MIMOSA for Condition-based Maintenance

Riku Salokangas, Martin Larrañaga, Petri Kaarmila, Olli Saarela, Erkki Jantunen

    Research output: Contribution to journalArticleScientificpeer-review


    The paper describes how the use of MIMOSA open source data model supports the development of a low-cost monitoring system that is capable to carry out automatic diagnosis and prognosis. MIMOSA follows the ISO 13374 definitions (condition monitoring) and links well with the ISO 17359 (diagnosis) and ISO 13381 (prognosis). The MIMOSA data model defines all the necessary ontology for the automatic monitoring system. As a use case the paper describes the installation of the MIMOSA data model in a Raspberry where MariaDB is used as the database engine. A low-cost MEMS accelerometer has been installed to a Raspberry thus enabling the collection of vibration data from rolling element bearings of a conveyor. The goal is to compare the results of low cost devices to a more expensive data acquisition system. The necessary signal analysis functions are programmed with VTT 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 Remaining Useful Life (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. In addition, the article discusses the system architecture, which enables data transfer from cloud to cloud conveniently and reliably, regardless of the number or location of the clouds. In conclusion, the paper summarises the key role of MIMOSA in building and using this kind of automatic monitoring systems.
    Original languageEnglish
    Number of pages10
    JournalInternational Journal of COMADEM
    Issue number2
    Publication statusPublished - 2021
    MoE publication typeA1 Journal article-refereed


    • CBM
    • Mimosa
    • Signal Analysis
    • Diagnostics
    • Prognostics
    • Low-Cost Hardware


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