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2021 | OriginalPaper | Chapter

Predictive Maintenance for Sensor Enhancement in Industry 4.0

Authors : Carla Silva, Marvin F. da Silva, Arlete Rodrigues, José Silva, Vítor Santos Costa, Alípio Jorge, Inês Dutra

Published in: Recent Challenges in Intelligent Information and Database Systems

Publisher: Springer Singapore

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Abstract

This paper presents an effort to timely handle 400+ GBytes of sensor data in order to produce Predictive Maintenance (PdM) models. We follow a data-driven methodology, using state-of-the-art python libraries, such as Dask and Modin, which can handle big data. We use Dynamic Time Warping for sensors behavior description, an anomaly detection method (Matrix Profile) and forecasting methods (AutoRegressive Integrated Moving Average - ARIMA, Holt-Winters and Long Short-Term Memory - LSTM). The data was collected by various sensors in an industrial context and is composed by attributes that define their activity characterizing the environment where they are inserted, e.g. optical, temperature, pollution and working hours. We successfully managed to highlight aspects of all sensors behaviors, and produce forecast models for distinct series of sensors, despite the data dimension.

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previous chapter Unsupervised Barter Model Based on Natural Human Interaction

next chapter Cloud Based Business Process Modeling Environment – The Systematic Literature Review

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Title: Predictive Maintenance for Sensor Enhancement in Industry 4.0
Authors: Carla Silva
Marvin F. da Silva
Arlete Rodrigues
José Silva
Vítor Santos Costa
Alípio Jorge
Inês Dutra
Publisher: Springer Singapore
Book: Recent Challenges in Intelligent Information and Database Systems
Print ISBN: 978-981-16-1684-6

Electronic ISBN: 978-981-16-1685-3

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-981-16-1685-3_33

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