Classification of Bearing Faults in an Induction Motor Using Statistical Analysis and ANN

This paper addresses a method for the classification and diagnosis of bearing fault. In this method a multivariable statistical analysis combined with ANN is used. The fault features are extracted using the statistical approach. The extracted features are used as inputs in neural network for the classification purpose. The results obtained prove that the developed method can reliably diagnose different condition of bearing including healthy condition.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-21
DOI
https://doi.org/10.5281/zenodo.17669699
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Classification of Bearing Faults in an Induction Motor Using Statistical Analysis and ANN

Zenodo (CERN European Organization for Nuclear Research)
Machine Fault Diagnosis Techniques
article

Classification of Bearing Faults in an Induction Motor Using Statistical Analysis and ANN

article en

Abstract

This paper addresses a method for the classification and diagnosis of bearing fault. In this method a multivariable statistical analysis combined with ANN is used. The fault features are extracted using the statistical approach. The extracted features are used as inputs in neural network for the classification purpose. The results obtained prove that the developed method can reliably diagnose different condition of bearing including healthy condition.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 41%
Machine Fault Diagnosis Techniques
DOIOpen Access PDFOpenAlex
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Classification of Bearing Faults in an Induction Motor Using Statistical Analysis and ANN · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS