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...

Full description

Bibliographic Details
Main Authors: Juan Wen, Hongli Gao
Format: Article
Language:English
Published: SAGE Publishing 2018-09-01
Series:Advances in Mechanical Engineering
Online Access:https://doi.org/10.1177/1687814018797261
id doaj-b0753987e6ab4396a36588dca2b4d5ef
record_format Article
spelling 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 AT juanwen degradationassessmentfortheballscrewwithvariationalautoencoderandkerneldensityestimation
AT hongligao degradationassessmentfortheballscrewwithvariationalautoencoderandkerneldensityestimation
_version_ 1724517345430339584