Name
Title | ||
|---|---|---|
Fault Detection and Severity Identification of Ball Bearings by Online Condition Monitoring |
Abstract | ||
|---|---|---|
This paper presents a fast, accurate, and simple systematic approach for online condition monitoring and severity identification of ball bearings. This approach utilizes compact one-dimensional (1-D) convolutional neural networks (CNNs) to identify, quantify, and localize bearing damage. The proposed approach is verified experimentally under several single and multiple damage scenarios. The experimental results demonstrated that the proposed approach can achieve a high level of accuracy for damage detection, localization, and quantification. Besides its real-time processing ability and superior robustness against the high-level noise presence, the compact and minimally trained 1-D CNNs in the core of the proposed approach can handle new damage scenarios with utmost accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TIE.2018.2886789 | IEEE Transactions on Industrial Electronics |
Keywords | Field | DocType |
Vibrations,Feature extraction,Two dimensional displays,Fault detection,Training,Fault diagnosis,Hidden Markov models | Ball bearing,Pattern recognition,Convolutional neural network,Fault detection and isolation,Control engineering,Feature extraction,Robustness (computer science),Bearing (mechanical),Artificial intelligence,Condition monitoring,Engineering,Hidden Markov model | Journal |
Volume | Issue | ISSN |
66 | 10 | 0278-0046 |
Citations | PageRank | References |
5 | 0.41 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Osama Abdeljaber | 1 | 12 | 1.54 |
| Sadok Sassi | 2 | 5 | 0.41 |
| Onur Avci | 3 | 5 | 0.41 |
| Serkan Kiranyaz | 4 | 750 | 61.15 |
| Abdelrahman Aly Ibrahim | 5 | 5 | 0.41 |
| Moncef Gabbouj | 6 | 3282 | 386.30 |