Authors:
Krishna Mohan Mishra
and
Kalevi J. Huhtala
Affiliation:
Unit of Automation Technology and Mechanical Engineering, Tampere University, Tampere and Finland
Keyword(s):
Elevator System, Deep Autoencoder, Fault Detection, Feature Extraction, Random Forest.
Related
Ontology
Subjects/Areas/Topics:
Applications and Case-studies
;
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Communication and Software Technologies and Architectures
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Domain Analysis and Modeling
;
e-Business
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Knowledge Engineering
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Symbolic Systems
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 installa
tion sites by detecting false alarms in various predictive maintenance systems.
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