目的 针对包装机深沟球轴承在高速启停、高粉尘湿环境下的早期故障难检测问题,研究多模态特征融合与智能算法优化的故障诊断方法,以提升设备的运维效率。方法 鉴于传统深度置信网络用于状态监测与故障诊断时存在结构复杂、训练困难及参数无法自适应选取等问题,通过构建“小波包变换（WPT）+麻雀搜索算法（SSA）+深度置信网络（DBN）”多模态智能诊断模型,利用WPT提取时域、频域及能量特征,降低输入维度;引入SSA自适应优化DBN的网络层数、隐含层节点数及学习率,解决传统DBN参数依赖经验的问题,实现参数自适应选取,从而更准确、快速地识别轴承故障状态。结果 多模态智能模型对包装机深沟球轴承正常状态诊断准确率达到100%,不同程度的内圈、外圈、滚动体故障平均诊断准确率分别提升至98.58%、97.75%、98.42%,训练时间缩短约1 min。结论 通过优化模型可有效解决包装机深沟球轴承在复杂工况下的诊断难题,为包装机预知性维护提供了智能诊断方案。
Abstract
To solve the problem of difficult early fault detection of deep groove ball bearings in packaging machines under high-speed start/stop and high dust and humidity environments, the work aims to study a fault diagnosis method based on multimodal feature fusion and intelligent algorithm optimization to improve equipment operation and maintenance efficiency. Considering the problems of complex structure, difficult training, and inability to adaptively select parameters in traditional deep belief networks for state monitoring and fault diagnosis, a multimodal intelligent diagnostic model was constructed by combining wavelet packet transform (WPT), sparrow search algorithm (SSA), and deep belief network (DBN). WPT was used to extract time-domain, frequency-domain, and energy features, reducing input dimensions. Introducing SSA adaptive optimization of the network layers, hidden layer nodes, and learning rate of DBN to solve the problem of traditional DBN parameter dependence on experience achieved adaptive selection of parameters, more accurately and quickly identifying bearing fault states. The multimodal intelligent model had a diagnostic accuracy of 100% for the normal state of deep groove ball bearings in packaging machines. The average diagnostic accuracy of different degrees of inner ring, outer ring, and rolling element faults was improved to 98.58%, 97.75%, and 98.42%, respectively, and the training time was shortened by about one minute. The optimized model can effectively solve the diagnostic problem of deep groove ball bearings in packaging machines under complex working conditions, providing an intelligent diagnostic solution for predictive maintenance of packaging machines.
关键词
包装机 /
深沟球轴承 /
小波包变换 /
麻雀搜索算法
Key words
packaging machine /
deep groove ball bearings /
wavelet packet transform /
sparrow search algorithm
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基金
陕西省教育厅科研项目（24JK0340）; 咸阳市科技计划（L2024-ZDYF-ZDYF-GY-0043）
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