Digital twin enhanced fault prediction for the autoclave with insufficient data

Abstract 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.

[1]  Wesley J. Cantwell,et al.  Autoclave cure simulation of composite structures applying implicit and explicit FE techniques , 2013 .

[2]  Michael Obersteiner,et al.  Model validation: A bibliometric analysis of the literature , 2019, Environ. Model. Softw..

[3]  K. L. Edwards.  A risk-based approach to manufacturing process control: use in autoclave moulded composite sandwich panels , 2005 .

[4]  Javam C. Machado,et al.  A Fault Detection Method for Hard Disk Drives Based on Mixture of Gaussians and Nonparametric Statistics , 2017, IEEE Transactions on Industrial Informatics.

[5]  Andrew Y. C. Nee,et al.  Digital twin driven prognostics and health management for complex equipment , 2018 .

[6]  Tobias A Weber,et al.  A fast method for the generation of boundary conditions for thermal autoclave simulation , 2016 .

[7]  Hongwei Liu,et al.  Discrete event-driven model predictive control for real-time work-in-process optimization in serial production systems , 2020 .

[8]  Fei Tao,et al.  A physical model and data-driven hybrid prediction method towards quality assurance for composite components , 2021 .

[9]  Wenguang Yang,et al.  Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine , 2017, J. Sensors.

[10]  Peter Butala,et al.  Knowledge elicitation for fault diagnostics in plastic injection moulding: A case for machine-to-machine communication , 2017 .

[11]  Guangzhong Dong,et al.  Data-Driven Battery Health Prognosis Using Adaptive Brownian Motion Model , 2020, IEEE Transactions on Industrial Informatics.

[12]  Zhibin Zhao,et al.  Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing , 2019, IEEE Transactions on Industrial Informatics.

[13]  Yulfian Aminanda,et al.  Spring-back simulation of unidirectional carbon/epoxy flat laminate composite manufactured through autoclave process , 2015 .

[14]  Nazlı Gülüm Mutlu,et al.  Assessment of occupational risks In Turkish manufacturing systems with data-driven models , 2019, Journal of Manufacturing Systems.

[15]  Fei Tao,et al.  Make more digital twins , 2019, Nature.

[16]  Robert X. Gao,et al.  Virtualization and deep recognition for system fault classification , 2017 .

[17]  Kurt Matyas,et al.  A procedural approach for realizing prescriptive maintenance planning in manufacturing industries , 2017 .

[18]  Zhihan Lv,et al.  Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics , 2017, IEEE Transactions on Industrial Informatics.

[19]  Abdul Samad Shibghatullah,et al.  Neural network prognostics model for industrial equipment maintenance , 2011, 2011 11th International Conference on Hybrid Intelligent Systems (HIS).

[20]  Lidia Auret,et al.  Fault diagnosis and economic performance evaluation for a simulated base metal leaching operation , 2018, Minerals Engineering.

[21]  Xin Lu,et al.  Fog Computing and Convolutional Neural Network Enabled Prognosis for Machining Process Optimization , 2019, Springer Series in Advanced Manufacturing.

[22]  Marc Sartor,et al.  Creation of helicopter dynamic systems digital twin using multibody simulations , 2019, CIRP Annals.

[23]  Arne Bilberg,et al.  Digital twin driven human–robot collaborative assembly , 2019, CIRP Annals.

[24]  John G. Breslin,et al.  Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case , 2020 .

[25]  Rainer Stark,et al.  Development and operation of Digital Twins for technical systems and services , 2019, CIRP Annals.

[26]  Meng Zhang,et al.  Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing , 2017, IEEE Access.