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首页> 外文期刊>International Journal of Performability Engineering >Intelligent Fault Diagnosis of Delta 3D Printers using Attitude Sensors based on Extreme Learning Machines
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Intelligent Fault Diagnosis of Delta 3D Printers using Attitude Sensors based on Extreme Learning Machines

机译：基于极端学习机的姿态传感器智能故障诊断Delta 3D打印机

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摘要

The influence of intelligent fault diagnosis on industrial development is becoming more and more important. In order to study the fault diagnosis technique of delta 3D printers using extreme learning machine (ELM), a low-cost attitude sensor was used in our designed machine. In the research process, the cross validation method was used to train ELM to obtain the optimal model. Through the analysis of different activation functions, we found that the correct recognition rates corresponding to the same activation function are different, and there are great differences among training samples and fault categories. The sin function, mexihat function, and tribas function recognition effects were better. The analysis of different activation functions revealed that the correct recognition rates corresponding to the same activation function are different, and there are great differences in different training samples and fault categories.
机译：智能故障诊断对工业发展的影响变得越来越重要。 为采用极端学习机（ELM）研究Delta 3D打印机的故障诊断技术，我们设计的机器中使用了低成本的姿态传感器。 在研究过程中，交叉验证方法用于训练ELM获得最佳模型。 通过对不同激活功能的分析，我们发现与相同的激活功能相对应的正确识别率不同，训练样本和故障类别之间存在很大差异。 SIN函数，MEXIHAT功能和培训函数识别效果更好。 不同激活函数的分析表明，对应于相同的激活功能的正确识别率是不同的，不同的训练样本和故障类别存在很大差异。

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