scholarly journals Cyclostationary Analysis for Gearbox and Bearing Fault Diagnosis

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Zhipeng Feng ◽  
Fulei Chu
Keyword(s):  
Rolling Element Bearing ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Cyclic Frequency ◽  
Fm Signals ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration ◽  
Cyclic Spectrum

Gearbox and rolling element bearing vibration signals feature modulation, thus being cyclostationary. Therefore, the cyclic correlation and cyclic spectrum are suited to analyze their modulation characteristics and thereby extract gearbox and bearing fault symptoms. In order to thoroughly understand the cyclostationarity of gearbox and bearing vibrations, the explicit expressions of cyclic correlation and cyclic spectrum for amplitude modulation and frequency modulation (AM-FM) signals are derived, and their properties are summarized. The theoretical derivations are illustrated and validated by gearbox and bearing experimental signal analyses. The modulation characteristics caused by gearbox and bearing faults are extracted. In faulty gearbox and bearing cases, more peaks appear in cyclic correlation slice of 0 lag and cyclic spectrum, than in healthy cases. The gear and bearing faults are detected by checking the presence or monitoring the magnitude change of peaks in cyclic correlation and cyclic spectrum and are located according to the peak cyclic frequency locations or sideband frequency spacing.

A rolling element bearing fault diagnosis approach based on hierarchical fuzzy entropy and support vector machine

2015 ◽  
Vol 230 (13) ◽  
pp. 2314-2322 ◽  
Author(s):  
Keheng Zhu ◽  
Haolin Li
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Fuzzy Entropy ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration

Aiming at the non-linear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a new rolling element bearing fault diagnosis method based on hierarchical fuzzy entropy and support vector machine is proposed in this paper. By incorporating the advantages of both the concept of fuzzy sets and the hierarchical decomposition of hierarchical entropy, hierarchical fuzzy entropy is developed to extract the fault features from the bearing vibration signals, which can provide more useful information reflecting bearing working conditions than hierarchical entropy. After feature extraction with hierarchical fuzzy entropy, a multi-class support vector machine is trained and then employed to fulfill an automated bearing fault diagnosis. The experimental results demonstrate that the proposed approach can identify different bearing fault types as well as severities precisely.

scholarly journals An improved envelope detection method using particle swarm optimisation for rolling element bearing fault diagnosis

2017 ◽  
Vol 4 (4) ◽  
pp. 305-317 ◽  
Author(s):  
Sunil Tyagi ◽  
S.K. Panigrahi
Keyword(s):  
Fault Diagnosis ◽  
Particle Swarm Optimisation ◽  
Rolling Element Bearing ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Envelope Detection ◽  
Rolling Element ◽  
Element Bearing ◽  
Load Conditions

Abstract Traditionally Envelope Detection (ED) is implemented for detection of rolling element bearing faults by extracting the envelope of band-passed vibration signal and thereafter taking its Fourier transform. The performance of ED is highly sensitive to the envelope window (i.e. central frequency and bandwidth of the passband). This paper employs Particle Swarm Optimisation (PSO) to select the most optimum envelope window to band pass the vibration signals emanating from rotating driveline that was run in normal and with faults induced rolling element bearings. The envelopes of band-passed signals were extracted with the help of Hilbert Transform. The performance of ED whose envelope window was optimised by PSO to identify various commonly occurring bearing faults such as bearing with Outer Race Fault (ORF), Inner Race Fault (IRF) and Rolling Element Fault (REF) were checked under varying load conditions. The performance of ‘ED enhanced by PSO’ was also checked with increase in the severity of defect. It was shown that the improved ED method is successfully able to identify all types of bearing faults under different load conditions. It was shown that the by selecting envelope window by PSO makes ED especially useful to identify bearing faults at the incipient stage of defect. It was also shown by presenting comparative performance that by optimising the envelope window by PSO the performance of ED gets significantly enhanced in comparison to the traditional ED method for bearing fault diagnosis.

