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Fabrication of Electrochemical-DNA Biosensors for the Reagentless Detection of Nucleic Acids, Proteins and Small Molecules

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Published on: June 1, 2011

Data-Driven Virtual Sensing for Electrochemical Sensors.

Lucia Sangiorgi¹, Veronica Sberveglieri^2,3, Claudio Carnevale¹

¹Department of Mechanical and Industrial Engineering, University of Brescia, 25123 Brescia, Italy.

Sensors (Basel, Switzerland)

|March 13, 2024

Summary

Machine learning models can predict and manage faults in electrochemical sensors for ethanol measurement. This approach reconstructs sensor data and identifies redundancy, improving monitoring and management.

Keywords:

electrochemical sensors machine learning virtual sensing

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Area of Science:

Electrochemistry
Machine Learning
Sensor Technology

Background:

Electrochemical sensors generate substantial data, enabling advanced machine learning/artificial intelligence (AI) applications.
Machine learning models can reproduce sensor data and predict/manage sensor faults.
Virtual sensing using machine learning is transforming information monitoring and management.

Purpose of the Study:

To apply data-driven models for evaluating sensor redundancy in electrochemical ethanol sensors.
To predict and manage faults in electrochemical sensors using machine learning.
To reconstruct sensor data and assess performance after faults.

Main Methods:

Utilized autoregressive models and artificial neural networks for data-driven analysis.
Developed models to evaluate sensor redundancy within a series of six electrochemical sensors.
Implemented fault prediction and management strategies for sensor data reconstruction.

Main Results:

Demonstrated successful reconstruction of measured values from two electrochemical sensors.
Showcased the ability to reproduce sensor values after simulated faults.
Achieved encouraging results in performance and sensitivity analyses for fault management.

Conclusions:

Data-driven machine learning models effectively evaluate redundancy and manage faults in electrochemical ethanol sensors.
The proposed approach enhances the reliability and robustness of sensor systems.
This methodology offers a promising solution for advanced sensor data monitoring and fault tolerance.