Kalman Filter for Predictive Maintenance and Anomaly Detection
| Autor: | Sirarpi Hovsepyan, Paolo Mercorelli, Jan Papadoudis |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Gaussian Predictive Maintenance Pattern recognition Kalman filter Mixture model symbols.namesake Gaussian Mixture Model Manufacturing ComputingMethodologies_PATTERNRECOGNITION Error Detection Engineering Principal component analysis Outlier symbols Anomaly detection Artificial intelligence business Cluster analysis Kalman Filter Simulation Curse of dimensionality |
| Zdroj: | Hovsepyan, S, Papadoudis, J & Mercorelli, P 2021, Kalman Filter for Predictive Maintenance and Anomaly Detection . in 22nd International Carpathian Control Conference, ICCC 2021 ., 9454654, International Carpathian Control Conference, ICCC, no. 22, IEEE-Institute of Electrical and Electronics Engineers Inc., Piscataway, 22nd International Carpathian Control Conference, ICCC 2021, Virtual, Velke Karlovice, Czech Republic, 31.05.21 . https://doi.org/10.1109/ICCC51557.2021.9454654 |
| DOI: | 10.1109/ICCC51557.2021.9454654 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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