Authors:
Krishna Mohan Mishra
;
Tomi R. Krogerus
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, Profile Extraction.
Related
Ontology
Subjects/Areas/Topics:
Multidimensional Signal Processing
;
Multimedia
;
Multimedia Signal Processing
;
Neural Networks, Spiking Systems, Genetic Algorithms and Fuzzy Logic
;
Telecommunications
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 be
tter 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.
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