scholarly journals Correlation Coefficient of Simplified Neutrosophic Sets for Bearing Fault Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Lilian Shi
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Wavelet Packet ◽  
Energy Eigenvalue ◽  
Rolling Bearing ◽  
Neutrosophic Sets ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Features

In order to process the vagueness in vibration fault diagnosis of rolling bearing, a new correlation coefficient of simplified neutrosophic sets (SNSs) is proposed. Vibration signals of rolling bearings are acquired by an acceleration sensor, and a morphological filter is used to reduce the noise effect. Wavelet packet is applied to decompose the vibration signals into eight subfrequency bands, and the eigenvectors associated with energy eigenvalue of each frequency are extracted for fault features. The SNSs of each fault types are established according to energy eigenvectors. Finally, a correlation coefficient of two SNSs is proposed to diagnose the bearing fault types. The experimental results show that the proposed method can effectively diagnose the bearing faults.

A clustering K-SVD-based sparse representation method for rolling bearing fault diagnosis

2021 ◽  
Vol 63 (3) ◽  
pp. 160-167
Author(s):  
Qingwen Yu ◽  
Jimeng Li ◽  
Zhixin Li ◽  
Jinfeng Zhang
Keyword(s):  
Fault Diagnosis ◽  
Sparse Representation ◽  
Rolling Bearing ◽  
Vibration Signals ◽  
Redundant Dictionary ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Representation Method ◽  
The Impact

It is challenging to extract weak impulse features from vibration signals corrupted by strong noise in mechanical fault diagnosis. Due to its simple calculation, fast convergence and easy implementation, K-singular value decomposition (K-SVD) has been widely used in rolling bearing fault diagnosis. However, it fails to consider the influence of noise and harmonics on atoms learning from impulse characteristics, which results in many irrelevant atoms, and then increases the difficulty of extracting the impulse features in bearing fault signals. Therefore, a clustering K-SVD-based sparse representation method is proposed in this paper and it is combined with the particle swarm optimisation (PSO)-based time-varying filter empirical mode decomposition (TVF-EMD) for rolling bearing fault diagnosis. The PSO-based TVF-EMD is developed to automatically decompose the original signal to eliminate the impact of noise and harmonics on the impulses in the signal. Then, the clustering K-SVD method is applied to perform dictionary learning on the sensitive component containing impulses to obtain a redundant dictionary of atoms with obvious impulse patterns. Finally, the orthogonal matching pursuit (OMP) algorithm is introduced to extract the fault features from rolling bearing vibration signals. The experimental results show that the proposed method can improve the robustness of the dictionary atoms to noise and achieve the extraction of rolling bearing fault features.

scholarly journals A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

2018 ◽  
Vol 8 (9) ◽  
pp. 1621 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Yong Ren ◽  
Gongbo Zhou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Feature Space ◽  
Support Vector ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Acceleration Sensors ◽  
Reconstructed Signal ◽  
Original Feature

Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet and Improved BP Neural Network for Wind Turbines

2013 ◽  
Vol 347-350 ◽  
pp. 117-120
Author(s):  
Zhao Ran Hou
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Frequency Domain ◽  
Wind Turbines ◽  
Wavelet Packet ◽  
Roller Bearing ◽  
Rolling Bearing ◽  
Transmission System ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Vibration signal was a carrier of fault features of the wind turbine transmission system, it can reflect most of the fault information of the wind turbine transmission system. According to the frequency domain features of the roller bearing fault, wavelet packet transform for feature extraction was proposed as the characteristics of wind turbines in the presence of a large number of transient and non-stationary signals. The characteristics of wavelet packet was analyzed, combined with the wind turbines in the rolling bearing fault characteristic vibration extraction methods, the rolling bearing fault diagnosis was realized through the wavelet packet decomposition and reconstruction, the procedure was given. The simulation result shows that this application can reflect relationship of the failure characteristics and frequency domain feature vectors, also the nonlinear mapping ability of neural networks was played and the fault diagnosis capability enhanced.

