Damage Detection and Identification on Elevator Systems Using Deep Learning Algorithms and Multibody Dynamics Models
Timely damage detection on a mechanical system can prevent the appearance of catastrophic damage in it, as well as allow for better scheduling of its maintenance and repair process. For this purpose, multiple signal analysis methods have been developed to help identify anomalies in a system, through...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/101 |
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| author | Josef Koutsoupakis Dimitrios Giagopoulos Panagiotis Seventekidis Georgios Karyofyllas Amalia Giannakoula |
| author_facet | Josef Koutsoupakis Dimitrios Giagopoulos Panagiotis Seventekidis Georgios Karyofyllas Amalia Giannakoula |
| author_sort | Josef Koutsoupakis |
| collection | DOAJ |
| description | Timely damage detection on a mechanical system can prevent the appearance of catastrophic damage in it, as well as allow for better scheduling of its maintenance and repair process. For this purpose, multiple signal analysis methods have been developed to help identify anomalies in a system, through quantities such as vibrations or deformations in its critical components. In most applications, however, these data may be scarce or inexistent, hindering the overall process. For this purpose, a novel approach for damage detection and identification on elevator systems is developed in this work, where vibration data obtained through physical measurements and high-fidelity multibody dynamics models are combined with deep learning algorithms. High-quality training data are first generated through multibody dynamics simulations and are then combined with healthy state vibration measurements to train an ensemble of autoencoders and convolutional neural networks for damage detection and classification. A dedicated data acquisition system is then developed and integrated with an elevator cabin, allowing for condition monitoring through this novel methodology. The results indicate that the developed framework can accurately identify damages in the system, hinting at its potential as a powerful structural health monitoring tool for such applications, where manual damage localization would otherwise be considerably time-consuming. |
| format | Article |
| id | doaj-art-692a2dfcef0b4c49bab016f9e510c12c |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-692a2dfcef0b4c49bab016f9e510c12c2025-01-10T13:20:53ZengMDPI AGSensors1424-82202024-12-0125110110.3390/s25010101Damage Detection and Identification on Elevator Systems Using Deep Learning Algorithms and Multibody Dynamics ModelsJosef Koutsoupakis0Dimitrios Giagopoulos1Panagiotis Seventekidis2Georgios Karyofyllas3Amalia Giannakoula4School of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceSchool of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceSchool of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceSchool of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceKLEEMANN Group, 61100 Kilkis, GreeceTimely damage detection on a mechanical system can prevent the appearance of catastrophic damage in it, as well as allow for better scheduling of its maintenance and repair process. For this purpose, multiple signal analysis methods have been developed to help identify anomalies in a system, through quantities such as vibrations or deformations in its critical components. In most applications, however, these data may be scarce or inexistent, hindering the overall process. For this purpose, a novel approach for damage detection and identification on elevator systems is developed in this work, where vibration data obtained through physical measurements and high-fidelity multibody dynamics models are combined with deep learning algorithms. High-quality training data are first generated through multibody dynamics simulations and are then combined with healthy state vibration measurements to train an ensemble of autoencoders and convolutional neural networks for damage detection and classification. A dedicated data acquisition system is then developed and integrated with an elevator cabin, allowing for condition monitoring through this novel methodology. The results indicate that the developed framework can accurately identify damages in the system, hinting at its potential as a powerful structural health monitoring tool for such applications, where manual damage localization would otherwise be considerably time-consuming.https://www.mdpi.com/1424-8220/25/1/101structural healthy monitoringdeep learningmultibody dynamicssignal analysisdata acquisition |
| spellingShingle | Josef Koutsoupakis Dimitrios Giagopoulos Panagiotis Seventekidis Georgios Karyofyllas Amalia Giannakoula Damage Detection and Identification on Elevator Systems Using Deep Learning Algorithms and Multibody Dynamics Models Sensors structural healthy monitoring deep learning multibody dynamics signal analysis data acquisition |
| title | Damage Detection and Identification on Elevator Systems Using Deep Learning Algorithms and Multibody Dynamics Models |
| title_full | Damage Detection and Identification on Elevator Systems Using Deep Learning Algorithms and Multibody Dynamics Models |
| title_fullStr | Damage Detection and Identification on Elevator Systems Using Deep Learning Algorithms and Multibody Dynamics Models |
| title_full_unstemmed | Damage Detection and Identification on Elevator Systems Using Deep Learning Algorithms and Multibody Dynamics Models |
| title_short | Damage Detection and Identification on Elevator Systems Using Deep Learning Algorithms and Multibody Dynamics Models |
| title_sort | damage detection and identification on elevator systems using deep learning algorithms and multibody dynamics models |
| topic | structural healthy monitoring deep learning multibody dynamics signal analysis data acquisition |
| url | https://www.mdpi.com/1424-8220/25/1/101 |
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