Types Of Bearing Faults Research Articles

Research on Fault Diagnosis of Rolling Bearings Based on Variational Mode Decomposition Improved by the Niche Genetic Algorithm.

Due to the influence of signal-to-noise ratio in the early failure stage of rolling bearings in rotating machinery, it is difficult to effectively extract feature information. Variational Mode Decomposition (VMD) has been widely used to decompose vibration signals which can reflect more fault omens. In order to improve the efficiency and accuracy, a method to optimize VMD by using the Niche Genetic Algorithm (NGA) is proposed in this paper. In this method, the optimal Shannon entropy of modal components in a VMD algorithm is taken as the optimization objective, by using the NGA to constantly update and optimize the combination of influencing parameters composed of α and K so as to minimize the local minimum entropy. According to the obtained optimization results, the optimal input parameters of the VMD algorithm were set. The method mentioned is applied to the fault extraction of a simulated signal and a measured signal of a rolling bearing. The decomposition process of the rolling-bearing fault signal was transferred to the variational frame by the NGA-VMD algorithm, and several eigenmode function components were obtained. The energy feature extracted from the modal component containing the main fault information was used as the input vector of a particle swarm optimized support vector machine (PSO-SVM) and used to identify the fault type of the rolling bearing. The analysis results of the simulation signal and measured signal show that: the NGA-VMD algorithm can decompose the vibration signal of a rolling bearing accurately and has a better robust performance and correct recognition rate than the VMD algorithm. It can highlight the local characteristics of the original sample data and reduce the interference of the parameters selected artificially in the VMD algorithm on the processing results, improving the fault-diagnosis efficiency of rolling bearings.

Bearing Fault Diagnosis of Switched Reluctance Motor in Electric Vehicle Powertrain via Multisensor Data Fusion

A multisensor data fusion method is investigated for bearing fault diagnosis of a switched reluctance motor (SRM) of an electric vehicle (EV) powertrain under varying speed conditions. The accumulative rotation angle of the SRM rotor is estimated by fusing the synchronous sampled current and vibration signals. The time-domain vibration signal is then resampled on an angular domain, and the bearing fault type is identified on the envelope spectrum of the resampled signal. In this article, an experimental setup is designed to validate the performance of the proposed method compared with the traditional ones. The practical EV working conditions including driving, coasting, and braking are considered in the experiments. Results indicated that the proposed method successfully diagnoses the SRM bearing faults under random and complex conditions. The method is promising for online SRM fault diagnosis under varying speed conditions as it requires no extra tachometer, specifically when the sensorless control strategy is adopted.

Intelligent Diagnosis of Rolling Element Bearing Based on Refined Composite Multiscale Reverse Dispersion Entropy and Random Forest.

Rolling bearings are the vital components of large electromechanical equipment, thus it is of great significance to develop intelligent fault diagnoses for them to improve equipment operation reliability. In this paper, a fault diagnosis method based on refined composite multiscale reverse dispersion entropy (RCMRDE) and random forest is developed. Firstly, rolling bearing vibration signals are adaptively decomposed by variational mode decomposition (VMD), and then the RCMRDE values of 25 scales are calculated for original signal and each decomposed component as the initial feature set. Secondly, based on the joint mutual information maximization (JMIM) algorithm, the top 15 sensitive features are selected as a new feature set and feed into random forest model to identify bearing health status. Finally, to verify the effectiveness and superiority of the presented method, actual data acquisition and analysis are performed on the bearing fault diagnosis experimental platform. These results indicate that the presented method can precisely diagnose bearing fault types and damage degree, and the average identification accuracy rate is 97.33%. Compared with the refine composite multiscale dispersion entropy (RCMDE) and multiscale dispersion entropy (MDE), the fault diagnosis accuracy is improved by 2.67% and 8.67%, respectively. Furthermore, compared with the RCMRDE method without VMD decomposition, the fault diagnosis accuracy is improved by 3.67%. Research results prove that a better feature extraction technique is proposed, which can effectively overcome the deficiency of existing entropy and significantly enhance the ability of fault identification.

