Profile extraction and deep autoencoder feature extraction for elevator fault detection
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 16th International Conference on Signal Processing and Multimedia Applications |
| Subtitle of host publication | SIGMAP 2019, 26-28 July, 2019, Prague, Czech Republic |
| Editors | Christian Callegari |
| Place of Publication | Prague, Czech Republic |
| Publisher | SCITEPRESS |
| Pages | 313-320 |
| Number of pages | 8 |
| Volume | 16 |
| Edition | 2019 |
| ISBN (Electronic) | 978-989-758-378-0 |
| ISBN (Print) | 978-989-758-378-0 |
| DOIs | |
| Publication status | Published - 28 Jul 2019 |
| Publication type | A4 Article in a conference publication |
| Event | INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND MULTIMEDIA APPLICATIONS - Duration: 1 Jan 1900 → … |
Conference
| Conference | INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND MULTIMEDIA APPLICATIONS |
|---|---|
| Period | 1/01/00 → … |
Abstract
In this paper, we propose a new algorithm for data extraction from time series signal data, and furthermore automatic calculation of highly informative deep features to be used in fault detection. In data extraction elevator start and stop events are extracted from sensor data, and a generic deep autoencoder model is also developed for automated feature extraction from the extracted profiles. After this, extracted deep features are classified with random forest algorithm for fault detection. Sensor data are labelled as healthy and faulty based on the maintenance actions recorded. The remaining healthy data are used for validation of the model to prove its efficacy in terms of avoiding false positives. We have achieved 100% accuracy in fault detection along with avoiding false positives based on new extracted deep features, which outperforms results using existing features. Existing features are also classified with random forest to compare results. Our developed algorithm provides better results due to the new deep features extracted from the dataset compared to existing features. This research will help various predictive maintenance systems to detect false alarms, which will in turn reduce unnecessary visits of service technicians to installation sites.