自动扶梯运行状态监测原理验证样机测试数据集

本数据集采集来自深圳地铁实地数据，该数据集可为后续模式识别算法提供有效的数据支持，提高自动扶梯故障识别的准确率。产生方法为：将自动扶梯故障监测系统分别放置于深圳地铁机房之中，通过振动、噪音、温度、图像、编码器等传感器采集自动扶梯关键部位信号。在深圳地铁数据获取过程中，分别采集了驱动主机、扶手带、附加制动器、扶手带、梯级、梯级链等主要部件数据，数据量约80GB。

This dataset is derived from on-site field data collected at Shenzhen Metro. It can provide effective data support for subsequent pattern recognition algorithms, and enhance the accuracy of escalator fault identification. The data generation process is as follows: Escalator fault monitoring systems were deployed in the machine rooms of Shenzhen Metro stations, and signals from key components of escalators were collected via various sensors including vibration, noise, temperature, image, and encoder sensors. During the data acquisition at Shenzhen Metro, data from major escalator components such as main drive unit, handrail belt, auxiliary brake, handrail belt, step, and step chain were collected separately. The total data volume amounts to approximately 80 GB.

搜集汇总

背景与挑战

背景概述

该数据集采集自深圳地铁，包含自动扶梯关键部位的振动、噪音、温度等信号，数据量约80GB，旨在支持模式识别算法以提高故障识别准确率。数据集由中铁第四勘察设计院集团有限公司创建，属于国家重点研发计划项目的一部分。