THE SUPPORT VECTOR MACHINE PARAMETER OPTIMIZATION METHOD BASED ON ADAPTIVE ELITIST DIFFERENTIAL EVOLUTION ALGORITHM AND ITS APPLICATION TO ROLLER BEARING FAULT DIAGNOSIS

SVM parameters have serious effects on the accuracy rate of classification result. Tuning SVM parameters is always a challenge for scientists. In this paper, a SVM parameter optimization method based on Adaptive Elitist Differential Evolution (AeDE-SVM) is proposed. Furthermore, AeDE-SVM is applied to diagnose roller bearing fault by using complementary ensemble empirical mode decomposition (CEEMD) and singular value decomposition (SVD) techniques. First, original acceleration vibration signals are decomposed into Intrinsic Mode Function (IMFs) by using CEEMD method. Second, initial feature matrices are extracted from (IMFs) by singular value decomposition (SVD) techniques to obtain single values. Third, these values serve as input vector for AeDE-SVM classifier. The results show that the combination of AeDE-SVM classifiers and the CEEMD-SVD method obtains higher classification accuracy and lower cost time compared to other methods. In this paper, the roller bearing vibration signals were used to evaluate the proposed method. The experimental results showed that the superior performance compared to other SVM parameter optimization techniques and successfully recognized different fault types of roller bearing during its operation.

Moment matching-based intraclass multisource domain adaptation network for bearing fault diagnosis

Deep learning based fault diagnosis methods assume that training and testing data with sufficient labels are available and share a same distribution. In practical scenarios, this assumption does not generally hold due to variable working conditions of rotating machineries and the difficulty in labeling vibration data under all working conditions. Transfer learning (TL) overcomes this problem by utilizing knowledge learned from the source domain to help accomplish tasks on the target domain. Although TL based fault diagnosis has been considerably studied, most studies mainly focus on single-source TL. Since multisource domains with labeled samples from which more useful knowledge can be extracted are available, in this paper, a novel multisource TL model, called the moment matching-based intraclass multisource domain adaptation network, is proposed. This model uses a feature learner to generate features of each source and target domain data to enable the joint weight classifier to predict target labels. It also introduces a moment matching-based distance metric to reduce the distance among all source domains and the target domain. During the training of the model, an intraclass alignment training strategy is applied to match the marginal and conditional distributions of each domain simultaneously. Experiments on two datasets are performed, wherein the proposed method is used to identify bearing fault types under four load conditions. Experiment results, such as high diagnostic accuracies support the reliability and generalizability of the proposed model.

A new fault diagnosis model of rolling element bearing based on a recurrent neural network

The rolling element bearing is an important part of mechanical equipment, it has various kinds of malfunctions, the location of the fault may occur in the inner ring, outer ring, or rolling element of the bearing. Therefore, traditional methods of classification are difficult to classify and identify effectively. To improve the accuracy of bearing fault diagnosis, the deep learning method is used to diagnose the fault of the rolling element bearing. In this paper, the long short-term memory and gated recurrent unit are combined to build a bearing fault diagnosis model. On the other hand, this paper adjusts the hidden layer structure and optimizes the network parameters to establish a better long short-term memory–gated recurrent unit–long short-term memory diagnostic model and classify the fault types of bearings with Softmax. The model proposed in this paper can effectively diagnose the bearing fault under the bearing data set of Case Western Reserve University and the University of Cincinnati. Compared with the traditional long short-term memory and the gated recurrent unit, the model proposed in this paper has high accuracy in fault diagnosis as well as certain reliability and generalization ability.

An optimized variational mode decomposition method and its application in vibration signal analysis of bearings

The performance of the rolling bearing of a spindle device is directly related to the safety and reliability of the operation of a mine hoist. To extract bearing vibration signal features effectively for fault diagnosis, a feature extraction method based on the parameter optimization of a variational mode decomposition (VMD) method and permutation entropy (PE) is proposed. In addition, a support vector machine (SVM) classifier is used to identify bearing fault types. An analogue signal is used to test the effect of noise and sampling frequency on VMD performance. Focused on the problem of the VMD method needing to determine the number of mode components K and a penalty factor α during the signal decomposition process, a genetic algorithm is used to optimize the parameter combination [K,α] with the minimum sample entropy as the indicator. By using mean squared error (MSE) and correlation coefficient, an evaluation indicator is constructed to determine the decomposition effects of the optimized VMD, centre frequency, empirical mode decomposition (EMD) and ensemble EMD (EEMD) methods. The normalized PE of the five mode components is used as an eigenvalue, which is used as the input parameter of the SVM. Two different experimental datasets are used to verify the effectiveness of the proposed method. The results show that the proposed method has better diagnostic accuracy than EMD, EEMD and a BP neural network in the case of limited samples and unknown sample inputs. It can provide a good reference for the diagnosis of a rolling bearing and has practical application value.

