Comparative analysis of grain size for bearing coating using variational mode decomposition of vibration signal

Abstract One of the prime factors behind variation in properties of the given material is the change in its grain size. In this work, any change in grain size of bearing coating selected as an indicator to alert deterioration in coated surface of the bearing. Here, an online method to differentiate the level of grain size for the bearing surface is presented using vibration signature analysis. Five ball bearings (Make: NBC, Model: 6205) were electroplated with industry specified coatings, Black oxide, Silver, Copper, Zinc phosphate (ZnP), Nickel. These coated bearings were installed in customised bearing test rig, one after another. Each coated bearing was made to run at five different speeds varying from 300 rpm to 1500 rpm and its vibration signatures were captured. The obtained vibration signals must be carrying unique characteristics corresponding to interaction of bearing balls with surfaces of different grain size thus distributed in general. The signals were decomposed into six modes using Variational mode decomposition (VMD) for band wise evaluation Further, to study distributed characteristics present in the signal in relevance to grain size, prominently used statistical parameters: Root Mean Square (RMS), crest factor, Variance, Skewness, Kurtosis, Shannon entropy and Log energy were selected. Then, all previously mentioned statistical parameters were calculated individually for newly generated frequency bands. Further, it has been noted that at 300 & 1500 rpm, RMS has shown considerable correlation to grain size variation. Additionally, it has been noted that these responded Intrinsic Mode Functions (IMFs) were not having high energy contents of the signal which indicates the miniscule presence of grain size signature in vibration signal in comparison to other rotational characteristics. On further analysis based on the chain indexing, RMS was found to be most relevant statistical parameter at low rpm as well as high rpm in reference to grain size.

Authors

Institutions

Publication Details

Journal
Engineering Research Express
Published
2024-12-01
DOI
https://doi.org/10.1088/2631-8695/ad9e83
Citations
2
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
0.66
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Comparative analysis of grain size for bearing coating using variational mode decomposition of vibration signal

Manpreet Singh, Rameshwar Cambow
2 citations
Engineering Research Express
Machine Fault Diagnosis Techniques
0.66
article

Comparative analysis of grain size for bearing coating using variational mode decomposition of vibration signal

Manpreet Singh, Rameshwar Cambow
article en
2 citations

Abstract

Abstract One of the prime factors behind variation in properties of the given material is the change in its grain size. In this work, any change in grain size of bearing coating selected as an indicator to alert deterioration in coated surface of the bearing. Here, an online method to differentiate the level of grain size for the bearing surface is presented using vibration signature analysis. Five ball bearings (Make: NBC, Model: 6205) were electroplated with industry specified coatings, Black oxide, Silver, Copper, Zinc phosphate (ZnP), Nickel. These coated bearings were installed in customised bearing test rig, one after another. Each coated bearing was made to run at five different speeds varying from 300 rpm to 1500 rpm and its vibration signatures were captured. The obtained vibration signals must be carrying unique characteristics corresponding to interaction of bearing balls with surfaces of different grain size thus distributed in general. The signals were decomposed into six modes using Variational mode decomposition (VMD) for band wise evaluation Further, to study distributed characteristics present in the signal in relevance to grain size, prominently used statistical parameters: Root Mean Square (RMS), crest factor, Variance, Skewness, Kurtosis, Shannon entropy and Log energy were selected. Then, all previously mentioned statistical parameters were calculated individually for newly generated frequency bands. Further, it has been noted that at 300 & 1500 rpm, RMS has shown considerable correlation to grain size variation. Additionally, it has been noted that these responded Intrinsic Mode Functions (IMFs) were not having high energy contents of the signal which indicates the miniscule presence of grain size signature in vibration signal in comparison to other rotational characteristics. On further analysis based on the chain indexing, RMS was found to be most relevant statistical parameter at low rpm as well as high rpm in reference to grain size.

Engineering Research ExpressVol. 6(4)
Lovely Professional University (IN)
Affordable and clean energy
Openalex Percentile: Top 28%
Machine Fault Diagnosis Techniques
0.66
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Comparative analysis of grain size for bearing coating using variational mode decomposition of vibration signal — Manpreet Singh, Rameshwar Cambow · Engineering Research Express (2024) | TGRS Research Map | TGRS