Fault detection of elevator system using profile extraction and deep autoencoder feature extraction

Krishna Mohan Mishra, John-Eric Saxén, Jerker Björkqvist, Kalevi Huhtala

    Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

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    In this paper, we propose a new algorithm for data extraction from time series 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 rest of the healthy data are used for validation of the model to prove its efficacy in terms of avoiding false positives. We have achieved nearly 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 when 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.

    Original languageUndefined/Unknown
    Title of host publication33rd Annual European Simulation and Modelling Conference
    ISBN (Print)9789492859099
    Publication statusPublished - 2019
    MoE publication typeA4 Article in a conference publication
    EventEuropean Simulation and Modelling Conference - 33rd Annual European Simulation and Modelling Conference
    Duration: 28 Oct 201930 Oct 2019


    ConferenceEuropean Simulation and Modelling Conference

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