Research on compound fault diagnosis of rolling bearing based on intrinsic time scale decomposition and information fusion

In view of the difficulty to effectively extract compound faults of rolling bearing from aero-engine and precisely identify their types, the paper has proposed a method integrating signal separation algorithm and information fusion. Firstly, the method decomposes the vibration acceleration signals collected by sensors from different positions at the same moment based on intrinsic time scale decomposition algorithm. Secondly, cross correlation analysis is given to the proper rotation component (PRC) of the same layer, which are obtained after decomposition and correspond to the sensors from different positions and cross-correlation function is introduced to embody information fusion. Thirdly, signals are reconstructed according to cross-correlation function of each PRC. Finally, based on the frequency spectrum of reconstructed signal, extract the characteristics of rolling bearing and identify the type of faults under different sensor combinations and multiple compound fault types. The result shows, the proposed method can effectively extract the characteristics of compound faults of bearing and precisely identify the type of faults under different sensor combinations and multiple compound fault types of rolling bearing.

A study on the feature separation and extraction of compound faults of bearings based on casing vibration signals

The autocorrelation function is combined with wavelet transform and cyclostationary theory (WT-AF-CT) in place of threshold denoising, and meanwhile the mean power ratio (MPR) is calculated by the proposed method. Furthermore, extracted characteristic as well as calculated MPR is used to identify compound faults of rolling bearings in aero-engine based on casing vibration acceleration signal-including the ones of common rolling bearing (inner race rotates and outer race is constant) and intershaft bearing (co-rotates with outer and inner race). A comparative analysis was carried out between conventional researches (cyclostationary theory (CT) or wavelet transform combined with threshold value denoising (WT-TD)) and proposed WT-AF-CT method. Additionally, the effect of sensors installation direction for feature separation and extraction of compound faults is considered. The results indicate that the proposed WT-AF-CT method can separate and extract characteristics of compound faults exactly and identify fault types of bearings precisely, no matter sensors are installed horizontally or vertically, while CT or WT cannot.

Fault Extraction and Identification on Transmission Components of Mechanical Equipment

In order to solve the problem of nonlinear, nonstationary, complex components and redundant information of rolling bearing vibration signal in single scale, a rolling bearing fault feature extraction method based on wavelet packet decomposition and permutation entropy and sample entropy is proposed. Firstly, wavelet packet decomposition is used to decompose the original signal of rolling bearing into several subbands with different frequencies, and the permutation entropy and sample entropy of signal data at different frequencies are calculated. Secondly, the sample entropy and permutation entropy of different frequency signals after decomposition and reconstruction are extracted to form a high-dimensional feature vector to complete the initial fault feature extraction. Finally, the extracted feature samples are randomly arranged for fault recognition. The experimental data of rolling bearing processed by this method are identified by extreme learning machine. The results show that the method can effectively identify the fault types of rolling bearing, and the classification effect is better than that of the original data set training, and the classification accuracy reaches 99.8.

Application of Entropy Information on Bearing Fault Diagnosis

In order to solve the problem of nonlinear, nonstationary, complex components and redundant information of rolling bearing vibration signal in single scale, a rolling bearing fault feature extraction method based on wavelet packet decomposition and permutation entropy and sample entropy is proposed. Firstly, wavelet packet decomposition is used to decompose the original signal of rolling bearing into several sub bands with different frequencies, and the permutation entropy and sample entropy of signal data at different frequencies are calculated. Secondly, the sample entropy and permutation entropy of different frequency signals after decomposition and reconstruction are extracted to form a high-dimensional feature vector to complete the initial fault feature extraction. Finally, the extracted feature samples are randomly arranged for fault recognition. The experimental data of rolling bearing processed by this method are identified by extreme learning machine. The results show that the method can effectively identify the fault types of rolling bearing, and the classification effect is better than that of the original data set training, and the classification accuracy reaches 99.8

Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings.

