scholarly journals An integrated condition monitoring method for rolling element bearings based on perceptual vibration hashing and SOM

2021 ◽  
Vol 1207 (1) ◽  
pp. 012012
Author(s):  
Shiwei Yan ◽  
Haining Liu ◽  
Fajia Li ◽  
Huanyong Cui
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Rolling Element Bearings ◽  
Health Indicator ◽  
Self Organizing Maps ◽  
Monitoring Method ◽  
Vibration Data ◽  
Healthy State ◽  
Rolling Element ◽  
Som Network

Abstract Rolling element bearings are widely used in rotating machinery. Bearing faults will result in damage to property. So, the condition monitoring of bearings is of great significance, but few methods can achieve both degradation assessment and fault diagnosis. In this paper, an integrated condition monitoring method for rolling element bearings based on perceptual vibration hashing (PVH) and self-organizing maps (SOM) is proposed. Distance matric based on PVH is used as a health indicator for degradation assessment, in which the baseline of healthy state is selected based on the clustering centre of SOM instead of experience. When the value of health indicator exceeds the pre-set threshold, visualized fault diagnosis can also be achieved by training the SOM network. The effectiveness of the developed method is verified with the vibration data from accelerated degradation tests of rolling element bearings.

The Condition Monitoring of Rolling Element Bearings Using Vibration Analysis

1984 ◽  
Vol 106 (3) ◽  
pp. 447-453 ◽  
Author(s):  
J. Mathew ◽  
R. J. Alfredson
Keyword(s):  
Condition Monitoring ◽  
Sufficient Evidence ◽  
Rolling Element Bearings ◽  
Bearing Failure ◽  
Machine Condition ◽  
Vibration Data ◽  
Machine Condition Monitoring ◽  
Rolling Element ◽  
Vibration Signatures ◽  
Bearing Damage

A brief review on techniques of machine condition monitoring is presented followed by a description and results of a study involving the monitoring of vibration signatures of several rolling element bearings with a view to detecting incipient failure. The vibration data were analyzed and several parameters were assessed with regard to their effectiveness in the detection of bearing condition. It was found that all the parameters were of some value depending on the type of bearing failure encountered. Generally, frequency domain parameters were more consistent in the detection of damage than time domain parameters. However, sufficient evidence is produced to show that it would be unreliable to depend exclusively on any one technique to detect bearing damage.

Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission

2021 ◽  
pp. 107754632110161
Author(s):  
Aref Aasi ◽  
Ramtin Tabatabaei ◽  
Erfan Aasi ◽  
Seyed Mohammad Jafari
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Time Domain ◽  
Central Moment ◽  
Rolling Element Bearings ◽  
Test Rig ◽  
Acoustic Emission Sensor ◽  
Outer Race ◽  
Common Time ◽  
Rolling Element

Inspired by previous achievements, different time-domain features for diagnosis of rolling element bearings are investigated in this study. An experimental test rig is prepared for condition monitoring of angular contact bearing by using an acoustic emission sensor for this purpose. The acoustic emission signals are acquired from defective bearing, and the sensor takes signals from defects on the inner or outer race of the bearing. By studying the literature works, different domains of features are classified, and the most common time-domain features are selected for condition monitoring. The considered features are calculated for obtained signals with different loadings, speeds, and sizes of defects on the inner and outer race of the bearing. Our results indicate that the clearance, sixth central moment, impulse, kurtosis, and crest factors are appropriate features for diagnosis purposes. Moreover, our results show that the clearance factor for small defects and sixth central moment for large defects are promising for defect diagnosis on rolling element bearings.

A NEW GEARBOX FAULT DIAGNOSIS METHOD BASED ON LUCY–RICHARDSON DECONVOLUTION

2015 ◽  
Vol 39 (3) ◽  
pp. 593-603
Author(s):  
Xinghui Zhang ◽  
Jianshe Kang ◽  
Hongzhi Teng ◽  
Jianmin Zhao
Keyword(s):  
Fault Diagnosis ◽  
Experimental Studies ◽  
Experimental Tests ◽  
Rolling Element Bearings ◽  
Envelope Analysis ◽  
Rolling Element ◽  
Negative Effect ◽  
Diagnosis Method ◽  
Good Identification ◽  
Incipient Fault Diagnosis

Gear and bearing faults are the main causes of gearbox failure. Till now, incipient fault diagnosis of these two components has been a problem and needs further research. In this context, it is found that Lucy–Richardson deconvolution (LRD) proved to be an excellent tool to enhance fault diagnosis in rolling element bearings and gears. LRD’s good identification capabilities of fault frequencies are presented which outperform envelope analysis. This is very critical for early fault diagnosis. The case studies were carried out to evaluate the effectiveness of the proposed method. The results of simulated and experimental studies show that LRD is efficient in alleviating the negative effect of noise and transmission path. The results of simulation and experimental tests demonstrated outperformance of LRD compared to classical envelope analysis for fault diagnosis in rolling element bearings and gears, especially when it is applied to the processing of signals with strong background noise.

Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis

2013 ◽  
Vol 332 (8) ◽  
pp. 2081-2097 ◽  
Author(s):  
Feiyun Cong ◽  
Jin Chen ◽  
Guangming Dong ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Rolling Element Bearings ◽  
Vibration Model ◽  
Rolling Element ◽  
Rotor Bearing ◽  
Bearing System
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