Fault detection of elevator system using deep autoencoder feature extraction for acceleration signals
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 11th International Conference on Knowledge Engineering and Ontology Development |
| Subtitle of host publication | KEOD 2019, September 17 - 19, 2019 in Vienna, Austria |
| Editors | Joaquim Filipe, Jan Dietz, David Aveiro |
| Place of Publication | Portugal |
| Publisher | SCITEPRESS |
| Pages | 336-342 |
| Number of pages | 7 |
| Volume | 2 |
| Edition | 2019 |
| ISBN (Electronic) | 978-989-758-382-7 |
| DOIs | |
| Publication status | Published - 19 Sep 2019 |
| Publication type | A4 Article in a conference publication |
| Event | INTERNATIONAL CONFERENCE ON KNOWLEDGE ENGINEERING AND ONTOLOGY DEVELOPMENT - Duration: 1 Jan 1900 → … |
Publication series
| Name | International Conference on Knowledge Engineering and Ontology Development |
|---|---|
| Publisher | SCITEPRESS |
| ISSN (Print) | 2184-3228 |
| ISSN (Electronic) | 2184-3228 |
Conference
| Conference | INTERNATIONAL CONFERENCE ON KNOWLEDGE ENGINEERING AND ONTOLOGY DEVELOPMENT |
|---|---|
| Period | 1/01/00 → … |
Abstract
In this research, we propose a generic deep autoencoder model for automatic calculation of highly informative deep features from the elevator time series data. Random forest algorithm is used for fault detection based on extracted deep features. Maintenance actions recorded are used to label the sensor data into healthy or faulty. Avoiding false positives are performed with the rest of the healthy data in terms of validation of the model to prove its efficacy. New extracted deep features provide 100% accuracy in fault detection along with avoiding false positives, which is better than existing features. Random forest was also used to detect faults based on existing features to compare results. New deep features extracted from the dataset with deep autoencoder random forest outperform the existing features. Good classification and robustness against overfitting are key characteristics of our model. This research will help to reduce unnecessary visits of service technicians to installation sites by detecting false alarms in various predictive maintenance systems.