Evaluating real-time monitoring and data-storage systems for active magnetic bearings based high-speed machines

Abstract

With the increase of internet of things (IoT)-based systems and data-driven technologies, there has been increased need of effective data management for such systems. The increase of scientific data also calls for the open access to such data for further usage. As an example from industry examples is active bearing (AMB)-based rotating machinery which includes in-built sensor technology that provide extensive data. Based on the literature review there remains a gap in real-time visualization, storing and open access to the sensor data. This thesis aims to bridge this gap by evaluating performance and integration capabilities of data management systems (DMS) like Snowflake, Databricks, and time-series databases (TSDBs) within an IoT and AMB based system. This thesis systematically compares the TSDBs by reviewing benchmark tests while, Snowflake and Databricks by reviewing case study evaluations and integration assessments with IoT technologies. The findings reveal that while modern-data platforms like Snowflake and Databricks offer solutions for data warehousing and real-time analytics, TSDBs, particularly InfluxDB, perform better in managing timeseries data from IoT devices due to their optimized ingestion rates and query performances. This thesis contributes by providing an understanding of the functional compatibilities of different DMSs with IoT frameworks, highlighting the strengths and limitations of each system.