Non-intrusive detection and characterisation of bearing faults from the temporal features of vibration-Reference-Cited by-同舟云学术

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Non-intrusive detection and characterisation of bearing faults from the temporal features of vibration

Published:2018-06-18 Issue:sup1 Volume:18 Page:S132-S139
ISSN:1448-4846
Container-title:Australian Journal of Mechanical Engineering
language:en
Short-container-title:Australian Journal of Mechanical Engineering

Author:

Arun P.¹,Lincon S. Abraham²,Prabhakaran N.³

Affiliation:

1. Department of Electronics and Communication Engineering, St. Joseph’s College of Engineering and Technology, India

2. Department of Electronics & Instrumentation Engg, Annamalai University, Chennai, India

3. Department of Electrical and Electronics Engineering, St. Joseph’s College of Engineering and Technology, India

Funder

agency in the public, commercial, or not-for-profit sectors

Publisher

Informa UK Limited

Subject

Mechanical Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/14484846.2018.1483468

Reference20 articles.

1. Intelligent Identification of Bearing Faults Using Time Domain Features

2. Time-domain bearing condition monitoring in induction motors using instantaneous frequency of motor voltage

3. Different indexes for eccentricity faults diagnosis in three-phase squirrel-cage induction motors: A review

4. Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive FuzzyC-Means Clustering

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2. Bearing Fault Diagnostics Based on the Square of the Amplitude Gains Method;Applied Sciences;2023-02-08

3. The Bearing Faults Detection Methods for Electrical Machines—The State of the Art;Energies;2022-12-27

4. Wavelet-based features for prognosis of degradation in rolling element bearing with non-linear autoregressive neural network;Australian Journal of Mechanical Engineering;2019-06-24

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