Automatic Fault Detection and Classification in Lift Door Systems Using Vibration Signal Features

Angel Torres Perez*, Stefan Kaczmarczyk, Rory Smith

*Corresponding author for this work

Research output: Contribution to Book/ReportChapterpeer-review

Abstract

The Internet of Things (IoT) is shaping the concept of the modern intelligent built environment. The latest developments in IoT have led to secure, energy efficient systems enabling low-cost real-time analytics. In the Vertical Transportation (VT) technologies developed by the lift industry real-time analytics are facilitating predictive maintenance which in turn decreases operational and downtime costs. Data driven predictive maintenance does not always reach an optimal performance because the quality and quantity of the data matters. Fault classification and the estimation of the remaining useful life (RUL) requires a deep understanding of failure modes and component degradation. In lift systems, most of the malfunctions are due to faults developed by the automatic power operated door systems. The most widespread Structural Health Monitoring (SHM) sensor technology used in monitoring the door mechanisms are acoustic and vibration sensors. In this paper, an automatic fault detection system using Artificial Neural Networks (ANN) is implemented using vibration signal features. Obtained results reveal that the fault classification performance is high (>70%) under low noise environmental conditions.
Original languageEnglish
Title of host publication10th European Workshop on Structural Health Monitoring, EWSHM 2020
Subtitle of host publication Lecture Notes in Civil Engineering
EditorsRizzo P, Milazzo A
PublisherSpringer International
Pages765-775
Number of pages11
Volume128
ISBN (Electronic)978-3-030-64908-1
ISBN (Print)978-3-030-64908-1, 978-3-030-64907-4
DOIs
Publication statusPublished - 3 Mar 2021

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