Kalman Filter for Predictive Maintenance and Anomaly Detection

Sirarpi Hovsepyan; Jan Papadoudis; Paolo Mercorelli

doi:10.1109/ICCC51557.2021.9454654

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

Harvard

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

APA

Hovsepyan, S., Papadoudis, J., & Mercorelli, P. (2021). Kalman Filter for Predictive Maintenance and Anomaly Detection. In 22nd International Carpathian Control Conference, ICCC 2021 Article 9454654 (International Carpathian Control Conference, ICCC; No. 22). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCC51557.2021.9454654

Vancouver

Hovsepyan S, Papadoudis J , Mercorelli P. Kalman Filter for Predictive Maintenance and Anomaly Detection. In 22nd International Carpathian Control Conference, ICCC 2021. Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc. 2021. 9454654. (International Carpathian Control Conference, ICCC; 22). doi: 10.1109/ICCC51557.2021.9454654

Bibtex

@inbook{6df9a391d34e41db83c14512d9883147,

title = "Kalman Filter for Predictive Maintenance and Anomaly Detection",

abstract = "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.",

keywords = "Error Detection, Gaussian Mixture Model, Kalman Filter, Manufacturing, Predictive Maintenance, Simulation, Engineering",

author = "Sirarpi Hovsepyan and Jan Papadoudis and Paolo Mercorelli",

note = "Funding Information: The authors acknowledge FCT (Fundacao para a Ciencia e a Tecnologia), Lisbon, through the 3 Quadro Comunitrio de Apoio, the POCTI and FEDER programs, and the grant PTDC-EME-PME-108859-2008 (through IDMEC) and UID/CTM/50025/2013 (through IPC), and the SFRH/BD/74027/2010 PhD schollarship within POPH/FSE (Programa Operacional Potencial Humano/ Fundo Social Europeu). Publisher Copyright: {\textcopyright} 2021 IEEE.; 22nd International Carpathian Control Conference, ICCC 2021 ; Conference date: 31-05-2021 Through 01-06-2021",

year = "2021",

month = may,

day = "31",

doi = "10.1109/ICCC51557.2021.9454654",

language = "English",

isbn = "978-1-7281-8610-8",

series = "International Carpathian Control Conference, ICCC",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

number = "22",

booktitle = "22nd International Carpathian Control Conference, ICCC 2021",

address = "United States",

url = "http://www.icc-conf.cz/conference/iccc2021/",

}

RIS

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 -

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Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

DOI

https://doi.org/10.1109/ICCC51557.2021.9454654
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Kalman Filter for Predictive Maintenance and Anomaly Detection

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