Publications Repository - Gdańsk University of Technology

Page settings

polski
Publications Repository
Gdańsk University of Technology

Treść strony

Condition-Based Monitoring of DC Motors Performed with Autoencoders

This paper describes a condition-based monitoring system estimating DC motor degradation with the use of an autoencoder. Two methods of training the autoencoder are evaluated, namely backpropagation and extreme learning machines. The root mean square (RMS) error in the reconstruction of successive fragments of the measured DC motor angular-frequency signal, which is fed to the input of autoencoder, is used to determine the health indicator (HI). A complete test bench is built using a Raspberry Pi system (i.e., motor driver controlling angular frequency) and Jetson Nano (i.e., embedded compute node to estimate motor degradation) to perform exploratory analysis of autoencoders for condition-based monitoring and comparison of several classical artificial intelligence algorithms. The experiments include detection of degradation of DC motor working in both constant and variable work points. Results indicate that the HI obtained with the autoencoders trained with the use of either training method is suitable for both work points. Next, an experiment with multiple autoencoders trained on each specific work point and running in parallel is reviewed. It is shown that, in this case, the minimum value of RMS error among all autoencoders should be taken as HI. Furthermore, it has been shown that there is a nearlinear relationship between HI and the difference between measured and reconstructed angular-frequency waveforms.

Authors

Additional information

DOI
Digital Object Identifier link open in new tab 10.1007/978-3-031-16159-9_15
Category
Publikacja monograficzna
Type
rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
Language
angielski
Publication year
2022

Source: MOSTWiedzy.pl - publication "Condition-Based Monitoring of DC Motors Performed with Autoencoders" link open in new tab

Portal MOST Wiedzy link open in new tab