Condition Monitoring of Industrial Elevators Based on Machine Learning Models
Authors:
- 12 September 2023
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-5
Abstract
With the increasing demands for safe, robust and high efficient systems, the performance indicators should be quantified over the system’s life-cycle. Condition monitoring drastically reduce the maintenance costs, prevents unscheduled working interruption of the system, keeps productivity performance and the system in safe operating mode. Artificial intelligence and machine learning models are able to process big data sets, harness the data and predict the failures. This paper presents a method to monitor the states conditions and to identify the faults that may appear during the operation of an industrial elevator by developing a Long Short-Term Memory model.Keywords
This publication has 6 references indexed in Scilit:
- Comparing repetitive control strategies in lift applications2022
- Research on elevator intelligent monitoring and grading warning system2021
- Smart Multisensor Node for Remote Elevator Condition Monitoring2020
- Remote Condition Monitoring of Elevator's Vibration and Acoustics Parameters for Optimised Maintenance Using IoT Technology2018
- Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic TechniquesIEEE Industrial Electronics Magazine, 2014
- The necessity of lift inspections in Greece2012