基于深度学习的智能电机故障诊断方法  

Intelligent Motor Fault Diagnosis Method Based on Deep Learning

作　　者：辛光明 袁庆海 秦永 卢春雷 郭垒 XIN Guangming;YUAN Qinghai;QIN Yong;LU Chunlei;GUO Lei(Huayuan Jingtian Resource Development Co.,Ltd.,Jining Mining Group,Jining,Shandong 272200,China)

机构地区：[1]济宁矿业集团花园井田资源开发有限公司,山东济宁272200

出　　处：《自动化应用》2024年第10期125-127,共3页Automation Application

摘　　要：深入研究了一种基于长短期记忆网络(LSTM)的智能电机故障诊断方法。首先,详细探讨了LSTM模型的基本原理,并阐述了其在时间序列分析中的优势;其次,提出了一种基于LSTM的电机故障诊断方法,通过对电机振动信号进行深度学习建模实现对电机运行状态异常的精准捕捉;最后,使用CMAPSS数据集进行了广泛的模型测试,验证了该方法在电机故障诊断中的有效性和可靠性。结果表明,该方法在多类别故障情况下表现出良好的性能,为电机系统的智能化健康管理提供了一种先进且可行的解决方案。This paper delves into a deep learning-based intelligent motor fault diagnosis method,predominantly employing the long short-term memory(LSTM)model.Firstly,the fundamental principles of the LSTM model are thoroughly explored,elucidating its advantages in time series analysis.Secondly,a motor fault diagnosis method based on LSTM is proposed,involving deep learning modeling of motor vibration signals to accurately capture anomalous motor operating states.Extensive model testing is conducted using the CMAPSS dataset,validating the effectiveness and reliability of the proposed method in motor fault diagnosis.The research findings indicate that the method exhibits commendable performance in multi-category fault scenarios,providing an advanced and viable solution for intelligent health management of motor systems.

关 键 词：深度学习 长短期记忆网络 电机故障 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TM307[自动化与计算机技术—控制科学与工程]