Rolling Bearing Fault Diagnosis and Health Assessment Based on EMD–TEO

2014 ◽  
Vol 998-999 ◽  
pp. 470-475 ◽  
Author(s):  
You Ning Tang ◽  
Chen Lu ◽  
Jia Meng Hu
Keyword(s):  
Fault Diagnosis ◽  
Energy Spectrum ◽  
Rolling Bearing ◽  
Health State ◽  
Mahalanobis Distances ◽  
Vibration Signals ◽  
The Real ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Teager Energy

An intelligent rolling bearing fault diagnosis method is proposed using empirical mode decomposition (EMD)–Teager energy operator (TEO) and Mahalanobis distance. EMD can adaptively decompose vibration signals into a series of intrinsic mode functions (IMFs), which are zero mean monocomponent AM–FM signals. TEO can estimate the total mechanical energy required to generate signals. Thus, TEO exhibits good time resolution and self-adaptive ability with regard to the transient components of the signal, which is an advantage in detecting signal impact characteristics. With regard to the impulse feature of the bearing fault vibration signals, TEO can be used to detect the cyclical impulse characteristic caused by bearing failure, gain an instantaneous amplitude spectrum for each IMF component, and then identify the characteristic frequency of a single, interesting IMF component in the bearing fault by means of the Teager energy spectrum. The amplitude of the Teager energy spectrum in the inner race and outer race fault frequencies, as well as the ratio of the energy of the resonance frequency band to the total energy, were extracted as feature vectors, which were then separately used as training samples and test samples for fault diagnosis. Thereafter, the Mahalanobis distances between the real measure and the different overall types of fault samples were calculated to classify the real condition of the rolling bearing. Finally, the Mahalanobis distances were converted into CV values, which assessed the current health state of the rolling bearing. Experimental results prove that this method can accurately identify and diagnose different fault types of rolling bearings.

scholarly journals A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method

2019 ◽  
Vol 9 (11) ◽  
pp. 2356 ◽  
Author(s):  
Yinsheng Chen ◽  
Tinghao Zhang ◽  
Zhongming Luo ◽  
Kun Sun
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Relevance Vector Machine ◽  
Sample Entropy ◽  
Analysis Method ◽  
Bearing Fault Diagnosis ◽  
Energy Entropy ◽  
Fault Severity ◽  
Fault Features

To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is proposed in this paper. A fast sample entropy calculation method based on a kd tree is adopted to improve the real-time performance of fault detection in this paper. In view of the non-linearity and non-stationarity of the vibration signals, the vibration signal of the rolling bearing is decomposed into several sub-signals containing fault information by using a wavelet packet. Then, the energy entropy values of the sub-signals decomposed by the wavelet packet are calculated to generate the feature vectors for describing different fault types and severity levels of rolling bearings. The multiclass relevance vector machine modeled by the feature vectors of different fault types and severity levels is used to realize fault type identification and a fault severity analysis of the bearings. The proposed fault diagnosis and severity analysis method is fully evaluated by experiments. The experimental results demonstrate that the fault detection method based on the sample entropy can effectively detect rolling bearing failure. The fault feature extraction method based on the wavelet packet energy entropy can effectively extract the fault features of vibration signals and a multiclass relevance vector machine can identify the fault type and severity by means of the fault features contained in these signals. Compared with some existing bearing rolling fault diagnosis methods, the proposed method is excellent for fault diagnosis and severity analysis and improves the fault identification rate reaching as high as 99.47%.

scholarly journals Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network

2020 ◽  
Vol 10 (3) ◽  
pp. 770 ◽  
Author(s):  
Guoqiang Li ◽  
Chao Deng ◽  
Jun Wu ◽  
Zuoyi Chen ◽  
Xuebing Xu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Wavelet Packet Transform ◽  
Fault Classification ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Timely sensing the abnormal condition of the bearings plays a crucial role in ensuring the normal and safe operation of the rotating machine. Most traditional bearing fault diagnosis methods are developed from machine learning, which might rely on the manual design features and prior knowledge of the faults. In this paper, based on the advantages of CNN model, a two-step fault diagnosis method developed from wavelet packet transform (WPT) and convolutional neural network (CNN) is proposed for fault diagnosis of bearings without any manual work. In the first step, the WPT is designed to obtain the wavelet packet coefficients from raw signals, which then are converted into the gray scale images by a designed data-to-image conversion method. In the second step, a CNN model is built to automatically extract the representative features from gray images and implement the fault classification. The performance of the proposed method is evaluated by a real rolling-bearing dataset. From the experimental study, it can be seen the proposed method presents a more superior fault diagnosis capability than other machine-learning-based methods.

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