We propose a novel fault-diagnosis approach for rolling bearings by integrating variational mode decomposition (VMD), refined composite multiscale dispersion entropy (RCMDE), and support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Firstly, VMD was selected from various signal decomposition methods to decompose the original signal. Then, the signal features were extracted by RCMDE as the input of the diagnosis model. Compared with multiscale sample entropy (MSE) and multiscale dispersion entropy (MDE), RCMDE proved to be superior. Afterwards, SSA was used to search the optimal parameters of SVM to identify different faults. Finally, the proposed coordinated VMD–RCMDE–SSA–SVM approach was verified and evaluated by the experimental data collected by the wind turbine drivetrain diagnostics simulator (WTDS). The results of the experiments demonstrate that the proposed approach not only identifies bearing fault types quickly and effectively but also achieves better performance than other comparative methods.

Acoustic Emission Analysis for Wind Turbine Blade Bearing Fault Detection Under Time-Varying Low-Speed and Heavy Blade Load Conditions

This article uses acoustic emission (AE) analysis to diagnose an industrial-scale and slow-speed wind turbine blade bearing. The main challenge for AE analysis is that the fault signals are mingled with heavy noise. As a result, the objective of this article is to filter the raw AE signals and extract weak fault signals. To achieve this goal, a general linear and nonlinear auto-regressive (GLNAR) model is first developed to exploit the nonlinear characteristics of the AE signals. Then, the sparse augmented Lagrangian (SAL) algorithm is applied to learn the built GLNAR model and filter the raw AE signals. The characteristics of SAL is that it is a novel sparse representation technique which can convert the original filtering problem into a number of suboptimization problems, and these subproblems can be solved separately. Finally, as the blade bearing rotates at fluctuating speeds, the filtered signals are resampled so that the bearing fault type can be diagnosed in the order domain. The proposed diagnostic framework was validated in several experiments under time-varying low-speed and heavy-blade-load conditions. The results indicate that our proposed methods are effective and accurate.

Improved VMD‐KFCM algorithm for the fault diagnosis of rolling bearing vibration signals

In order to make accurate judgements of rolling bearing main fault types using the small sample size fault data set, a novel approach is put forward that combines particle swarm optimisation kernel fuzzy C-means (PSO-KFCM) and variational mode decomposition (VMD). Firstly, by calculating the centre frequency and Pearson correlation coefficient of each mode function of VMD, the decomposition level K of VMD is determined, and the optimal decomposition result is obtained. The singular value decomposition method was used to extract a characteristic value corresponding to the main fault types of bearings from the optimal decomposition results, and faulty feature sample space was established. Then, the kernel function parameters and the initial clustering centre were used as optimisation variables. The PSO algorithm was used to solve the clustering model. The clustering centre of each fault type under the optimal classification result was obtained, and the fault diagnosis model was established. Finally, different fault classification methods are compared, and the conclusions drawn from the experiment show that the method can achieve good results in bearing fault diagnosis. The accuracy of fault classification was improved obviously.

A new bearing fault diagnosis approach combining sensitive statistical features with improved multiscale permutation entropy method

Obtaining the sensitive feature vectors from the vibration signal is crucial to indicate the bearing’s actual condition. Most often, weak feature vectors are the consequence of heavy noise in the original signal and the incompatibility of the methods to deal with the nonlinear and non-stationary nature of vibration signals. In this paper, the first issue is addressed by employing a complementary ensemble empirical mode decomposition method. A new method based on improved multiscale permutation entropy method and dominant statistical parameters is proposed to deal with the second issue. The proposed approach’s denoising capability is first verified on an amplitude modulated and frequency modulated simulated signal. On the experimental front, the proposed method is investigated under a wide range of operating conditions to simultaneously recognize bearing fault type and severity. Since the experimental investigation includes identifying dominant statistical parameters and classifying different bearing faults, a recent method named XGBoost is explored comprehensively. The results show that the classification accuracy with features extracted by the proposed method exceeds 3% to 18% compared to features extracted by other state-of-the-art permutation-based feature extraction methods.

