Abstract
Through technological advents from Industry 4.0 and the Internet of Things, as well as new Big Data solutions, predictive maintenance begins to play a strategic role in the increasing operational performance of any industrial facility. Equipment failures can be very costly and have catastrophic consequences. In its basic concept, Predictive maintenance allows minimizing equipment faults or service disruptions, presenting promising cost savings. This paper presents a data-driven approach, based on multiple-instance learning, to predict malfunctions in End-of-Line Testing Systems through the extraction of operational logs, which, while not designed to predict failures, contains valid information regarding their operational mode over time. For the case study performed, a real-life dataset was used containing thousands of log messages, collected in a real automotive industry environment. The insights gained from mining this type of data will be shared in this paper, highlighting the main challenges and benefits, as well as good recommendations, and best practices for the appropriate usage of machine learning techniques and analytics tools that can be implemented in similar industrial environments.
Supported by Continental Advanced Antenna (CAA), Portugal (a Continental AG Group Company).
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Notes
- 1.
To demonstrate this approach, the authors used datasets from in-service EOL devices, nevertheless, this methodology can also be applied to other research fields, as e.g., IT infrastructures or any industrial equipment, in which such sort of logs could be collected.
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Acknowledgements
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. Special thanks to all the personnel involved, that gave the support necessary to make the publishing of this work possible, in particular to the Continental Advanced Antenna and the University of Trás-os-Montes and Alto Douro.
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Vicêncio, D., Silva, H., Soares, S., Filipe, V., Valente, A. (2021). An Intelligent Predictive Maintenance Approach Based on End-of-Line Test Logfiles in the Automotive Industry. In: Peñalver, L., Parra, L. (eds) Industrial IoT Technologies and Applications. Industrial IoT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 365. Springer, Cham. https://doi.org/10.1007/978-3-030-71061-3_8
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