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2025 | OriginalPaper | Buchkapitel

Bees Algorithm for Hyperparameter Search with Deep Learning to Estimate the Remaining Useful Life of Ball Bearings

verfasst von : Anurakt Kumar, Satyam Kumar, Neha Gupta, Nathinee Theinnoi, D. T. Pham

Erschienen in: Intelligent Engineering Optimisation with the Bees Algorithm

Verlag: Springer Nature Switzerland

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Abstract

Hyperparameter searching is one of the significant challenges in training deep learning models. To solve this challenge, the Bees Algorithm (BA), which simulates the foraging behaviour of honey bees, is used for hyperparameter searching and finding the best set of hyperparameters for a given deep learning model. This study applies a two-parameter version of the Bees Algorithm (BA₂) to search for the best set of hyperparameters for a Convolutional Neural Network (CNN) combined with a Long Short-Term Memory (LSTM) model. Then, the model is used to predict the remaining useful life (RUL) of ball bearings. BA₂ uses the traplining foraging technique of bees to integrate explorative and exploitative search mechanisms, reduce the number of parameters to only two and improve the remaining useful life (RUL) prediction. The algorithm can find a set of hyperparameters that makes the deep learning model perform better than the IEEE PHM 2012 Prognostic challenge winner by 38.97%.

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Vorheriges Kapitel The Bees Algorithm for Robotics-Enabled Collaborative Manufacturing

Nächstes Kapitel Bees Local Phase Quantisation Feature Selection for RGB-D Facial Expression Recognition

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Titel: Bees Algorithm for Hyperparameter Search with Deep Learning to Estimate the Remaining Useful Life of Ball Bearings
verfasst von: Anurakt Kumar
Satyam Kumar
Neha Gupta
Nathinee Theinnoi
D. T. Pham
Verlag: Springer Nature Switzerland
Buch: Intelligent Engineering Optimisation with the Bees Algorithm
Print ISBN: 978-3-031-64935-6

Electronic ISBN: 978-3-031-64936-3

Copyright-Jahr: 2025
DOI: https://doi.org/10.1007/978-3-031-64936-3_11

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