Name
Title | ||
|---|---|---|
Automatic Extraction of a Health Indicator from Vibrational Data by Sparse Autoencoders |
Abstract | ||
|---|---|---|
We present a method for automatically extracting a health indicator of an industrial component from a set of signals measured during operation. Differently from traditional feature extraction and selection methods, which are labor-intensive and based on expert knowledge, the method proposed is automatic and completely unsupervised. Run-to-failure data collected during the component life are fed to a Sparse AutoEncoder (SAE), and the various features extracted from the hidden layer are evaluated to identify those providing the most accurate quantification of the component degradation. The method is applied to a synthetic and a bearing vibration dataset. The results show that the developed SAE-based method is able to automatically extract an efficient health indicator. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICSRS.2018.8688720 | 2018 3rd International Conference on System Reliability and Safety (ICSRS) |
Keywords | DocType | ISBN |
feature extraction,health indicator,sparse autoencoder,deep learning,vibration data | Conference | 978-1-7281-0238-2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhe Yang | 1 | 4 | 1.45 |
| Piero Baraldi | 2 | 236 | 21.96 |
| Enrico Zio | 3 | 77 | 7.43 |