Remaining Useful Life Prediction Using Machine Learning Algorithms

Madeira, Malcolm Andrew; Jacob, Teslin

doi:10.1007/978-981-16-8862-1_22

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…Elasha et al 12 proposed a machine learning technique that combines regression models and multi-layer artificial neural network models, the method takes vibration measurements as input and uses predictive algorithms to diagnose the fault types and predict the remaining useful life (RUL) of turbine gearboxes. Madeira et al 13 used multiple types of machine learning methods to predict the RUL of the mechanical system in order to find the best prediction model. This study evaluated various regression methods and classification methods and achieved good prediction results.…”

Section: Introductionmentioning

confidence: 99%

An ensemble deep autoencoders based on asymmetric structure for operational reliability assessment of bearings

Jin

Yang

et al. 2022

Advances in Mechanical Engineering

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At present, with the rapid growth of manufacturing and big data, reliability technology has gradually become a topical issue in the industrial field. Aiming at the operation reliability assessment of rolling bearings, this paper proposes a bearings operational reliability assessment using an ensemble deep autoencoder based on asymmetric structure. In this method, an ensemble deep autoencoder is used to adaptively learn degradation features from condition monitoring data, where the ensemble deep autoencoder adopts an asymmetric structure with different activation functions in the encoder and decoder. Then, the learned features are classified by correlation analysis, and the typical features in each category are selected. Finally, the operation reliability of rolling bearings is evaluated through the definition of reliability based on Mahalanobis distance. Through the example evaluation of rolling bearing operation reliability and comparison with other feature extraction methods, it can be concluded that this method has stronger feature extraction ability and can effectively show the trend of bearing degradation.

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Section: Introductionmentioning

confidence: 99%