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ISSN:2164-6376 (print)
ISSN:2164-6414 (online)
Discontinuity, Nonlinearity, and Complexity

Dimitry Volchenkov (editor), Dumitru Baleanu (editor)

Dimitry Volchenkov(editor)

Mathematics & Statistics, Texas Tech University, 1108 Memorial Circle, Lubbock, TX 79409, USA

Email: dr.volchenkov@gmail.com

Dumitru Baleanu (editor)

Cankaya University, Ankara, Turkey; Institute of Space Sciences, Magurele-Bucharest, Romania

Email: dumitru.baleanu@gmail.com

Remaining Useful Life Prediction Method For Different Types Of Rolling Bearings Based On Bi-Lstm Quantification

Discontinuity, Nonlinearity, and Complexity 14(1) (2025) 197--214 | DOI:10.5890/DNC.2025.03.012

Kondhalkar Ganesh Eknath$^{1,2}$, G. Diwakar$^{3}$

$^{1}$ Research Scholar, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation (Deemed to be University), Green Fields, Vaddeswaram, Guntur District, A.P., INDIA. 522 302

$^{2}$ Assistant Professor, Anantrao Pawar College Of Engineering & Research, Parvati, Pune, Maharashtra, India- 411009

$^{3}$ Professor, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation (Deemed to be University), Green Fields, Vaddeswaram, Guntur District, A.P., India. 522 302

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Abstract

The remaining Useful Life (RUL) forecast for rolling bearings is still a crucial part of condition-based maintenance (CBM) for mechanical systems. To predict the RUL, the existing research utilized traditional Deep Learning techniques, however, it has trouble quantifying uncertainty. Therefore, this research suggested a novel deep learning (DL) model to improve RUL prediction. Initially, to define the degree of rolling bearing deterioration and comprehend the non-linear qualities, time domain features, frequency domain features, and time-frequency domain features are removed. Then, this study suggested using a Bi-LSTM - RF framework to predict the RUL, this framework has an LSTM layer in a combination of forward and backward motion, a fully connected layer, an RF classifier, and a dropout layer. As a result, our proposed deep learning-based RUL prediction obtains the Accuracy of 0.9845, Precision of 0.93, Recall of 1.0, and F1-score of 0.9656.

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