finally, based on the vibration data recorded in the bearing center of the Case Western Reserve University, three kinds of bearings with different faults and normal bearings are selected as the sample data and the training samples and test samples are constructed. Experimental results show that the proposed EEMD method and optimized Elman_AdaBoost model can effectively diagnose the rolling bearing under random noise, and has the advantages of fast speed, small error, and stable performance. Comparing with the traditional Elman_AdaBoost and single Elman neural network algorithm, this paper with fewer characteristics can get better accuracy and real-time processing performance, and presents a simple and practical resolution for the fault diagnosis of rolling bearings.

Rolling bearings are key components in most mechanical facilities; hence, the diagnosis of their faults is very important in predictive maintenance. Up to date, vibration analysis has been widely used for fault diagnosis in practice. However, acoustic analysis is still a novel approach. In this study, acoustic analysis with classification is used for fault diagnosis of rolling bearings. First, Hilbert transform (HT) and power spectral density (PSD) are used to extract features from the original sound signal. Then, decision tree algorithm C5.0, support vector machines (SVMs) and the ensemble method boosting are used to build models to classify the instances for three different classification tasks. Performances of the classifiers are compared w.r.t. accuracy and receiver operating characteristic (ROC) curves. Although C5.0 and SVM show comparable performances, C5.0 with boosting classifier indicates the highest performance and perfectly discriminates normal instances from the faulty ones in each task. The defect sizes to create faults used in this study are notably small compared to previous studies. Moreover, fault diagnosis is done for rolling bearings operating at different loading conditions and speeds. Furthermore, one of the classification tasks incorporates diagnosis of five states including four different faults. Thus, these models, due to their high performance in classifying multiple defect scenarios having different loading conditions and speeds, can be readily implemented and applied to real-life situations to detect and classify even incipient faults of rolling bearings of any rotating machinery.

This paper presents a fault diagnosis method for rolling bearings working in non-stationary running conditions. The proposed approach is based on an improved version of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the multivariate denoising using wavelet analysis and principal component analysis (PCA), the spectral kurtosis, and the order tracking analysis (OTA). The results show that the improved CEEMDAN has completely decomposed the raw signal into different intrinsic mode functions (IMFs) representing the natural oscillatory modes embedded into the signal. The most relevant IMF from which the defect was extracted is selected by the kurtogram plot which allows locating the optimal frequency band having the highest kurtosis value. Multivariate denoising based on wavelet analysis and PCA is used to increase the signal-to-noise ratio (SNR) of the selected IMF. The results show the great contribution of the denoising approach when comparing the selected denoised IMF with the original one. Finally, order tracking analysis is applied on the denoised IMF’s envelope to remove the effect of speed variation, and an envelope order spectrum is obtained. The proposed approach is first applied on theoretical signal simulating rolling bearing defect in variable regime including three different phases. The final order spectrum shows exactly the simulated defect order and several of its harmonics. For the experimental validation, several signals of defective rolling bearings have been measured on the Machine Fault Simulator test rig in variable regime. Despite the combined variable regime including acceleration-constant regime-deceleration, at the same time, the obtained results indicate the efficiency of the proposed method to extract the fault order with high accuracy. The maximum error between the theoretical order and the experimentally obtained one was 1.3% for outer race defect and 1% for inner race defect. Finally, the performances of the proposed method are compared to those of another diagnosis method designed for variable regime conditions. Both outer race and inner race defects are considered in acceleration regime. The results show the superiority of the proposed method to highlight the defect order with highest clarity.

Rolling bearing is commonly used in rotating machinery and the rolling bearing fault diagnosis is of great significance to enhance the reliability of the rotating machinery. In this paper., an intelligent fault diagnosis method using deep belief network (DBN) via deep-Iayerwise feature extraction is proposed for rolling bearing fault identification. In this method, discrete wavelet packet transform is first used to calculate the original features from raw vibration signals. Due to information redundancy of the original features, the paper constructs a deep belief network with three hidden layers for deep-layerwise feature extraction and dimensionality reduction. Furthermore., the effectiveness of the proposed method is verified by two rolling bearing datasets and comparisons with the traditional intelligent fault diagnosis methods are also carried out. The result confirms that the proposed method is capable to detect the faults in rolling bearing and performs much better than the traditional intelligent fault diagnosis method.

Vibration monitoring has become a relatively common method for the fault diagnosis of rolling bearings in recent years. Vibration-based fault diagnosis technologies essentially rely upon accurate fault signature extraction from noisy vibration signals. To this end, this paper presents a sparsity-assisted denoising method using the fractional spline wavelet transform (FrSWT) and overlapping group shrinkage (OGS) with non-convex regularization and convex optimization (OGSNCRCO) to diagnose bearing faults. The FrSWT is used to promote sparsity of the wavelet coefficients for bearing fault signals. The OGSNCRCO balances data consistency and sparsity by using non-convex regularization and convex optimization, and is applied to shrink the wavelet coefficients of noisy signals in the fractional spline wavelet domain to extract bearing fault signals. Simulations and experiments validate the efficiency of the proposed method for fault diagnosis of rolling bearings. The results of analysis suggest that the proposed method is superior to other state-of-art techniques also commonly used for extracting fault signatures of rolling bearings, including the L1-norm denoising method (FrSWT with L1-norm regularization) and spectral kurtosis method.

Rolling bearings are vital components in rotary machines where even minor faults can lead to the collapse of the entire mechanical system, resulting in significant economic losses. The condition monitoring and fault diagnosis of rolling bearings are crucial for safe operation of complex industrial systems. Traditional fault diagnosis methods mainly depend on expert experience, which leads to obvious deficiencies in diagnostic efficiency, especially in terms of accuracy. To address the issues of traditional bearing fault diagnosis relying on expert experience and low efficiency and accuracy of fault diagnosis, a bearing fault diagnosis model combining synchrosqueezed wavelet transform (SWT) and SE-ResNet is proposed. Firstly, the one-dimensional non-stationary bearing vibration signal is converted into a high-frequency two-dimensional time-frequency image through SWT, which serves as the input for the convolutional neural network; Secondly, an attention mechanism is introduced to construct an SE-ResNet model for extracting two-dimensional image features, which enhances the attention of important features and thus enhances the network's representation ability; Finally, the results of fault diagnosis are output through Softmax layer. To validate the capacity of the proposed model, experiments with the bearing dataset collected from Case Western Reserve University (CWRU) were conducted. The results show that the transfer learning accuracy of the CWRU dataset is 86.25% to 99%. This method has good feasibility, good convergence performance, and has high practical application value.

The rolling bearing fault signals are accompanied with the specific periodic impulse components in case of fault. Due to the background noise, the periodic impulse component is extremely weak and difficult to be extracted. The periodic segment matrix (PSM) has excellent singular value aggregation property and can accurately separate the periodic impulse components from the signal. Based on this, a symplectic period mode decomposition (SPMD) method is proposed. Firstly, the singular value squared difference ratio (SVSDR) spectrum is defined, and the embedded dimension of PSM is determined. Then, the symplectic geometry component matrix is obtained by symplectic geometry similarity transformation, and the first symplectic geometry period component (SPC) is obtained by periodic impact intensity (PII). Finally, the spectral L2/L1 norm (SNL2/L1) index is used as the termination condition, and the iterative decomposition is terminated when the SNL2/L1 of the residual signal decreases. The emulation and experimental signals results indicate that the SPMD method can extract periodic impulse components accurately and is an effective rolling bearing fault diagnosis method.