Kalman Filter for Predictive Maintenance and Anomaly Detection
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Kalman Filter for Predictive Maintenance and Anomaly Detection. / Hovsepyan, Sirarpi; Papadoudis, Jan; Mercorelli, Paolo.
22nd International Carpathian Control Conference, ICCC 2021. Piscataway : IEEE - Institute of Electrical and Electronics Engineers Inc., 2021. 9454654 (International Carpathian Control Conference, ICCC; No. 22).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Kalman Filter for Predictive Maintenance and Anomaly Detection
AU - Hovsepyan, Sirarpi
AU - Papadoudis, Jan
AU - Mercorelli, Paolo
N1 - Conference code: 22
PY - 2021/5/31
Y1 - 2021/5/31
N2 - 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.
AB - 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.
KW - Error Detection
KW - Gaussian Mixture Model
KW - Kalman Filter
KW - Manufacturing
KW - Predictive Maintenance
KW - Simulation
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85113416819&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/4b1210b4-71b5-317c-848b-3d7f48d90b5d/
U2 - 10.1109/ICCC51557.2021.9454654
DO - 10.1109/ICCC51557.2021.9454654
M3 - Article in conference proceedings
AN - SCOPUS:85113416819
SN - 978-1-7281-8610-8
T3 - International Carpathian Control Conference, ICCC
BT - 22nd International Carpathian Control Conference, ICCC 2021
PB - IEEE - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway
T2 - 22nd International Carpathian Control Conference, ICCC 2021
Y2 - 31 May 2021 through 1 June 2021
ER -