...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Manufacturing Processes >An improved fault diagnosis approach for FDM process with acoustic emission
【24h】

An improved fault diagnosis approach for FDM process with acoustic emission

机译：带有声发射的FDM过程的一种改进的故障诊断方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The reliability and performance of additive manufacturing (AM) machines affect the product quality and manufacturing cost. Developing effective health monitoring and prognostics methods is critical to AM productivity. Yet limited work is done on machine health monitoring. Recently, the application of acoustic emission sensor (AE) to the fault diagnosis of material extrusion or fused deposition modeling process was demonstrated. One challenge in real-time process monitoring is processing the large amount of data collected by high-fidelity sensors for diagnostics and prognostics. In this paper, the efficiency of machine state identification from AE data is significantly improved with reduced feature space dimension. In the proposed method, features extracted in both time and frequency domains are combined and then reduced with the linear discriminant analysis. An unsupervised density based clustering method is applied to classify and recognize different machine states of the extruder. Experimental results show that the proposed approach can effectively identify machine states of the extruder even within a much smaller feature space.
机译：增材制造（AM）机器的可靠性和性能会影响产品质量和制造成本。开发有效的健康监测和预测方法对于AM生产率至关重要。但是在机器运行状况监视方面所做的工作有限。最近，证明了声发射传感器（AE）在材料挤压或熔融沉积建模过程的故障诊断中的应用。实时过程监控中的一项挑战是处理高保真传感器收集的大量数据以进行诊断和预测。本文通过减少特征空间尺寸显着提高了从AE数据中机器状态识别的效率。在提出的方法中，将在时域和频域中提取的特征进行组合，然后通过线性判别分析进行简化。基于无监督的基于聚类的聚类方法被用于分类和识别挤出机的不同机器状态。实验结果表明，该方法即使在很小的特征空间内也可以有效地识别挤出机的机器状态。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号