机电工程技术2025，Vol.54Issue(4)：171-176,6.DOI:10.3969/j.issn.1009-9492.2025.04.030
基于CNN-LSTM算法的电梯振动故障预测
Prediction of Elevator Vibration Fault Based on CNN-LSTM Algorithm
艾学忠 1张玉龙 1徐春博1
作者信息
- 1. 吉林化工学院信息与控制工程学院,吉林吉林 132022
- 折叠
摘要
Abstract
In order to overcome the shortcomings of the traditional elevator fault detection system in accuracy and dynamic range,a technical scheme combining a high-performance STM32 microcontroller and an MPU6050 accelerometer has been adopted at the hardware level,aiming at realizing high-precision acquisition of elevator vibration data.At the software level,the deep learning algorithm model of convolutional neural network-long short yerm memory network(CNN-LSTM)is adopted to conduct in-depth analysis of the collected elevator vibration data signals,effectively identify the key features related to elevator faults,and carry out accurate prediction analysis.Through the intelligent analysis system,the running state of the elevator can be monitored in real time,and the prediction results can be displayed intuitively on the upper computer interface.The results show that the system can fit the overall fluctuation trend of the elevator vibration signal,and the prediction result can reach 83%without excluding the external human interference,and the overall loss value of the prediction set is 0.000 6.The system can well adapt to the operating environment of the elevator,detect the operating state of the elevator in real time,and identify the potential fault information in advance.关键词
电梯振动信号/故障预测/CNN-LSTM算法Key words
elevator vibration signal/failure prediction/CNN-LSTM arithmetic分类
信息技术与安全科学引用本文复制引用
艾学忠,张玉龙,徐春博..基于CNN-LSTM算法的电梯振动故障预测[J].机电工程技术,2025,54(4):171-176,6.
