Abstract
In this study, a fault detection and classification method using neural network-based acoustic measurement is proposed. In this method, a measured acoustic signal of the target equipment undergoes Fast Fourier transformation.
The magnitude, for a range of frequencies, is accumulated and normalized to train predefined neural network model. To validate the proposed method, an experimental setup for cooling fan is established. The faults of the device are classified into five categories. A series of experiments for the experimental setup are conducted to validate the performance of the fault detection and classification of the proposed method. An accuracy of up to 98.6% is obtained for the test data. Thus, the experimental results show the effectiveness of the proposed fault detection algorithm.
The magnitude, for a range of frequencies, is accumulated and normalized to train predefined neural network model. To validate the proposed method, an experimental setup for cooling fan is established. The faults of the device are classified into five categories. A series of experiments for the experimental setup are conducted to validate the performance of the fault detection and classification of the proposed method. An accuracy of up to 98.6% is obtained for the test data. Thus, the experimental results show the effectiveness of the proposed fault detection algorithm.
| Translated title of the contribution | A Neural Network based Fault Detection and Classification System Using Acoustic Measurement |
|---|---|
| Original language | Korean |
| Pages (from-to) | 210-215 |
| Number of pages | 6 |
| Journal | 한국생산제조학회지 |
| Volume | 29 |
| Issue number | 3 |
| DOIs | |
| State | Published - Jun 2020 |