Fault Detection of Elevator Systems Using Deep Autoencoder Feature Extraction

被引:3
作者
Mishra, Krishna Mohan [1 ]
Krogerus, Tomi R. [1 ]
Huhtala, Kalevi J. [1 ]
机构
[1] Tampere Univ, Unit Automat Technol & Mech Engn, Tampere, Finland
来源
2019 13TH INTERNATIONAL CONFERENCE ON RESEARCH CHALLENGES IN INFORMATION SCIENCE (RCIS) | 2019年
关键词
Elevator System; Deep Autoencoder; Fault Detection; Feature Extraction; Random Forest; DIAGNOSIS;
D O I
10.1109/rcis.2019.8876984
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this research, we propose a generic deep autoencoder model for automated feature extraction from the raw sensor data. 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 is 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 outperform the results using existing features. Existing features are also classified with random forest to compare results. Deep autoencoder random forest provides better results due to the new deep features extracted from the dataset when compared to existing features. Our model provides good classification and is robust against overfitting characteristics. This research will help various predictive maintenance systems to detect false alarms, which will reduce unnecessary visits of service technicians to installation sites.
引用
收藏
页码:69 / 74
页数:6
相关论文
共 36 条
[1]  
Albuquerque A., 2018, 2018 International Joint Conference on Neural Networks (IJCNN), P1
[2]  
[Anonymous], P ESANN 2018
[3]  
[Anonymous], 1987, AUTOMATA NETWORKS CO
[4]  
[Anonymous], 2011, LIFT REPORT
[5]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[6]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[7]  
Bulla J., 2018 INT JOINT C NEU, P1
[8]  
Ciabattoni L, 2015, IEEE INTL CONF IND I, P771, DOI 10.1109/INDIN.2015.7281834
[9]  
Desa U., 2014, World urbanization prospects, the 2011 revision
[10]  
Ebeling T., 2016, P ELEVCON MADR SPAIN