Classification of Bearing Fault Signals in Rotating Machinery Using Neural Networks
Guardado en:
| Publicado en: | Journal Europeen des Systemes Automatises vol. 58, no. 1 (Jan 2025), p. 89-97 |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
International Information and Engineering Technology Association (IIETA)
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3261046845 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1269-6935 | ||
| 022 | |a 2116-7087 | ||
| 024 | 7 | |a 10.18280/jesa.580110 |2 doi | |
| 035 | |a 3261046845 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
| 100 | 1 | |a Retz Mahima Devarapalli | |
| 245 | 1 | |a Classification of Bearing Fault Signals in Rotating Machinery Using Neural Networks | |
| 260 | |b International Information and Engineering Technology Association (IIETA) |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Rotating Machinery is a vital component in the manufacturing process. Its health conditions directly affect production, and any failure of the Machinery may reduce production and cause accidents. Condition-based monitoring detects faults in the early stages, which, in turn, reduces machine failures. Machine learning condition monitoring has made remarkable achievements in fault detection, but it requires various feature calculations and is a time-consuming process. Recently, deep learning-based models outperformed traditional machine learning techniques as they automatically identify features through the learning process. This paper proposes a deep-learning model to classify bearing faults, specifically a convolution Neural Network Model (CNN) and Convolution Invariant Neural Network (CINN). The bearing dataset from Case Western Reserve University (CWRU) is used for training and testing the proposed CNN and CINN Models. The performance of model is evaluated on different working conditions of the bearing faults with varying loads, demonstrating 99% and above accuracy. | |
| 653 | |a Predictive maintenance | ||
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Fault diagnosis | ||
| 653 | |a Bearings | ||
| 653 | |a Machinery | ||
| 653 | |a Sensors | ||
| 653 | |a Neural networks | ||
| 653 | |a Classification | ||
| 653 | |a Breakdowns | ||
| 653 | |a Vibration | ||
| 700 | 1 | |a Kontham, Raja Kumar | |
| 700 | 1 | |a Kontham, John David Christopher | |
| 773 | 0 | |t Journal Europeen des Systemes Automatises |g vol. 58, no. 1 (Jan 2025), p. 89-97 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3261046845/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3261046845/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |