Fault Diagnosis for Reducers Based on a Digital Twin

Liu, Weimin; Han, Bin; Zheng, Aiyun; Zheng, Zhi

doi:10.3390/s24082575

Cited by 2 publications

(1 citation statement)

References 54 publications

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“…Furthermore, it shows the potential of DTs as a valuable tool for renewable energy, boosting efficiency and sustainability in floating offshore wind installations. In [23,24], they propose a DT-based method for fault diagnosis and prediction, aiming to compensate for the shortcomings of existing methods: the single fault type, low similarity, and poor visual effect of state monitoring. For this, a complete model is built that represents all the elements, processes, and businesses of the physical workshop using a testing platform to acquire real and simulated signals under various operating conditions.…”

Section: Related Workmentioning

confidence: 99%

Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes

Caiza,

Sanz

2024

Applied Sciences

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Digital twin (DT) technology provides a path for implementing cyber–physical systems (CPS) and developing smart manufacturing because they are essential tools for monitoring and controlling manufacturing processes. It is considered a vital technology in smart manufacturing and is being widely researched in academia and industry. Furthermore, the combination of DTs and immersive environments has shown great potential for integrating novel capabilities into the new generation of CPS. This research presents an architecture for implementing immersive digital twins under ISO 23247 in flexible manufacturing processes. The proposed system is based on the integration of DT technologies in conjunction with augmented reality (AR) and gesture tracking, and validation was performed in the sorting station of the MPS 500 to increase the interaction and flexibility between physical and virtual environments in real time, thus enhancing the capabilities of the DT. The methodology used for the design and implementation of the DT includes (1) general principles and requirements; (2) models with functional views based on domains and entities; (3) attributes of the observable manufacturing elements; and (4) protocols for the exchange of information between entities. The results show that the integration of these technologies improves the monitoring, control, and simulation capabilities of processes using 3D resources and immersive environments, achieving a higher level of interactivity. In addition, error detection tests were carried out, where a reduction of time was observed in the resolution of errors that may be caused by internal or external disturbances of the process, thus avoiding production delays.

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Section: Related Workmentioning

confidence: 99%

Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes

Caiza,

Sanz

2024

Applied Sciences

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Study on Few-Shot Fault Diagnosis Method for Marine Fuel Systems Based on DT-SViT-KNN

Li,

Qi,

Shi

et al. 2024

Sensors

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The fuel system serves as the core component of marine diesel engines, and timely and effective fault diagnosis is the prerequisite for the safe navigation of ships. To address the challenge of current data-driven fault-diagnosis-based methods, which have difficulty in feature extraction and low accuracy under small samples, this paper proposes a fault diagnosis method based on digital twin (DT), Siamese Vision Transformer (SViT), and K-Nearest Neighbor (KNN). Firstly, a diesel engine DT model is constructed by integrating the mathematical, mechanism, and three-dimensional physical models of the Medium-speed diesel engines of 6L21/31 Marine, completing the mapping from physical entity to virtual entity. Fault simulation calculations are performed using the DT model to obtain different types of fault data. Then, a feature extraction network combining Siamese networks with Vision Transformer (ViT) is proposed for the simulated samples. An improved KNN classifier based on the attention mechanism is added to the network to enhance the classification efficiency of the model. Meanwhile, a Weighted-Similarity loss function is designed using similarity labels and penalty coefficients, enhancing the model’s ability to discriminate between similar sample pairs. Finally, the proposed method is validated using a simulation dataset. Experimental results indicate that the proposed method achieves average accuracies of 97.22%, 98.21%, and 99.13% for training sets with 10, 20, and 30 samples per class, respectively, which can accurately classify the fault of marine fuel systems under small samples and has promising potential for applications.

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Fault Diagnosis for Reducers Based on a Digital Twin

Cited by 2 publications

References 54 publications

Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes

Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes

Study on Few-Shot Fault Diagnosis Method for Marine Fuel Systems Based on DT-SViT-KNN

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