A modified threshold function for de-noising of rolling bearing vibration signal

2018 ◽  
Author(s):  
Hongmei Qi ◽  
Hesheng Zhang ◽  
Ke Fang ◽  
Yimei Dai
Keyword(s):  
Vibration Signal ◽  
Rolling Bearing ◽  
Threshold Function ◽  
Bearing Vibration

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.

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 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 Rolling bearing fault diagnosis based on adaptive smooth ITD and MF-DFA method

2019 ◽  
Vol 39 (4) ◽  
pp. 968-986
Author(s):  
Zhe Yuan ◽  
Tingting Peng ◽  
Dong An ◽  
Daniel Cristea ◽  
Mihai Alin Pop
Keyword(s):  
Fault Diagnosis ◽  
Time Scale ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Identification Accuracy ◽  
Threshold Function ◽  
Fluctuation Analysis ◽  
Intrinsic Time ◽  
Time Scale Decomposition ◽  
Proper Rotation

To effectively utilize a feature set to further improve fault diagnosis of a rolling bearing vibration signal, a method based on multi-fractal detrended fluctuation analysis (MF-DFA) and smooth intrinsic time-scale decomposition (SITD) was proposed. The vibration signal was decomposed into several proper rotation components by applying this new SITD method to overcome noise effects, preserve the effective signal, and improve the signal-to-noise ratio. Wavelet analysis was embedded in iteration procedures of intrinsic time-scale decomposition (ITD). For better results, an adaptive threshold function was used for signal recovery from noisy proper rotation components in the wavelet domain. Additionally, MF-DFA was used to reveal the multi-fractality present in the instantaneous amplitude of the proper rotation components. Finally, linear local tangent space alignment was applied for feature dimension reduction and to obtain fault characteristics of different types, further improving identification accuracy. The performance of the proposed method is determined to be superior to that of the ITD-MF-DFA method.

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.

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 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.

Sign in / Sign up

Export Citation Format

Share Document