Varying Rotation Speed Bearing Fault Features and Extraction Method Based on Wavelet Packet

2014 ◽  
Vol 644-650 ◽  
pp. 286-289
Author(s):  
Bin Wu ◽  
Shan Ping Yu ◽  
Yue Gang Luo ◽  
Chang Jian Feng
Keyword(s):  
Rotation Speed ◽  
Hydrodynamic Lubrication ◽  
Contact Stiffness ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Resonance Frequencies ◽  
Bearing Vibration ◽  
The Impact

When bearing rotates, it comes with elastic hydrodynamic lubrication effect. Interaction between the effect and the bearing vibration leads to the change of lubricant film thickness, thus, contact stiffness of contact pair changes along with the rotation speed of the bearing, and then the resonance frequencies of the bearing system changes according to the rotation speed. In addition, the impact signal of varying speed bearing damage point no longer has the periodic characteristics. Based on the analysis of the bearing failure mechanism, this paper proposed a varying speed bearing vibration signal fault model, and also utilizes wavelet packet to extract the bearing fault signal by means of a variable speed rolling bearing vibration experiment table.

scholarly journals Improving rolling bearing online fault diagnostic performance based on multi-dimensional characteristics

2018 ◽  
Vol 5 (5) ◽  
pp. 180066 ◽  
Author(s):  
Chuanlei Yang ◽  
Hechun Wang ◽  
Zhanbin Gao ◽  
Xinjie Cui
Keyword(s):  
Pattern Recognition ◽  
Health Status ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Economic Losses ◽  
Bearing Fault ◽  
Box Counting ◽  
Box Counting Dimension ◽  
Bearing Vibration ◽  
Fault Diagnostic

As the main cause of failure and damage to rotating machinery, rolling bearing failure can result in huge economic losses. As the rolling bearing vibration signal is nonlinear and has non-stationary characteristics, the health status information distributed in the rolling bearing vibration signal is complex. Using common time-domain or frequency-domain approaches cannot easily enable an accurate assessment of rolling bearing health. In this paper, a novel rolling bearing fault diagnostic method based on multi-dimensional characteristics was developed to meet the requirements for accurate diagnosis of different fault types and severities with real-time computational performance. First, a multi-dimensional feature extraction algorithm based on entropy characteristics, Holder coefficient characteristics and improved generalized fractal box-counting dimension characteristics was performed to extract the health status feature vectors from the bearing vibration signals. Second, a grey relation algorithm was employed to achieve bearing fault pattern recognition intelligently using the extracted multi-dimensional feature vector. This experimental study has illustrated that the proposed method can effectively recognize different fault types and severities after integration of the improved fractal box-counting dimension into the multi-dimensional characteristics, in comparison with existing pattern recognition methods.

scholarly journals Automated Bearing Fault Diagnosis using Packet Features of Vibration Signal and Gaussian Support Vector Machine

2020 ◽  
Vol 8 (6S) ◽  
pp. 72-81
Keyword(s):  
Support Vector Machine ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
True Positive Rate ◽  
Support Vector ◽  
True Negative ◽  
Bearing Fault ◽  
Dimensional Reduction Method ◽  
Positive Rate

Effective detection of the bearing fault and, specifically performance dilapidation assessment of a bearing is the topic of intensive analysis that may scale back prices and therefore the nonscheduled down time. This article presents an adaptive approach that is based on Bhattacharya space ranking method and dimensional reduction method as general discriminate analysis (GDA) with Gaussian support vector machine (GSVM) to accurately detect the defects of rolling bearing. For this investigation, first, vibration signal generated by rolling bearing was disintegrated to five levels employing wavelet packet (WP) method. Sixty three logarithmic wavelet packet features (LWPFs) were taken out from five level disintegrated vibration signals. After this, sixty three features were ranked by Bhattacharya space and top ten LWPFs were chosen. The top ten features were reduced to a new feature using GDA for effective detection and then applied to GSVM for detection of bearing fault. The experimental results show that new automated diagnosing approach attained classifier performance parameters as sensitivity (SE) or true positive rate, specificity (SP) or true negative rate, accuracy (AC) and positive prediction value (PPV) of 100, 98.50, 100 and 99.67 % for inner raceway (IR) and, AC: 99.49, SE: 100, SP: 98.78 and PPV: 99.87 for ball bearing (BB) at 0.18 mm diameter faults.

