Approach for a Holistic Predictive Maintenance Strategy by Incorporating a Digital Twin

Werner, A.; Zimmermann, Nikolas; Lentes, Joachim

doi:10.1016/j.promfg.2020.01.265

Cited by 60 publications

(22 citation statements)

References 1 publication

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“…How to reduce the dependence of the method on the amount of data requires a lot of research. For the future, integrate DT, physical model-based method, and virtual model-based method to establish an integrated learning prediction model to achieve complementary advantages, of course, this is a more complex modeling process, but [240,241] has also tried. It cannot be ignored that DT has shown a strong advantage in gear RUL forecast and performance degradation assessment, the dots of buds have already grown, and the moment it blooms, it will attract the world's attention.…”

Section: Discussionmentioning

confidence: 99%

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Liu

Dong

2021

Journal of Computing and Information Science in Engineering

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As a transmission component, the gear has been obtained widespread attention. The remaining useful life (RUL) prediction of gear is critical to the prognostics health management (PHM) of gear transmission systems. The digital twin (DT) provides support for gear RUL prediction with the advantages of rich health information data and accurate health indicators (HI). This paper reviews digital twin-driven RUL prediction methods for gear performance degradation, from the view of digital twin-driven physical model-based and virtual model-based prediction method. From the view of physical model-based one, it includes prediction model based on gear crack, gear fatigue, gear surface scratch, gear tooth breakage, and gear permanent deformation. From the view of digital twin-driven virtual model-based one, it includes non-deep learning methods, and deep learning methods. Non-deep learning methods include wiener process, gamma process, hidden Markov model (HMM), regression-based model, proportional hazard model. Deep learning methods include deep neural networks (DNN), deep belief networks (DBN), convolutional neural networks (CNN), and recurrent neural networks (RNN), etc. It mainly summarizes the performance degradation and life test of various models in gear, and evaluates the advantages and disadvantages of various methods. In addition, it encourages future works.

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Section: Discussionmentioning

confidence: 99%

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Liu

Dong

2021

Journal of Computing and Information Science in Engineering

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“…They discussed the requirements for building a digital twin model by suggesting a model-updating scheme based on parameter sensitivity analysis to enhance its adaptability. The outcome showed that the digital twin rotor model constructed could accurately diagnose and was adaptive to degradation analysis [ 73 ]. Likewise, Ritto et al (2021) built a DTT-based model for the damaged mechanical fan structure.…”

Section: Methodsmentioning

confidence: 99%

Application of Digital Twins and Metaverse in the Field of Fluid Machinery Pumps and Fans: A Review

Yang

et al. 2022

Sensors

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Digital twins technology (DTT) is an application framework with breakthrough rules. With the deep integration of the virtual information world and physical space, it becomes the basis for realizing intelligent machining production lines, which is of great significance to intelligent processing in industrial manufacturing. This review aims to study the application of DTT and the Metaverse in fluid machinery in the past 5 years by summarizing the application status of pumps and fans in fluid machinery from the perspective of DTT and the Metaverse through the collection, classification, and summary of relevant literature in the past 5 years. The research found that in addition to relatively mature applications in intelligent manufacturing, DTT and Metaverse technologies play a critical role in the development of new pump products and technologies and are widely used in numerical simulation and fault detection in fluid machinery for various pumps and other fields. Among fan-type fluid machinery, twin fans can comprehensively use technologies, such as perception, calculation, modeling, and deep learning, to provide efficient smart solutions for fan operation detection, power generation visualization, production monitoring, and operation monitoring. Still, there are some limitations. For example, real-time and accuracy cannot fully meet the requirements in the mechanical environment with high-precision requirements. However, there are also some solutions that have achieved good results. For instance, it is possible to achieve significant noise reduction and better aerodynamic performance of the axial fan by improving the sawtooth parameters of the fan and rearranging the sawtooth area. However, there are few application cases of the Metaverse in fluid machinery. The cases are limited to operating real equipment from a virtual environment and require the combination of virtual reality and DTT. The application effect still needs further verification.

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“…The simulation model learns from actual experiences and events in reality, which makes it no longer a simple theoretical calculation. This model reproduces the real behavior of a machine or individual components almost identically and is thus called "a digital twin" (Werner et al, 2019).…”

Section: Computer-integrated Manufacturingmentioning

confidence: 97%

Remanufacturing and Advanced Machining Processes for New Materials and Components

Gevorkyan¹,

Rucki²,

Nerubatskyi³

et al. 2022

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Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint.The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.

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Approach for a Holistic Predictive Maintenance Strategy by Incorporating a Digital Twin

Cited by 60 publications

References 1 publication

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Application of Digital Twins and Metaverse in the Field of Fluid Machinery Pumps and Fans: A Review

Remanufacturing and Advanced Machining Processes for New Materials and Components

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