The paper describes how the use of Mimosa open source data model supports the development of a low-cost condition 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 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 accelerometer has been installed to a Raspberry thus enabling the collection of vibration data from rolling element bearings of a conveyor. In addition, a low-cost system that uses Arduino is presented for data collection in future use cases. The necessary signal analysis functions are programmed with Python which offers a wide collection of useful functions. The paper summarises the key role of Mimosa in building and using this kind of automatic monitoring systems.
|Series||Smart Innovation, Systems and Technologies|
|Conference||32nd International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management, COMADEM 2019|
|Abbreviated title||COMADEM 2019|
|Period||3/09/19 → 5/09/19|
- Low-cost hardware