scholarly journals Rolling Bearing Diagnosis Based on Adaptive Probabilistic PCA and the Enhanced Morphological Filter

2020 ◽  
Vol 2020 ◽  
pp. 1-26
Author(s):  
Yuanqing Luo ◽  
Changzheng Chen ◽  
Siyu Zhao ◽  
Xiangxi Kong ◽  
Zhong Wang
Keyword(s):  
Fault Diagnosis ◽  
Principal Component ◽  
Rolling Bearing ◽  
Rolling Element Bearing ◽  
Experimental Comparison ◽  
Probabilistic Pca ◽  
Comparison Results ◽  
Grasshopper Optimization Algorithm ◽  
Rolling Element ◽  
Element Bearing

Early fault diagnosis of rolling element bearing is still a difficult problem. Firstly, in order to effectively extract the fault impulse signal of the bearing, a new enhanced morphological difference operator (EMDO) is constructed by combining two optimal feature extraction-type operators. Next, in the process of processing the test signal, in order to reduce the interference problem caused by strong background noise, the probabilistic principal component analysis (PPCA) method is introduced. In the traditional PPCA method, two important system parameters (decomposition principal component k and original variable n) are usually set artificially; this will greatly reduce the noise reduction performance of PPCA. To solve this problem, a parameter adaptive PPCA method based on grasshopper optimization algorithm (GOA) is proposed. Finally, combining the advantages of the above algorithms, a comprehensive rolling bearing fault diagnosis method named APPCA-EMDF is proposed in this paper. Experimental comparison results show that the proposed method can effectively diagnose the vibration signals of rolling element bearing.

Fault Diagnosis based on Wavelet Analysis for Bearings under Low Rotating Speed

2011 ◽  
Vol 291-294 ◽  
pp. 2006-2009
Author(s):  
Hua Qing Wang ◽  
Yong Wei Guo ◽  
Jin Ji Gao ◽  
Feng Wang
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Rolling Bearing ◽  
Rolling Element Bearing ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Rotating Speed ◽  
Analysis Technique ◽  
Rolling Element ◽  
Element Bearing

Bearing faults signal is very weak under a low rotating speed, and therefore fault diagnosis for bearings under a low rotating speed, is more difficult than under a high rotating speed. The wavelet analysis technique is adopted for fault diagnosis of rolling element bearing under low rotating speed. This work also acquired vibration signals and acoustic emission signals from the rolling bearing under low speed respectively, and analyzed the both kinds of signals for diagnosing the typical bearing faults contrastively.

Research on Fault Diagnosis and Application for Rolling Element Bearing Based on Fuzzy Analysis

2011 ◽  
Vol 189-193 ◽  
pp. 1358-1361 ◽  
Author(s):  
Xiao Guang Yu ◽  
Jian Liu
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Theory ◽  
Amplitude Spectrum ◽  
Rolling Bearing ◽  
Rolling Element Bearing ◽  
Ring Fault ◽  
Rolling Element ◽  
Rolling Body ◽  
Element Bearing ◽  
Fuzzy Diagnosis

In order to simulate the fault of a single rolling bearing, this paper used the fault diagnosis lab desk to simulate some representative conditions of 30205 type of rolling element bearing, including regular condition, inner ring fault, outer ring fault and rolling body fault. It also adopted fuzzy theory to analyze signals and diagnose faults on the base of the MATLAB software desk. This paper also adopts the amplitude spectrum, the power spectrum, the envelope demodulated spectrum and the delayed correlation-envelop spectrum to diagnosis and analyze simulating signals. Comparatively, the fuzzy diagnosis theory is dependable．

scholarly journals A Sparse Underdetermined Blind Source Separation Method and Its Application in Fault Diagnosis of Rotating Machinery

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
HongChao Wang ◽  
WenLiao Du
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Rolling Element Bearing ◽  
Underdetermined Blind Source Separation ◽  
Rolling Element ◽  
Element Bearing ◽  
Non Gaussian

Rolling element bearing is one of the most commonly used supporting parts in rotating machinery, and it is also one of the most easily failing rotating parts. It is of great safety and economic significance to study the effective fault diagnosis method of rolling element bearing. The fault characteristic signal of rolling bearing is often affected by other interference signals in practical engineering, and the situation is much more serious when the rolling bearing fault occurs in gearbox. Besides, only a limited number of measuring points are used in the process of rolling bearing fault signal acquisition due to the limitation of sensors installation condition. In some sense, the above two factors often cause the result that the fault diagnosis of rolling bearing is the problem of underdetermined blind source separation. The independence and non-Gaussian characteristic of the observed signals are the prerequisite of most of existent blind source separation methods. Unlike traditional blind source separation methods, SCA originating from sparse representation is an effective method to solve the problem of underdetermined blind source separation, because it does not require the independence or non-Gaussian characteristics of the observed signals, and it only makes full use of the sparse characteristics of the observed signals to extract the source signal from the observed signals. Based on these, a sparse component analysis (SCA) method based on linear clustering (LC) named LC-SCA is proposed for the purpose of underdetermined blind source separation of vibration signals of rolling element bearing, and the LC is introduced into SCA to improve the computation efficiency of SCA. The effectiveness of the proposed method is verified by simulation and experiment. In addition, the superiority of the method is verified by comparison with the other related methods such as constrained independent component analysis (cICA) and SCA.

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.

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