Kalman Filter for Predictive Maintenance and Anomaly Detection

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet


There are various strategies in optimization of anomaly detection problem on sensor data. This paper describes a Gaussian Mixture Model (GMM) and Kalman filter to detect outliers within the sensor data of wire bonding. With limitation on data samples and high dimensional parameters, Principal Component Analysis (PCA) helped to reduce dimensionality without losing important information. The Expectation-Maximization algorithm for estimating Gaussian distribution parameters of GMM provided us a clustering model to fit our data. A weighted distance from the cluster center in the employed GMM model is applied to tune noise variances on measurement errors. The proposed method is validated using real measurements in the context of a manufacturing system.

Titel22nd International Carpathian Control Conference, ICCC 2021
Anzahl der Seiten6
VerlagIEEE - Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)978-1-7281-8610-8
ISBN (elektronisch)978-1-7281-8609-2
PublikationsstatusErschienen - 31.05.2021
Veranstaltung22nd International Carpathian Control Conference, ICCC 2021 - Online, Virtual, Velke Karlovice, Tschechische Republik
Dauer: 31.05.202101.06.2021
Konferenznummer: 22

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