Early fault diagnostic system for rolling bearing faults in wind turbines

2021 ◽  
pp. 1-37
Author(s):  
Libowen Xu ◽  
Qing Wang ◽  
Ioannis Ivrissimtzis ◽  
Shisong Li
Keyword(s):  
Wind Turbines ◽  
Early Stage ◽  
Reference Signal ◽  
Diagnostic System ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Bearing Failures ◽  
Fault Diagnostic

Abstract The operation and maintenance costs of windfarms are always high due to high labour costs and the high replacement cost of parts. Thus, it is of great importance to have real-time monitoring and an early fault diagnostic system to prevent major events, reduce time-based maintenance and minimize the cost. In this paper, such a 2-step system for early-stage rolling bearing failures in off-shore wind turbines is introduced. Firstly, Empirical Mode Decomposition (EMD) is applied to minimize the effect of ambient noise. Next, correlation coefficients between a reference signal and test signals are obtained and incipient fault detection is achieved by comparing the results with a threshold value. Through further analysis of the envelope spectrum, Sample Entropy for selected Intrinsic Mode Functions is obtained, which is further used to train a Support Vector Machine classifier to achieve fault classification and degradation state recognition. The proposed diagnostic approach is verified by experimental tests, and an accuracy of 98% in identifying and classifying rolling bearing failures under various loading conditions is obtained.

scholarly journals Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 519 ◽  
Author(s):  
Weibo Zhang ◽  
Jianzhong Zhou
Keyword(s):  
Diagnostic Algorithm ◽  
Feature Space ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Classification Performance ◽  
Fault Classification ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions

Aimed at distinguishing different fault categories of severity of rolling bearings, a novel method based on feature space reconstruction and multiscale permutation entropy is proposed in the study. Firstly, the ensemble empirical mode decomposition algorithm (EEMD) was employed to adaptively decompose the vibration signal into multiple intrinsic mode functions (IMFs), and the representative IMFs which contained rich fault information were selected to reconstruct a feature vector space. Secondly, the multiscale permutation entropy (MPE) was used to calculate the complexity of reconstructed feature space. Finally, the value of multiscale permutation entropy was presented to a support vector machine for fault classification. The proposed diagnostic algorithm was applied to three groups of rolling bearing experiments. The experimental results indicate that the proposed method has better classification performance and robustness than other traditional methods.

scholarly journals CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 617
Author(s):  
Umer Saeed ◽  
Young-Doo Lee ◽  
Sana Ullah Jan ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Diagnostic System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Context Aware ◽  
Sensor Faults ◽  
Training Time ◽  
Low Intensity ◽  
Fault Diagnostic

Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

scholarly journals Application of Variational Mode Decomposition and Permutation Entropy for Rolling Bearing Fault Diagnosis

2019 ◽  
Vol 24 (2) ◽  
pp. 303-311 ◽  
Author(s):  
Xiaoxia Zheng ◽  
Guowang Zhou ◽  
Dongdong Li ◽  
Haohan Ren
Keyword(s):  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Bearing Vibration

Rolling bearings are the key components of rotating machinery. However, the incipient fault characteristics of a rolling bearing vibration signal are weak and difficult to extract. To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM). In the proposed method, the bearing vibration signal is decomposed by VMD, and the intrinsic mode functions (IMFs) are obtained in different scales. Then, the PE values of each IMF are calculated to uncover the multi-scale intrinsic characteristics of the vibration signal. Finally, PE values of IMFs are fed into SVM to automatically accomplish the bearing condition identifications. The proposed method is evaluated by rolling bearing vibration signals. The results indicate that the proposed method is superior and can diagnose rolling bearing faults accurately.

scholarly journals Safety Region Estimation and State Identification of Rolling Bearing Based on Statistical Feature Extraction

2013 ◽  
Vol 20 (5) ◽  
pp. 833-846 ◽  
Author(s):  
Yuan Zhang ◽  
Yong Qin ◽  
Zongyi Xing ◽  
Limin Jia ◽  
Xiaoqing Cheng
Keyword(s):  
Principal Component ◽  
Rolling Bearing ◽  
Least Square ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Time Interval ◽  
Intrinsic Mode Functions ◽  
Outer Race ◽  
State Identification ◽  
Bearing Condition Monitoring

The idea of safety region was introduced into the rolling bearing condition monitoring. The safety region estimation and the state identification of the rolling bearing operational were performed by the comprehensive utilization of Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA), and the Least Square Support Vector Machine (LSSVM). The collected vibration data was segmented according to a certain time interval, and then the Intrinsic Mode Functions (IMFs) of each piece of the data were obtained by EMD. The control limits of two statistical variables extracted by PCA were presented as state characteristics. The safety region estimation for the rolling bearing operational status was performed by two-class LSSVM. The states of normal bearing, ball fault, inner race fault, and outer race fault were identified by the multi-class LSSVM. The results show that the estimation accuracy for both the safety region and the states identification reached 95%, and that the validity of the proposed method was verified.

