Kalman Filter for Predictive Maintenance and Anomaly Detection
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Authors
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.
Original language | English |
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Title of host publication | 22nd International Carpathian Control Conference, ICCC 2021 |
Number of pages | 6 |
Place of Publication | Piscataway |
Publisher | IEEE - Institute of Electrical and Electronics Engineers Inc. |
Publication date | 31.05.2021 |
Article number | 9454654 |
ISBN (Print) | 978-1-7281-8610-8 |
ISBN (Electronic) | 978-1-7281-8609-2 |
DOIs | |
Publication status | Published - 31.05.2021 |
Event | 22nd International Carpathian Control Conference, ICCC 2021 - Online, Virtual, Velke Karlovice, Czech Republic Duration: 31.05.2021 → 01.06.2021 Conference number: 22 http://www.icc-conf.cz/conference/iccc2021/ |
- Error Detection, Gaussian Mixture Model, Kalman Filter, Manufacturing, Predictive Maintenance, Simulation
- Engineering