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A Smart Sensor Node for the Internet-of-Elevators-Non-Invasive Condition and Fault Monitoring
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Information Science and Engineering.ORCID iD: 0000-0002-3054-6413
KTH, School of Electrical Engineering (EES).
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2017 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 17, no 16, p. 5198-5208Article in journal (Refereed) Published
Abstract [en]

The signal processing scheme of a smart sensor node for the Internet-of-Elevators is presented. The sensor node is a self-contained black box unit only requiring power to be supplied, which enables a cost efficient way to modernize existing elevator systems in terms of condition monitoring capabilities. The sensor node monitors the position of the elevator using an inertial navigation system in conjugation with a simultaneous localization and mapping framework. Features reflecting the elevator system's operation and health condition are calculated by evaluating the ride quality parameters defined by the ISO 18738-1 standards, the vibration versus frequency spectrum, and the vibration versus position spectrum. Abnormal stops are identified by detecting decelerations that deviate from the typical deceleration pattern of the elevator or when the stopping position of the elevator does not match the learned floor levels. Furthermore, the condition of the door system is monitored by tracking the magnetic field variations that the motion of the doors creates; the number of door openings and the time required for the doors to close are estimated. The capability and performance of the blacksignal processing scheme are illustrated through a series of experiments. The experiments show, inter alia, that using low-cost sensors similar to those in a smartphone, the position of the elevator car can, with 99.9% probability, be estimated with an error of less than 1 m for travels up to 43 s long. The experiments also indicate that small degradations in the doors' closing time can be detected from the magnetic field measurements.

Place, publisher, year, edition, pages
IEEE, 2017. Vol. 17, no 16, p. 5198-5208
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-211732DOI: 10.1109/JSEN.2017.2719630ISI: 000406310500022OAI: oai:DiVA.org:kth-211732DiVA, id: diva2:1133664
Note

QC 20170816

Available from: 2017-08-16 Created: 2017-08-16 Last updated: 2017-08-16Bibliographically approved

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Skog, Isaac

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ACCESS Linnaeus CentreAutomatic ControlInformation Science and EngineeringSchool of Electrical Engineering (EES)
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