摘要
稀土熔盐电解过程中电解给料自动辅机组件之间工作关联大,故障复杂多样,使用单一故障诊断方法效果不理想。针对这一问题,通过分析给料自动辅机组件之间的工作关系,提出基于故障树和LSTM-SVM的粉体下料设备故障诊断方法。首先搭建多层故障树,分析故障模式,然后根据故障树数据提取重要度较高的故障模式,建立长短期记忆神经网络故障诊断模型,故障定位后根据故障树分析结果所定义的权重大小输出诊断结果,并使用SVM对非故障异常工作状态进行分级。测试结果表明该模型具有较高的故障识别准确率。
In the process of rare earth molten salt electrolysis,the working relationship between the automatic auxiliary components of electrolysis feeding is large,the faults are complex and diverse,and the effect of using a single fault diagnosis method is not ideal.To solve this problem,a fault diagnosis method for powder blanking equipment based on fault tree and LSTM-SVM was proposed by analy⁃zing the working relationship between the components of the feeding automatic auxiliary machine.A multi-layer fault tree was built,the fault modes were analyzed,then the fault modes with high importance were extracted according to the fault tree data,a long-term and short-term memory neural network fault diagnosis model was established,the diagnosis results were output according to the weight size defined by the fault tree analysis results after fault location,and SVM was used to grade the non-fault abnormal working state.The test results show that the model has a high accuracy of fault identification.
作者
程哲
罗奕
王腾飞
文渊
董学琴
CHENG Zhe;LUO Yi;WANG Tengfei;WEN Yuan;DONG Xueqin(College of Mechanical and Electrical Engineering,Guilin University of Electronic Science and Technology,Guilin Guangxi 541004,China)
出处
《机床与液压》
北大核心
2024年第1期217-224,共8页
Machine Tool & Hydraulics
基金
2021年中央引导地方科技发展专项资金项目(桂科计字[2021]195号)
桂林电子科技大学研究生创新基金资助项目(2021YCXS011)。