Rolling bearing fault detection approach based on improved dispersion entropy and AFSA optimized SVM

2020 ◽  
pp. 002072092094058
Author(s):  
Wuqiang Liu ◽  
Jinxing Shen ◽  
Xiaoqiang Yang
Keyword(s):  
Fault Detection ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Classification Model ◽  
Coarse Grained ◽  
Fault Classification ◽  
Support Vector ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm

The support vector machine (SVM) does not have a fixed parameter selection method and the manual selection of parameters is difficult to determine the validity, which affects the accuracy of recognition. simultaneously, The existing coarse-grained approach cannot effectively analyze the high-frequency components of time series. In view of the shortcomings of the above method, we put forward a new technique of rolling bearings for fault detection, which combines wavelet packet dispersion entropy (WPDE) and artificial fish swarm algorithm (AFSA) optimize support vector machines (AFSA-SVM). First of all, wavelet packet is devoted to decompose the original vibration signal into components of different frequency bands. Secondly, the dispersion entropy (DE) are calculated for each of the obtained frequency band components to acquire more comprehensive and complete fault information. Afterward, Input feature samples into the SVM model for training, and AFSA is used to optimize the parameters of SVM to obtain the optimal value so as to establish the best classification model. Finally, the prepared test set is input into AFSA-SVM for fault classification. The achievement of bearing detection experiments show that this approach can accurately and quickly identify fault types.

Rolling bearing fault diagnosis of PSO–LSSVM based on CEEMD entropy fusion

2020 ◽  
Vol 44 (3) ◽  
pp. 405-418
Author(s):  
Shuzhi Gao ◽  
Tianchi Li ◽  
Yimin Zhang
Keyword(s):  
Fault Diagnosis ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Kernel Principal Component Analysis ◽  
Permutation Entropy ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Taking aim at the nonstationary nonlinearity of the rolling bearing vibration signal, a rolling bearing fault diagnosis method based on the entropy fusion feature of complementary ensemble empirical mode decomposition (CEEMD) is proposed in combination with information fusion theory. First, CEEMD of the vibration signal of the rolling bearing is performed. Then the signal is decomposed into the sum of several intrinsic mode functions (IMFs), and the singular entropy, energy entropy, and permutation entropy are obtained for the IMFs with fault features. Second, the feature extraction method of entropy fusion is proposed, and the three entropy data obtained are input into kernel principal component analysis (KPCA) for feature fusion and dimensionality reduction to obtain complementary features. Finally, the extracted features are imported into the particle swarm optimization (PSO) algorithm to optimize the least-squares support vector machine (LSSVM) for fault classification. Through experimental verification, the proposed method can be used for roller bearing fault diagnosis.

Study on Unified Chaotic System-Based Wind Turbine Blade Fault Diagnostic System

2015 ◽  
Vol 25 (03) ◽  
pp. 1550042 ◽  
Author(s):  
Ying-Che Kuo ◽  
Chin-Tsung Hsieh ◽  
Her-Terng Yau ◽  
Yu-Chung Li
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Chaotic System ◽  
Wind Turbines ◽  
Diagnostic System ◽  
Vibration Signal ◽  
Wind Turbine Blade ◽  
Operating Conditions ◽  
Unified Chaotic System ◽  
Fault Diagnostic

At present, vibration signals are processed and analyzed mostly in the frequency domain. The spectrum clearly shows the signal structure and the specific characteristic frequency band is analyzed, but the number of calculations required is huge, resulting in delays. Therefore, this study uses the characteristics of a nonlinear system to load the complete vibration signal to the unified chaotic system, applying the dynamic error to analyze the wind turbine vibration signal, and adopting extenics theory for artificial intelligent fault diagnosis of the analysis signal. Hence, a fault diagnostor has been developed for wind turbine rotating blades. This study simulates three wind turbine blade states, namely stress rupture, screw loosening and blade loss, and validates the methods. The experimental results prove that the unified chaotic system used in this paper has a significant effect on vibration signal analysis. Thus, the operating conditions of wind turbines can be quickly known from this fault diagnostic system, and the maintenance schedule can be arranged before the faults worsen, making the management and implementation of wind turbines smoother, so as to reduce many unnecessary costs.

scholarly journals A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 290
Author(s):  
Jianghua Ge ◽  
Tianyu Niu ◽  
Di Xu ◽  
Guibin Yin ◽  
Yaping Wang
Keyword(s):  
Fault Diagnosis ◽  
High Frequency ◽  
Signal Reconstruction ◽  
Pearson Correlation ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Multi Scale ◽  
Reconstructed Signal

Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%.

scholarly journals Application of Feature Fusion Using Coaxial Vibration Signal for Diagnosis of Rolling Element Bearings

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jing Jiao ◽  
Jianhai Yue ◽  
Di Pei ◽  
Zhunqing Hu
Keyword(s):  
Fault Diagnosis ◽  
Feature Fusion ◽  
Random Noise ◽  
Vibration Signal ◽  
Fault Classification ◽  
Support Vector ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Rolling Element ◽  
Vibration Signal Analysis

The research of rolling element bearings (REBs) fault diagnosis based on single sensor vibration signal analysis is very common. However, the information provided by an individual sensor is very limited, and the robustness of the system is poor. In this paper, a novel fault diagnosis method based on coaxial vibration signal feature fusion (CVSFF) is proposed to fully analyze the multisensor information of the system and build a more reliable diagnostic system. An ensemble empirical mode decomposition (EEMD) method is used to decompose the original vibration signal into a number of intrinsic mode functions (IMFs). Then the autocorrelation analysis is introduced to reduce the random noise remaining in IMFs. After that, the Rényi entropy is calculated as the feature of bearings. Finally, the features of coaxial vibration signal are fused by a multiple-kernel learning support vector machine (MKL-SVM) to classify bearing conditions. In order to verify the effectiveness of the CVSFF method in REB diagnosis, eight data sets from the Case Western Reserve University Bearing Data Center are selected. The fault classification results demonstrate that the proposed approach is a valuable tool for bearing faults detection, and the fused feature from coaxial sensors improves fault classification accuracy for REBs.

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