Research of Rolling Bearing Fault Feature Extraction Based on EMD and Choi-Williams

2013 ◽  
Vol 694-697 ◽  
pp. 1377-1381
Author(s):  
Xing Chun Wei ◽  
Yu Lin Tang ◽  
Tao Chen
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Outer Ring ◽  
Intrinsic Mode Functions ◽  
Linear Superposition ◽  
Time Frequency ◽  
Bearing Fault ◽  
Rolling Element ◽  
Bearing Vibration

Aiming at rolling bearing fault signal of the non stationary feature, Apply a new method to the rolling bearing vibration signal of feature extraction, which is combined the Empirical Mode Decomposition (EMD) and the Choi-Williams distribution. Firstly, original signals were decomposed into a series of intrinsic mode functions (IMF) of different scales. To the decomposed each IMF component for Choi-Williams time-frequency analysis, Then take the linear superposition, finally obtained the rolling bearing vibration signal of Choi-Williams distribution. After the analyses of the rolling bearing inner ring, outer ring and rolling element fault signal ,the results show that this method can effectively suppress the frequency aliasing and interference caused by cross terms. And be able to accurately extract the fault frequency of the bearing inner ring, outer ring and rolling element, lay the foundation for the subsequent rolling bearing state recognition.

scholarly journals A Fault Diagnosis Approach for Rolling Bearing Integrated SGMD, IMSDE and Multiclass Relevance Vector Machine

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4352 ◽  
Author(s):  
Xiaoan Yan ◽  
Ying Liu ◽  
Minping Jia
Keyword(s):  
Fault Diagnosis ◽  
Symplectic Geometry ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Relevance Vector Machine ◽  
Symbolic Dynamic ◽  
Bearing Fault ◽  
Fault Features ◽  
Bearing Vibration ◽  
Diagnosis Approach

The vibration signal induced by bearing local fault has strong nonstationary and nonlinear property, which indicates that the conventional methods are difficult to recognize bearing fault patterns effectively. Hence, to obtain an efficient diagnosis result, the paper proposes an intelligent fault diagnosis approach for rolling bearing integrated symplectic geometry mode decomposition (SGMD), improved multiscale symbolic dynamic entropy (IMSDE) and multiclass relevance vector machine (MRVM). Firstly, SGMD is employed to decompose the original bearing vibration signal into several symplectic geometry components (SGC), which is aimed at reconstructing the original bearing vibration signal and achieving the purpose of noise reduction. Secondly, the bat algorithm (BA)-based optimized IMSDE is presented to evaluate the complexity of reconstruction signal and extract bearing fault features, which can solve the problems of missing of partial fault information existing in the original multiscale symbolic dynamic entropy (MSDE). Finally, IMSDE-based bearing fault features are fed to MRVM for achieving the identification of bearing fault categories. The validity of the proposed method is verified by the experimental and contrastive analysis. The results show that our approach can precisely identify different fault patterns of rolling bearings. Moreover, our approach can achieve higher recognition accuracy than several existing methods involved in this paper. This study provides a new research idea for improvement of bearing fault identification.

The Feature Extraction of Rolling Bearing Fault Based on Wavelet Packet-EMD Energy Distribution

2012 ◽  
Vol 233 ◽  
pp. 234-238 ◽  
Author(s):  
Cheng Wen ◽  
Chuan De Zhou
Keyword(s):  
Energy Distribution ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Resonance Signal ◽  
Outer Race ◽  
Bearing Fault ◽  
Noise Characteristics ◽  
Mode Decomposition ◽  
Simulation Signal

The new signal analysis method based on the combination of wavelet packet and empirical mode decomposition (EMD) energy distribution was proposed for rolling bearing vibration signal presenting modulating characteristic, non-stationary characteristics and containing a lot of noise characteristics. In this method, initial vibration signal was decomposed first by wavelet packet to extract the resonance signal with obvious modulating characteristics. Then the resonance signal was decomposed by EMD method and energy distribution of each Intrinsic Mode Function (IMF) was obtained. Finally the IMF component, which can reflect the vibration condition, was processed by Hilbert envelope demodulation to extract rolling bearing fault characteristics information. The application analysis of the simulation signal and fault signal of inner race, outer race and rolling element of rolling bearing shows that this method can effectively analyze rolling bearing fault information and realize the fault diagnosis.

scholarly journals Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jun He ◽  
Xiang Li ◽  
Yong Chen ◽  
Danfeng Chen ◽  
Jing Guo ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Learning Method ◽  
Target Domain ◽  
Source Domain ◽  
Second Order Statistics ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

