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Related Experiment Video

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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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A virtual sensor for online fault detection of multitooth-tools.

Andres Bustillo¹, Maritza Correa, Anibal Reñones

¹Department of Civil Engineering, University of Burgos, C/Francisco de Vitoria s/n, 09006, Burgos, Spain. abustillo@ubu.es

Sensors (Basel, Switzerland)

|December 14, 2011

Summary

This study introduces a virtual sensor for real-time fault detection in multitooth milling tools. The system uses electrical power and machining time data, achieving high accuracy in identifying tool issues without physical sensor proximity.

Keywords:

Bayesian classifier industrial applications multitooth-tools tool condition monitoring virtual sensor

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Area of Science:

Manufacturing Engineering
Machine Tool Monitoring
Artificial Intelligence in Manufacturing

Background:

Direct sensor installation near tool tips in industrial milling centers is often infeasible.
Online fault detection is crucial for many industrial tasks, necessitating alternative sensing solutions.
Multitooth tools present unique challenges for signal reliability due to intermittent cutting insert engagement.

Purpose of the Study:

To develop a robust virtual sensor for online fault detection of multitooth tools in milling operations.
To create a system that minimizes the need for frequent recalibration, even after maintenance.
To validate the virtual sensor's performance under real-world industrial conditions.

Main Methods:

Implementation of a virtual sensor utilizing a Bayesian classifier.
Integration of mathematical models with physical sensor data (electrical power consumption and machining time).
Validation using k-fold cross-validation for performance assessment.

Main Results:

The virtual sensor demonstrated high recognition accuracy, with an average of 0.957 true positives and 0.986 true negatives.
Measured accuracy reached 98%, indicating a strong capability to correctly identify new fault cases.
Successful validation on a milling center used for mass production of automobile engine crankshafts.

Conclusions:

The developed virtual sensor is a viable and effective solution for online fault detection in industrial milling processes.
The system's robustness and high accuracy make it suitable for demanding production environments.
This approach offers a practical alternative to physical sensors where direct installation is not possible.