A Novel Transfer Dictionary Learning Strategy for Rolling Bearing Fault Identification With a Mixed Noise Model

Rolling bearings are an essential core component in the power transmission systems of rotating machinery. Many previous dictionary learning (DL) methods have proven to be powerful for rolling bearing fault diagnosis. Existing diagnostic methods based on DL usually involve analyzing training and test data drawn from the same distribution. However, an unbalanced data distribution is a common phenomenon in practice. In addition, the signals in the source and target domains are often mixed with noise and irrelevant information in complex environments, which greatly reduces the performance of DL. Therefore, a new DL method based on a mixed noise model for transfer DL (TDL-MN) is presented. Specifically, the TDL-MN model is constructed to overcome noise and irrelevant signal interference in the DL process under variable conditions. Moreover, rolling bearing health status is diagnosed through sparse representation (SR) classification by calculating the target domain samples corresponding to the redundancy error in the SR vector. The results for two cases verify the ability of the TDL-MN method to accurately identify bearing fault types under complex and variable working conditions. Comparisons with other methods verify the applicability and superiority of the proposed method.

Conditional GAN and 2-D CNN for Bearing Fault Diagnosis With Small Samples

The rolling bearing is the key component of rotating machinery, and it is also a failure-prone component. The intelligent fault diagnosis method has been widely used to accurately diagnose bearing faults. However, in engineering practice, it is difficult to obtain sufficient sample data to train the intelligent diagnosis model. Therefore, in this paper, a fusion diagnosis model CGAN-2D-CNN that combines a conditional generative adversarial network (CGAN) and a two-dimensional convolutional neural network (2D-CNN) is proposed for bearing fault diagnosis with small samples. Considering the problem of insufficient sample data, CGAN is used to learn the data distribution of real samples to generate new samples with similar data distribution by the confrontation training of the generator and discriminator. Then, two-dimensional pre-processing is conducted to convert the generated one-dimensional data into two-dimensional grey gray images. Finally, these images are input into the 2D-CNN to extract the features and classify the bearing fault types. Two experimental cases are implemented to validate the effectiveness and feasibility of the proposed CGAN-2D-CNN. The experimental results illustrate that the diagnosis accuracy of the proposed method used on the small sample data is close to that of the 2D-CNN directly used on the enough original sample data whose size is equal to the expanded sample size. Additionally, compared with the 1D-CNN, SVM, and LSTM models, the 2D-CNN model after two-dimensional pre-processing has the higher fault classification accuracy.

A New Interpretable Learning Method for Fault Diagnosis of Rolling Bearings

In modern manufacturing processes, requirements for automatic fault diagnosis have been growing increasingly as it plays a vitally important role in the reliability and safety of industrial facilities. Rolling bearing systems represent a critical part in most of the industrial applications. In view of the strong environmental noise in the working environment of rolling bearing, its vibration signals have nonstationary and nonlinear characteristics, and those features are difficult to be extracted. In this article, we proposed a new intelligent fault diagnosis method for rolling bearing with unlabeled data by using the convolutional neural network (CNN) and fuzzy $C$ -means (FCM) clustering algorithm. CNN is first utilized to automatically extract features from rolling bearing vibration signals. Then, the principal component analysis (PCA) technique is used to reduce the dimension of the extracted features, and the first two principal components are selected as the fault feature vectors. Finally, the FCM algorithm is introduced to cluster those rolling bearing data in the derived feature space and identify the different fault types of rolling bearing. The results indicate that the newly proposed fault diagnosis method can achieve higher accuracy than other existing results in the literature.

A Multimodel Decision Fusion Method Based on DCNN-IDST for Fault Diagnosis of Rolling Bearing

Each pattern recognition method has its advantages and disadvantages to diagnose the state of rotating machinery. There are many fault types of rolling bearings with apparent uncertainty. The optimal fusion level is usually challenging to be selected for a specific fault diagnosis task, and extensive human labour and prior knowledge are also highly required during these selections. To solve the above problems, a multimodel decision fusion method based on Deep Convolutional Neural Network and Improved Dempster-Shafer Evidence Theory (DCNN-IDST) is proposed for the inspection of rolling bearing. To solve the defect of the original evidence theory method in the fusion of high-conflict evidence, the fuzzy consistency matrix is introduced. By calculating the factor weight, the reliability and rationality of D-S evidence theory are improved. The DCNN model can learn features from the original data and carry out adaptive feature extraction for multiple sensor information. The features extracted by DCNN adaptively are input into multiple network models for decision fusion. The new method of DCNN-IDST multimodel decision fusion is applied to detect the damage of rolling bearings. To evaluate the effectiveness of the proposed method, both the BP neural network and RBF neural network are used to set up a multigroup comparison test. The result demonstrates that the proposed method can detect the fault of the rolling bearing effectively and achieve the highest diagnosis accuracy among all the tested methods in the experiment.

Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.