In mechanical fault diagnosis, it is impossible to collect massive labeled samples with the same distribution in real industry. Transfer learning, a promising method, is usually used to address the critical problem. However, as the number of samples increases, the interdomain distribution discrepancy measurement of the existing method has a higher computational complexity, which may make the generalization ability of the method worse. To solve the problem, we propose a deep transfer learning method based on 1D-CNN for rolling bearing fault diagnosis. First, 1-dimension convolutional neural network (1D-CNN), as the basic framework, is used to extract features from vibration signal. The CORrelation ALignment (CORAL) is employed to minimize marginal distribution discrepancy between the source domain and target domain. Then, the cross-entropy loss function and Adam optimizer are used to minimize the classification errors and the second-order statistics of feature distance between the source domain and target domain, respectively. Finally, based on the bearing datasets of Case Western Reserve University and Jiangnan University, seven transfer fault diagnosis comparison experiments are carried out. The results show that our method has better performance.

Enhanced deep residual network with multilevel correlation information for fault diagnosis of rotating machinery

2020 ◽  
pp. 107754632094971 ◽  
Author(s):  
Shoucong Xiong ◽  
Shuai He ◽  
Jianping Xuan ◽  
Qi Xia ◽  
Tielin Shi
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Learning Algorithms ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Classification Performance ◽  
Residual Network ◽  
Time Frequency ◽  
Correlation Information

Modern machinery becomes more precious with the advance of science, and fault diagnosis is vital for avoiding economical losses or casualties. Among massive diagnosis methods, deep learning algorithms stand out to open an era of intelligent fault diagnosis. Deep residual networks are the state-of-the-art deep learning models which can continuously improve performance by deepening the network structures. However, in vibration-based fault diagnosis, the transient property instability of vibration signal usually calls for time–frequency analysis methods, and the characters of time–frequency matrices are distinct from standard images, which brings some natural limitations for the diagnosis performance of deep learning algorithms. To handle this issue, an enhanced deep residual network named the multilevel correlation stack-deep residual network is proposed in this article. Wavelet packet transform is used to preprocess the sensor signal, and then the proposed multilevel correlation stack-deep residual network uses kernels with different shapes to fully dig various kinds of useful information from any local regions of the processed input. Experiments on two rolling bearing datasets are carried out. Test results show that the multilevel correlation stack-deep residual network exhibits a more satisfactory classification performance than original deep residual networks and other similar methods, revealing significant potentials for realistic fault diagnosis applications.

The Bearing Vibration Signal Collecting System Based on STM32F103C8T6

2014 ◽  
Vol 971-973 ◽  
pp. 1376-1379
Author(s):  
Zhong Hu Yuan ◽  
Man Yang Xu ◽  
Xiao Xuan Qi
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Bearing Fault Diagnosis ◽  
Communication Module ◽  
Bearing Vibration ◽  
Signal Conditioning Circuit ◽  
Collecting System ◽  
Conditioning Circuit ◽  
New System

Vibration signal collecting is an important step in rolling bearing fault diagnosis process. The collected signal can exhibit effectiveness of the fault depend on the signal collecting system. Combine the attribute of the rolling bearing, a new signal collecting system base on the STM32F103C8T6 is designed in this paper. The new system is made of supply circuit, signal conditioning circuit, AD conversion circuit and communication module.

High Resonance Component of Resonance-Based Sparse Decomposition Application in Extraction of Rolling Bearing Fault Information

2013 ◽  
Vol 753-755 ◽  
pp. 2290-2296 ◽  
Author(s):  
Wen Tao Huang ◽  
Yin Feng Liu ◽  
Pei Lu Niu ◽  
Wei Jie Wang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Frequency Resolution ◽  
Sparse Decomposition ◽  
High Resonance ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Information Frequency ◽  
Fault Information

In the early fault diagnosis of rolling bearing, the vibration signal is mixed with a lot of noise, resulting in the difficulties in analysis of early weak fault signal. This article introduces resonance-based signal sparse decomposition (RSSD) into rolling bearing fault diagnosis, and studies the fault information contained in high resonance component and low resonance component. This article compares the effect of the two resonance components to extract rolling bearing fault information in four aspects: the amount of fault information, frequency resolution of subbands, sensitivity to noise and immunity to autocorrelation processing. We find that the high resonance component has greater advantage in extraction of rolling bearing fault information, and it is able to indicate rolling bearing failure accurately.

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