Koutsoupakis Josef, Giagopoulos Dimitrios, Seventekidis Panagiotis, Karyofyllas Georgios, Giannakoula Amalia
School of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
KLEEMANN Group, 61100 Kilkis, Greece.
Sensors (Basel). 2024 Dec 27;25(1):101. doi: 10.3390/s25010101.
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.
对机械系统进行及时的损伤检测可以防止其出现灾难性损伤，还能更好地安排其维护和修理流程。为此，已经开发了多种信号分析方法，通过诸如关键部件的振动或变形等参数来帮助识别系统中的异常情况。然而，在大多数应用中，这些数据可能很少或根本不存在，这阻碍了整个流程。为此，本文开发了一种用于电梯系统损伤检测与识别的新方法，该方法将通过物理测量获得的振动数据和高保真多体动力学模型与深度学习算法相结合。首先通过多体动力学模拟生成高质量的训练数据，然后将其与健康状态下的振动测量数据相结合，以训练用于损伤检测和分类的自动编码器和卷积神经网络集合。随后开发了一个专用数据采集系统，并将其与电梯轿厢集成，从而能够通过这种新方法进行状态监测。结果表明，所开发的框架能够准确识别系统中的损伤，这表明它有可能成为此类应用中强大的结构健康监测工具，否则手动进行损伤定位会相当耗时。
