Proposal of failure prediction method of factory equipment by vibration data with Recurrent Autoencoder

Proposal of failure prediction method of factory equipment by vibration data with Recurrent Autoencoder

In this paper, we propose a method to predict the failure of factory equipment by machine learning architectures using vibration data. We design the model so that we can predict robustly the failure of the equipment in advance. We use a Gaussian Mixture Model (GMM), a machine learning architecture,...

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Bibliographic Details
Main Authors: Shota ASAHI, Ayaka MATSUI, Satoshi TAMURA, Satoru HAYAMIZU, Ryosuke ISASHI, Akira FURUKAWA, Takayoshi NAITOU
Format: Article
Language:Japanese
Published: The Japan Society of Mechanical Engineers 2020-10-01
Series:Nihon Kikai Gakkai ronbunshu
Subjects:
anomaly detection
failure prediction
factory equipment
signal processing
vibration
machine learning
deep learning
autoencoder
recurrent neural network
gmm
Online Access:https://www.jstage.jst.go.jp/article/transjsme/86/891/86_20-00020/_pdf/-char/en

Internet

https://www.jstage.jst.go.jp/article/transjsme/86/891/86_20-00020/_pdf/-char/en

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