Low-Cost Solutions for Maintenance with a Raspberry Pi

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

The paper describes how to develop an inexpensive automatic condition monitoring system that can reliably monitor the conveyor bearings. For this purpose, a Raspberry Pi 3 and a low-cost MEMS accelerometer have been used comparing the results to a more expensive data acquisition system. The project utilizes the open-source Mimosa data model that is installed to the Raspberry Pi to store and transmit data for analytics to diagnose the fault and determine the Remaining Useful Life (RUL). The required signal analysis is programmed with VTT Python O&M Analytics, which provides the ability to conveniently perform signal analysis, offering a comprehensive set of algorithms that can detect a bearing failure and calculate the RUL. The amplitudes of the bearing fault frequencies can be reliably seen using envelope analysis, and the magnitude of the amplitudes can be used to determine whether the bearing is defective. Furthermore, this article presents some possible low-cost data acquisition systems to monitor components in industrial use cases reliably. In conclusion, Raspberry Pi 3 is suitable for use in some industrial systems, for example, as a low-cost single-board computer for bearing maintenance. The goal of the project is to get a reliable and inexpensive data acquisition system for bearing maintenance and replace the more expensive one.
Original languageEnglish
Title of host publicationeProceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference
EditorsPiero Baraldi, Francesco Di Maio, Enrico Zio
PublisherResearch publishing services
Number of pages7
Publication statusPublished - 22 Jun 2020
MoE publication typeA4 Article in a conference publication
Event30th European Safety and Reliability Conference ESREL 2020, The 15th Probabilistic Safety Assessment and Management Conference PSAM 15 - Venice, Italy
Duration: 1 Nov 20206 Nov 2020
https://www.esrel2020-psam15.org/index.html

Conference

Conference30th European Safety and Reliability Conference ESREL 2020, The 15th Probabilistic Safety Assessment and Management Conference PSAM 15
Abbreviated titleESREL 2020 PSAM 15
CountryItaly
CityVenice
Period1/11/206/11/20
Internet address

Keywords

  • Condition-based maintenance (CBM)
  • Low-Cost Hardware
  • MIMOSA
  • Signal analysis
  • Diagnostics
  • Prognostics

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  • Cite this

    Larrañaga, M., Salokangas, R., Kaarmila, P., & Saarela, O. (2020). Low-Cost Solutions for Maintenance with a Raspberry Pi. In P. Baraldi, F. Di Maio, & E. Zio (Eds.), eProceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference [3780] Research publishing services. https://www.rpsonline.com.sg/proceedings/esrel2020/pdf/3780.pdf