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基于数据驱动和本体建模的数控机床主轴故障诊断与推理
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2021年度浙江工业职业技术学院“专业学科一体化建设”科研项目(XKC202113011);安徽省教育厅重点项目(KJ2019A0803);安徽科技学院人才引进项目(DQYJ201902)


Diagnosis and Inference of Spindle Faults in CNC Machine Tools Based on Data-driven and Ontology Modeling
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    摘要:

    针对目前数控机床主轴系统故障诊断存在方法单一及智能化程度低的问题,提出基于数据驱动和本体建模的机床主轴故障诊断与推理方法。采用EMD对传感器采集的蕴含故障特征的原始信号进行数据处理与分析,提取原始统计特征,在此基础上,构建DBN-RF诊断模型实现深度特征自适应挖掘与故障模式识别。利用Protégé5.1工具结合领域知识构建机床主轴故障本体知识库,将DBN-RF诊断模型的故障辨识结果与本体知识库中的实例进行语义映射,实现故障知识推理,获得故障原因和故障解决策略。基于采集的不同工况下轴承故障数据验证了DBN-RF诊断模型的有效性,最高故障诊断平均准确率可达92.93%;构建实例验证了本体知识库的可重用性和推理功能;最后,设计开发了数控机床主轴健康管理服务系统,实现主轴系统状态实时感知和故障诊断与推理。

    Abstract:

    The machine tool spindle fault diagnosis and inference method was proposed based on data-driven and ontology modeling to address the problems of single method and low level of intelligence in current CNC machine tool spindle system fault diagnosis.EMD was used to process and analyze the raw signals containing fault features collected by sensors,the original statistical features were extracted,and based on this,a DBN-RF diagnostic model was constructed to achieve deep feature adaptive mining and fault pattern recognition.The Protégé5.1 tool was used and combined with domain knowledge to construct a machine tool spindle fault ontology knowledge base,the fault identification results of the DBN-RF diagnostic model were semantically mapped with instances in the ontology knowledge base to achieve fault knowledge inference,the fault causes and fault resolution strategies were obtained.The effectiveness of the DBN-RF diagnostic model was validated based on actual collected bearing fault data under different working conditions,with the highest average fault diagnosis accuracy reaching 92.93%.The reusability and inference function of ontology knowledge base was verified through the construction of an instance.Finally,a CNC machine tool spindle health management service system was designed and developed to achieve real-time perception of spindle system status and fault diagnosis and inference.

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徐丹丹,张帝.基于数据驱动和本体建模的数控机床主轴故障诊断与推理[J].机床与液压,2024,52(12):244-252.
XU Dandan, ZHANG Di. Diagnosis and Inference of Spindle Faults in CNC Machine Tools Based on Data-driven and Ontology Modeling[J]. Machine Tool & Hydraulics,2024,52(12):244-252

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  • 在线发布日期: 2024-07-05
  • 出版日期: 2024-06-28