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Condition monitoring of elevator systems using deep neural network

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


Original languageEnglish
Title of host publicationICORES 2020 - Proceedings of the 9th International Conference on Operations Research and Enterprise Systems
EditorsGreg H. Parlier, Federico Liberatore, Marc Demange
Number of pages7
ISBN (Electronic)978-989-758-396-4
Publication statusPublished - 24 Feb 2020
Publication typeA4 Article in a conference publication
EventInternational Conference on Operations Research and Enterprise Systems - Valletta, Malta
Duration: 22 Feb 202024 Feb 2020
Conference number: 9


ConferenceInternational Conference on Operations Research and Enterprise Systems
Internet address


In this research, we propose a generic deep autoencoder model for automatic calculation of highly informative deep features from the elevator 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. In our research, we have included all fault types present for each elevator. The rest of the healthy data is used for validation of the model to prove its efficacy in terms of avoiding false positives. New extracted deep features provide 100% accuracy in fault detection along with avoiding false positives, which is better than statistical features. Random forest was also used to detect faults based on statistical features to compare results. New deep features extracted from the dataset with deep autoencoder random forest outperform the statistical 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.

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