Abstract
The transformation of industrial manufacturing with computers and automation with smart systems leads us to monitor and log of industrial equipment events. It is possible to apply analytic approaches, and to find interpretive results for strategic decision making, providing advantages such as failure detection and predictive maintenance.
Over the last years, many researchers have been studying the application of machine learning techniques to improve such tasks. In this context, we develop a system capable of detect anomalies on an Air Production Unit (APU), taking into consideration the peak frequency of each sensor. The study started with the analysis of the sensors installed on the APU, defining its normal behavior and its failure mode. Using that information, we define rules, to monitor the APU, to detect anomalies on its components, and to predict possible failures. The definition of rules was based on the peak frequency analysis, which allowed the setting of boundaries of normality for the APU working modes and, thus, the identification of anomalies.
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Acknowledgments
This research was Funded from national funds through FCT - Science and Technology Foundation, I.P in the context of the project FailStopper (DSAIPA /DS/0086/2018).
This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/ 2020.
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Barros, M., Veloso, B., Pereira, P.M., Ribeiro, R.P., Gama, J. (2020). Failure Detection of an Air Production Unit in Operational Context. In: Gama, J., et al. IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. ITEM IoT Streams 2020 2020. Communications in Computer and Information Science, vol 1325. Springer, Cham. https://doi.org/10.1007/978-3-030-66770-2_5
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DOI: https://doi.org/10.1007/978-3-030-66770-2_5
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