Using Machine Learning Techniques to Estimate the Remaining Useful Life of a System with Different Types of Datasets
Document Type
Conference Proceeding
Publication Date
1-7-2021
Publication Title
Lecture Notes in Networks and Systems
Volume
182
First page number:
139
Last page number:
147
Abstract
© 2021, Springer Nature Switzerland AG. Intelligent Prognostics and Health Management (PHM) technology aims to estimate the Remaining Useful Life (RUL) of a subsystem or a component using data collected by sensors. The rise in the complexity of systems requires new models to capture the relationship between the sensors and the RUL. Novel deep Convolutional Neural Networks (CNN) have been proposed as an approach for estimating the RUL. However, large amount of data is needed to use Machine Learning (ML) techniques. We explore current ML methods being used with different types of datasets and provide a conclusion on deciding what learning method works best with unique datasets. We find that, for most systems, the ML method used highly depends on the dataset and can greatly decrease the cost and increase the reliability.
Keywords
Convolutional neural networks; Deep learning; Degradation data; Fault detection; Prognostics and health management
Disciplines
Electrical and Computer Engineering
Language
English
Repository Citation
Lemus, C.,
Latifi, S.
(2021).
Using Machine Learning Techniques to Estimate the Remaining Useful Life of a System with Different Types of Datasets.
Lecture Notes in Networks and Systems, 182
139-147.
http://dx.doi.org/10.1007/978-3-030-65796-3_13