Rolling element bearing health diagnosis – A comparative study of statistical features

2021 ◽  
Author(s):  
H.S. Kumar ◽  
Upadhyaya Gururaj ◽  
Pai P. Srinivasa
Keyword(s):  
Comparative Study ◽  
Rolling Element Bearing ◽  
Statistical Features ◽  
Rolling Element ◽  
Element Bearing ◽  
Health Diagnosis

scholarly journals Rolling Element Bearing Fault Detection using Statistical Features and Ensemble Classifiers

2020 ◽  
Vol 9 (3) ◽  
pp. 350-358
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Machine Learning Techniques ◽  
Ensemble Classifiers ◽  
Statistical Features ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing

Rolling element bearing health condition is monitored by analysing its vibration signature. Raw vibration signal picked up through suitably placed accelerometers is difficult to analyse hence many signal processing techniques have been proposed and developed by researchers to process the data for suitably extracting an effective signal feature set. Various machine learning techniques have been used for interpretation and accurate fault diagnosis using this extracted feature set. In this study “Empirical mode decomposition” is used for pre-processing the raw vibration data. Six “Statistical features” are extracted from the best Intrinsic mode function obtained through EMD and “Ensemble machine learning classifiers” are used for bearing fault diagnosis. A stacked ensemble of five classifiers is proposed for accurate fault diagnosis and results are compared with conventional ensemble classifiers to prove its effectiveness

Comparative study on optimum design of rolling element bearing

2015 ◽  
Vol 92 ◽  
pp. 595-604 ◽  
Author(s):  
S. Panda ◽  
S.N. Panda ◽  
P. Nanda ◽  
D. Mishra
Keyword(s):  
Comparative Study ◽  
Optimum Design ◽  
Rolling Element Bearing ◽  
Rolling Element ◽  
Element Bearing

Classification of rolling element bearing fault using singular value

2019 ◽  
Vol 26 (2) ◽  
pp. 181-197
Author(s):  
H.S. Kumar ◽  
P. Srinivasa Pai ◽  
Sriram N. S
Keyword(s):  
Singular Values ◽  
Rolling Element Bearing ◽  
Statistical Features ◽  
Test Rig ◽  
Vibration Signals ◽  
Content Type ◽  
Bearing Fault ◽  
Soft Computing Techniques ◽  
Rolling Element ◽  
Element Bearing

Purpose The purpose of this paper is to classify different conditions of the rolling element bearing (REB) using vibration signals acquired from a customized bearing test rig. Design/methodology/approach An effort has been made to develop health index (HI) based on singular values of the statistical features to classify different conditions of the REB. The vibration signals from the normal bearing (N), bearing with defect on ball (B), bearing with defect on inner race (IR) and bearing with defect on outer race (OR) have been acquired from a customized bearing test rig under variable load and speed conditions. These signals were subjected to “modified kurtosis hybrid thresholding rule” (MKHTR)-based denoising. The denoised signals were decomposed using discrete wavelet transform. A total of 17 statistical features have been extracted from the wavelet coefficients of the decomposed signal. Findings Singular values of the statistical features can be effectively used for REB classification. Practical implications REB are critical components of rotary machinery right across the industrial sectors. It is a well-known fact that critical bearing failures causes major breakdowns resulting in untold and most expensive downtimes that should be avoided at all costs. Hence, intelligently based bearing failure diagnosis and prognosis should be an integral part of the asset maintenance and management activity in any industry using rotary machines. Originality/value It is found that singular values of the statistical features exhibit a constant value and accordingly can be assigned to each type of bearing fault and can be used for fault characterization in practical applications. The effectiveness of this index has been established by applying this to data from Case Western Reserve University data base which is a standard bench mark data for this application. HIs minimizes the computation time when compared to fault diagnosis using soft computing techniques.

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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