Empirical Study of Machine Learning for Intelligent Bearing Fault Diagnosis

Aug 3, 2026

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Bearing failures significantly degrade manufacturing system performance and reliability, while also creating safety risks and potential losses of life and property. Research shows bearing faults account for roughly 30–40% of failures in induction motors, making early fault identification essential for uninterrupted operation. Faults in inner race, outer race, ball, and cage components alter vibration signals, enabling detection by comparing normal and faulty patterns. Using a public benchmark dataset, the study evaluates how data preparation (dimension reduction and noise removal) and feature extraction affect machine-learning-based fault detection and classification, and discusses implications for early diagnosis and more efficient maintenance planning.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Abstract

Introduction

Condition monitoring and safety relevance

Model-based vs data-driven approaches

Role of publicly available datasets

Methodology Overview

Data preparation and noise eradication

Feature extraction for induction motor signals

Machine learning classifiers for fault detection and classification

Implications and Conclusions

Early fault diagnosis and reliability

Maintenance planning in manufacturing systems

Santa Clara University Scholar Commons | General Engineering | School of Engineering | | --- | --- | | 7-2023
Empirical Study of Machine Learning for Intelligent Bearing Fault Diagnosis
Armin Moghadam
Fatemeh Davoudi Kakhki
Santa Clara University, [fdavoudikakhki@scu.edu](fdavoudikakhki@scu.edu)
Follow this and additional works at: [https://scholarcommons.scu.edu/eng_grad](https://scholarcommons.scu.edu/eng_grad) | | Recommended Citation Moghadam, A., & Kakhki, F. D. (2023) . Empirical Study of Machine Learning for Intelligent Bearing Fault Diagnosis. Production Management and Process Control, 104(104) . [https://doi.org/10.54941/](https://doi.org/10.54941/)[ ](https://doi.org/10.54941/)ahfe1003049 This Conference Proceeding is brought to you for free and open access by the School of Engineering at Scholar Commons. It has been accepted for inclusion in General Engineering by an authorized administrator of Scholar Commons. For more information, please contact [rscroggin@scu.edu](rscroggin@scu.edu). Empirical Study of Machine Learning for Intelligent Bearing Fault Diagnosis Armin Moghadam 1 and Fatemeh Davoudi Kakhki 1,2 1 Department of Technology, San Jose State University, San Jose, CA 95192, USA 2 Machine Learning & Safety Analytics Lab, Department of Technology, San Jose State University, San Jose, CA 95192, USA ABSTRACT Bearing failure highly impacts performance and production of manufacturing systems, causes safety incidents, and results in casualties and property loss. According to the current literature, bearing faults cause 30-40% of all failures in induction motors. Therefore, identiﬁcation of bearing faults, at early stages, is crucial to ensure seamless and reliable operation of induction motors in industrial and manufacturing operations. Faults occur in four components of bearing: inner race, outer race, ball, and cage. Regardless of the component in which fault occurs, it causes changes in vibration signals. Therefore, comparing normal signals with faulty ones is helpful in detecting localized faults in bearings. We use a benchmark publicly-available data set to conduct this analysis. The main challenge in using publicly-available benchmark datasets for fault detection is lack of manual for instruction on analysis experiments on the original data, which leaves researchers with the challenge and opportunity of applying various analytical methods for achieving higher accuracy rates and useful models for fault detection. This study presents a machine learning-based fault detection and classiﬁcation scheme in induction motors to evaluate the signiﬁcance and effects of various data preparation and feature extraction methods on accuracy and reliability of fault detection outcomes. The data preparation stage includes discussion of efﬁcient data dimension reduction, and noise eradication, as well as feature extraction methods for induction motor signals. The main methodology is developing a variety of machine learning classiﬁers for detection and classiﬁcation of normal bearings versus faulty bearings. Finally, the implications of the methodology and results for early fault diagnosis and enhanced reliability, as well as maintenance planning efforts in manufacturing systems are discussed. This study introduces proper implementation of machine learning models to improve system performance with higher speed and reliability. Furthermore, the methodology and results contribute to planning and undertaking maintenance operation more efﬁciently. Therefore, the approach, methodology, and results will be beneﬁcial to both researchers and practitioners involved in manufacturing systems reliability analysis and optimization. Keywords: Machine learning, Monitoring and fault diagnosis, Bearing faults INTRODUCTION Condition monitoring of industrial systems is significant in enhancing safety, reliability, performance, and overall quality of industrial processes through providing insights on diag

FAQs

Why is early bearing fault diagnosis important for induction motors?

Bearing faults strongly impact performance and can lead to safety incidents and losses. Since bearing faults account for about 30–40% of failures in induction motors, early identification supports seamless and reliable operation.

How do bearing faults affect the signals used for diagnosis?

Faults in the inner race, outer race, ball, and cage cause changes in vibration signals. Comparing normal signals with faulty ones helps detect localized faults.

What factors does the study evaluate to improve fault detection accuracy?

The study investigates the effect of data preparation methods (dimension reduction and noise eradication) and feature extraction approaches, then applies various machine-learning classifiers to detect and classify normal versus faulty bearings.

Aug 3, 2026

Upload by Riley