Contact characteristic and vibration mechanism of rolling element bearing in the process of fault evolution

2021 ◽  
Vol 235 (1) ◽  
pp. 19-36 ◽  
Author(s):  
Wenbing Tu ◽  
Jinwen Yang ◽  
Wennian Yu ◽  
Ya Luo
Keyword(s):  
Fault Diagnosis ◽  
Vibration Response ◽  
Rolling Element Bearing ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Vibration Mechanism ◽  
Contact Characteristic ◽  
Element Bearing ◽  
Bearing Vibration ◽  
Fault Evolution

The vibration response of rolling element bearing has a close relation with its fault. An accurate evaluation of the bearing vibration response is essential to the bearing fault diagnosis. At present, most bearing dynamics models are built based on rigid assumptions, which may not faithfully reveal the dynamic characteristics of bearing in the presence of fault. Moreover, previous similar works mainly focus on the fault with a specified size without considering the varying contact characteristics as the fault evolves. This paper developed an explicit dynamics finite element model for the bearing with three types of raceway faults considering the flexibility of each bearing component in order to accurately study the contact characteristic and vibration mechanism of defective bearings in the process of fault evolution. The developed model is validated by comparing its simulation results with both analytical and experimental results. The dynamic contact patterns between the rolling elements and the fault, the additional displacement due to the fault and the faulty characteristics within the bearing vibration signal during the fault evolution process are investigated. The analysis results from this work can provide practitioners an in-depth understanding towards the internal contact characteristics with the existence of raceway fault and theoretical basis for rolling bearing fault diagnosis.

Cyclic Spectrum Analysis on Rolling-Element Bearing with Inner-Race Point Defect

2011 ◽  
Vol 291-294 ◽  
pp. 1469-1473
Author(s):  
Wei Ke ◽  
Yong Xiang Zhang ◽  
Lin Li
Keyword(s):  
Point Defect ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing ◽  
Simulation Signal ◽  
Inner Race ◽  
Cyclic Spectrum

Vibration signal of rolling-element bearing is random cyclostationarity when a fault develops, the proper analysis of which can be used for condition monitor. Cyclic spectrum is a common cyclostationary analysis method and has a great many algorithms which have distinct efficiency in different application circumstance, two common algorithms (SSCA and FAM) are compared in the paper. The FAM is recommended to be used in diagnosing rolling-element bearing fault via calculation of simulation signal in different signal to noise ratio. The cyclic spectrum of practice signal of rolling-element bearing with inner-race point defect is analyzed and a new characteristic extraction method is put forward. The preferable result is acquired verify the correctness of the analysis and indicate that the cyclic spectrum is a robust method in diagnosing rolling-element bearing fault.

Rolling element bearing fault diagnosis based on multi-scale global fuzzy entropy, multiple class feature selection and support vector machine

2019 ◽  
Vol 41 (14) ◽  
pp. 4013-4022 ◽  
Author(s):  
Keheng Zhu ◽  
Liang Chen ◽  
Xiong Hu
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Entropy ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Vibration Signals ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration

Multi-scale fuzzy entropy (MFE) is a recently developed non-linear dynamic parameter for measuring the complexity of vibration signals of rolling element bearing over different scales. However, the calculation of fuzzy entropy (FuzzyEn) in each scale ignores the sequence’s global characteristics while the bearing vibration signals’ global fluctuation may vary as the bearing runs under different states. Therefore, in this paper, the multi-scale global fuzzy entropy (MGFE) method is put forward for extracting the fault features from the bearing vibration signals. After the feature extraction, multiple class feature selection (MCFS) method is introduced to select the most informative features from the high-dimensional feature vector. Then, a new rolling element bearing fault diagnosis approach is proposed based on MGFE, MCFS and support vector machine (SVM). The experimental results indicate that the proposed approach can effectively fulfill the fault diagnosis of rolling element bearing and has good classification performance.

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