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Digital twin enhanced fault prediction for the autoclave with insufficient data
Journal of Manufacturing Systems ( IF 12.1 ) Pub Date : 2021-06-30 , DOI: 10.1016/j.jmsy.2021.05.015
Yucheng Wang , Fei Tao , Meng Zhang , Lihui Wang , Ying Zuo

Since any faulty operations could directly affect the composite property, making early prognosis is particularly crucial for complex equipment. At present, data-driven approach has been typically used for fault prediction. However, for part of complex equipment, it is difficult to access reliable and sufficient data to train the fault prediction model. To address this issue, this paper takes autoclave as an example. A Digital Twin (DT) model containing multiple dimensions for the autoclave is firstly constructed and verified. Then the characteristics of autoclave under different conditions are analyzed and presented with specific parameters. The data in normal and faulty conditions are simulated by using the DT model. Both the simulated data and extracted historical data are applied to enhance fault prediction. A convolutional neural network for fault prediction will be trained with the generated data which matches the feature of the autoclave in faulty conditions. The effectiveness of the proposed method is verified through result analysis.



中文翻译:

数字孪生增强了数据不足的高压釜故障预测

由于任何错误的操作都会直接影响复合性能,因此对复杂设备进行早期预测尤为重要。目前,数据驱动的方法已被广泛用于故障预测。然而,对于部分复杂的设备,很难获得可靠、充足的数据来训练故障预测模型。针对这个问题,本文以高压釜为例。首先构建并验证了包含高压釜多个维度的数字孪生 (DT) 模型。然后分析了高压釜在不同条件下的特性,并给出了具体的参数。使用DT模型模拟正常和故障条件下的数据。模拟数据和提取的历史数据都用于增强故障预测。将使用生成的数据训练用于故障预测的卷积神经网络,该数据与故障条件下高压釜的特征相匹配。通过结果分析验证了所提方法的有效性。

更新日期:2021-07-01
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