机电工程技术2024，Vol.53Issue(11)：220-223,4.DOI:10.3969/j.issn.1009-9492.2024.11.047
嵌入式传感器结合机器学习的振动模式分类方法
Vibration Pattern Classification Method Combining Embedded Sensors with Machine Learning
李佳俊 1钱嵩橙 2李林行3
作者信息
- 1. 电子科技大学自动化工程学院,成都 611731
- 2. 电子科技大学成都学院行知学院,成都 611731
- 3. 四川轻化工大学计算机科学与工程学院,四川 宜宾 644000
- 折叠
摘要
Abstract
A method for intelligent vibration data analysis is developed by integrating an STM32L432KC microcontroller and LIS3DH accelerometer to diagnose mechanical equipment failures.By optimizing signal processing through frequency domain filtering and employing a random forest algorithm for fault mode recognition and classification,the method significantly enhances the accuracy and efficiency of vibration mode detection.Experimental results confirm the method's effectiveness in distinguishing different vibration modes,demonstrating a high recognition accuracy rate.The discovery underscores the superiority of combining embedded systems with machine learning algorithms in abnormal vibration detection,significantly impacting industrial safety incident prevention and maintenance cost reduction.Additionally,the study explores technical details of vibration signal processing and mode classification,including signal acquisition,preprocessing,feature extraction,and the selection and optimization of classification algorithms,aiming to provide an efficient and accurate diagnostic tool for mechanical fault diagnosis.The findings not only offer a new technical approach for mechanical fault diagnostics but also pave new avenues for future research on embedded systems in industrial applications.关键词
嵌入式/模式识别/机器学习/随机森林算法/故障诊断Key words
embedded systems/pattern recognition/machine learning/random forest algorithm/fault diagnosis分类
信息技术与安全科学引用本文复制引用
李佳俊,钱嵩橙,李林行..嵌入式传感器结合机器学习的振动模式分类方法[J].机电工程技术,2024,53(11):220-223,4.
