Using Machine Learning Techniques to Estimate the Remaining Useful Life of a System with Different Types of Datasets
Lecture Notes in Networks and Systems
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© 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.
Convolutional neural networks; Deep learning; Degradation data; Fault detection; Prognostics and health management
Electrical and Computer Engineering
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