基于CMAC神经网络的门式起重机故障识别和诊断的研究

Research on Fault Identification and Diagnosis of Gantry Cranes Based on CMAC Neural Network

门式起重机是港口码头、铁路集散中心等物流枢纽不可或缺的起重设备,其中电机和减速箱为其核心部件,一旦发生故障将严重影响作业效率和工程进度。为确保门式起重机稳定运行,提出一种基于CMAC神经网络的故障诊断系统。该系统首先通过数据预处理技术提取关键故障特征,然后利用CMAC神经网络进行故障模式识别与预测。实验结果表明,相较于传统故障诊断方法,该系统不仅准确性高,而且实时性强,为门式起重机的故障诊断提供了一种新的有效手段。

As an indispensable lifting equipment in logistics hubs such as port terminals and railway distribution centers,gantry cranes rely heavily on their motors and reducers.Failures in these core components can significantly impact operational efficiency and project progress.To ensure the stable operation of gantry cranes,this paper proposes a fault diagnosis system based on the CMAC neural network.The system first extracts key fault features through data preprocessing techniques and then utilizes the CMAC neural network for fault pattern recognition and prediction.Experimental results demonstrate that,compared to traditional fault diagnosis methods,the proposed system offers high accuracy and strong real-time performance,providing a new and effective means for gantry crane fault diagnosis.