<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Exploring effective logistics machinery and equipment fault diagnosis and prediction technology to achieve efficient and stable operation of logistics machinery and equipment. In this paper, starting from the logistics machinery and equipment fault diagnosis technology, we optimize the hyperparameters of the recurrent gate unit neural network by using the improved second-order oscillatory particle swarm algorithm and then construct the IPSO-GRU logistics machinery and equipment fault prediction model. The IPSO-GRU model is used to test the prediction effect of the hydraulic lift table and logistics hoist by using the historical data of the hydraulic lift table as training data. The prediction accuracy of the IPSO-GRU model was improved by 6% compared with BP neural network. From the prediction results of the logistic hoist, only 6 out of 250 data samples failed to achieve accurate prediction. This shows that the IPSO-GRU model can effectively achieve the prediction of logistics machinery and equipment fault diagnosis and also provides a proven method for predictive maintenance of logistics equipment.</p>
</abstract>

Exploring effective logistics machinery and equipment fault diagnosis and prediction technology to achieve efficient and stable operation of logistics machinery and equipment. In this paper, starting from the logistics machinery and equipment fault diagnosis technology, we optimize the hyperparameters of the recurrent gate unit neural network by using the improved second-order oscillatory particle swarm algorithm and then construct the IPSO-GRU logistics machinery and equipment fault prediction model. The IPSO-GRU model is used to test the prediction effect of the hydraulic lift table and logistics hoist by using the historical data of the hydraulic lift table as training data. The prediction accuracy of the IPSO-GRU model was improved by 6% compared with BP neural network. From the prediction results of the logistic hoist, only 6 out of 250 data samples failed to achieve accurate prediction. This shows that the IPSO-GRU model can effectively achieve the prediction of logistics machinery and equipment fault diagnosis and also provides a proven method for predictive maintenance of logistics equipment.

Prediction of mechanical equipment fault diagnosis based on IPSO-GRU deep learning algorithm

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploring effective logistics machinery and equipment fault diagnosis and prediction technology to achieve efficient and stable operation of logistics...

Prediction of mechanical equipment fault diagnosis based on IPSO-GRU deep learning algorithm

Data publikacji: 30 wrz 2023

Zakres stron: -

Otrzymano: 17 gru 2022

Przyjęty: 10 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00424

Słowa kluczowe
Logistics machinery and equipment, Fault diagnosis, Second-order oscillatory particle swarm, Cyclic gate unit, IPSO-GRU model

© 2023 Peng Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Prediction of mechanical equipment fault diagnosis based on IPSO-GRU deep learning algorithm

Data publikacji: 30 wrz 2023

Zakres stron: -

Otrzymano: 17 gru 2022

Przyjęty: 10 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00424

Słowa kluczoweLogistics machinery and equipment, Fault diagnosis, Second-order oscillatory particle swarm, Cyclic gate unit, IPSO-GRU model

© 2023 Peng Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Logistics machinery and equipment, Fault diagnosis, Second-order oscillatory particle swarm, Cyclic gate unit, IPSO-GRU model