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Prediction of Remaining useful life of Rolling Bearing using Hybrid DCNN-BiGRU Model

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Journal of Vibration Engineering & Technologies Aims and scope Submit manuscript

Abstract

Background

Rolling bearings are an essential equipment component, and evaluating its remaining useful life (RUL) is vital in guaranteeing safety and maintenance decision-making. While prior researchers utilize the D-CNN architecture to resolve the inadequacies of roller bearing remaining useful life prediction strategies, the extraction approach of operational deterioration attributes has a significant influence on the accuracy of the data-driven RUL prediction system.

Purpose

Unfortunately, the present network has many constraints when merging data into the model.

Method

The current research proposed a novel prediction method that combines a Deep Convolutional Neural Network (DCNN) and a gated recurrent unit (GRU), in which feature extraction utilizes a DCNN to extract vibration signal characteristics, where the fully connected layer substituted with the max-pooling layer to increase extraction accuracy. Simultaneously, the Gated recurrent unit (BiGRU) has been utilized to anticipate the remaining useful longevity of a rolling bearing. Moreover, this research builds up a bidirectional recurrent layer that can concentrate on past data while embedding future information to enhance the GRU model's potential to assimilate data.

Results

The experimental results show that the proposed technique reduces the time by incorporating DCNN with Bidirectional GRU compared with the baseline Simple Recurrent Unit (SRU) Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) takes more time than our proposed technique to complete. The error indexes of models, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), are compared with existing models. The results show that the proposed hybrid model gives less error value to improve the prediction results.

Conclusion

The simulation results show that the DCNN-BiGRU methodology has been established to lower manual intervention and time expenses while also providing an intelligent method of estimating the remaining useable life of roller bearings, founded on the idea of ensuring prediction accuracy.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation (KL - Deemed to be University), Green Fields, Vaddeswaram, Guntur, AP, 522 302, India

    Kondhalkar Ganesh Eknath & G. Diwakar

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  1. Kondhalkar Ganesh Eknath
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  2. G. Diwakar
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Correspondence to Kondhalkar Ganesh Eknath.

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Eknath, K.G., Diwakar, G. Prediction of Remaining useful life of Rolling Bearing using Hybrid DCNN-BiGRU Model. J. Vib. Eng. Technol. 11, 997–1010 (2023). https://doi.org/10.1007/s42417-022-00620-x

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  • DOI: https://doi.org/10.1007/s42417-022-00620-x

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