Relevant Experience – Mikel Projects

Completed Projects

Mikel has implemented the industrial and IT communication protocols that allow the ii40_Connect Monitoring Platform to communicate with different systems and collect operation and sensor data.

Gateway IoT2050 Xformer

Based on the machine data collected by the ii40_Gateway and applying data analysis and ML techniques, Mikel has developed different functionalities for individual user cases leveraging the value of the data:

  • Identification of sensor malfunction for F&B customer, to detect when temperature readings were abnormal
  • Identification of valve malfunction for F&B customer, to alert of mechanical jams.
  • Estimation of internal oven temperature, for thermal process, leveraging the Digital Twin and using indirect external temperatures as an input.
ML OVEN
Identification of valve malfunction

Further exploiting the ability to extract information from machine operation and sensor data, Mikel has recently worked on the development of the Failure Mode Event Analysis module (FMEA) within ii40_Connect to offer an integrated tool for equipment diagnostics and troubleshooting.

In this project, he designed the data model for an FMEA event, and managed the data retrieval and arrangement from the historical databases and the Digital Twin.

Current Projects

Mikel is currently working in a project for a customer that employs the ii40_Connect Platform to monitor their fleet of hydraulic units in a variety of sectors. The goal of this project is to develop a Remote Heath Assessment Service (RHAS), to determine the condition of the hidraulic unit based on the collected operation and sensor data, and allow early identification of any deterioration in machine health or performance, specially in applications with demanding, highly repetitive cycles..

Hidraulic unit sensors

Hydraulic Unit RHAS Testbed with Sensors

This project involves the development of ML models both in the edge-ii40_Gateway and in the ii40_Cloud, to allow fast edge computation during the short cycles of customer’s application, and to leverage the aggregated fleet data in the cloud for system health algorithms.

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