Degradation assessment for the ball screw with variational autoencoder and kernel density estimation
The ball screw is an important component of machine tools, and its degradation assessment is therefore critical for the health management of the entire machine tool. Generally, the degradation assessment includes health indicator construction and degradation modeling. However, the health indicator i...
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Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1177/1687814018797261 |
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doaj-b0753987e6ab4396a36588dca2b4d5ef2020-11-25T03:43:56ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402018-09-011010.1177/1687814018797261Degradation assessment for the ball screw with variational autoencoder and kernel density estimationJuan WenHongli GaoThe ball screw is an important component of machine tools, and its degradation assessment is therefore critical for the health management of the entire machine tool. Generally, the degradation assessment includes health indicator construction and degradation modeling. However, the health indicator is often constructed manually with prior knowledge, and its sensitivity can be affected by various factors. In addition, most existing degradation models rely on a large amount of failure data, which is not practical for the ball screw due to its high reliability. To solve these problems, this article presents a novel ball screw performance evaluation method. First, the raw data collected in the normal status are used to train the variational autoencoder, and then, the online raw signals are input into the learned variational autoencoder to construct health indicators. After that, the kernel density estimation is utilized to estimate the probability distribution of health indicator points in a dynamic sliding window, and then, the deterioration can be evaluated by summarizing the probability distribution that exceeds a predefined threshold. Experimental results show that the presented methodology can establish the health indicator automatically and adaptively. Also, it can evaluate the ball screw performance effectively and quantitatively when only data in healthy state are available.https://doi.org/10.1177/1687814018797261 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Juan Wen Hongli Gao |
spellingShingle |
Juan Wen Hongli Gao Degradation assessment for the ball screw with variational autoencoder and kernel density estimation Advances in Mechanical Engineering |
author_facet |
Juan Wen Hongli Gao |
author_sort |
Juan Wen |
title |
Degradation assessment for the ball screw with variational autoencoder and kernel density estimation |
title_short |
Degradation assessment for the ball screw with variational autoencoder and kernel density estimation |
title_full |
Degradation assessment for the ball screw with variational autoencoder and kernel density estimation |
title_fullStr |
Degradation assessment for the ball screw with variational autoencoder and kernel density estimation |
title_full_unstemmed |
Degradation assessment for the ball screw with variational autoencoder and kernel density estimation |
title_sort |
degradation assessment for the ball screw with variational autoencoder and kernel density estimation |
publisher |
SAGE Publishing |
series |
Advances in Mechanical Engineering |
issn |
1687-8140 |
publishDate |
2018-09-01 |
description |
The ball screw is an important component of machine tools, and its degradation assessment is therefore critical for the health management of the entire machine tool. Generally, the degradation assessment includes health indicator construction and degradation modeling. However, the health indicator is often constructed manually with prior knowledge, and its sensitivity can be affected by various factors. In addition, most existing degradation models rely on a large amount of failure data, which is not practical for the ball screw due to its high reliability. To solve these problems, this article presents a novel ball screw performance evaluation method. First, the raw data collected in the normal status are used to train the variational autoencoder, and then, the online raw signals are input into the learned variational autoencoder to construct health indicators. After that, the kernel density estimation is utilized to estimate the probability distribution of health indicator points in a dynamic sliding window, and then, the deterioration can be evaluated by summarizing the probability distribution that exceeds a predefined threshold. Experimental results show that the presented methodology can establish the health indicator automatically and adaptively. Also, it can evaluate the ball screw performance effectively and quantitatively when only data in healthy state are available. |
url |
https://doi.org/10.1177/1687814018797261 |
work_keys_str_mv |
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