An article on the Remaining Useful Life (RUL) of toolholder bearings has been published

An article by our colleague Giuseppe Dipace has been published in the special issue of Machines MDPI titled "Recent Advances in Machinery Condition Monitoring and Fault Diagnosis: From Traditional Algorithms to the Age of Artificial Intelligence ".

The article is part of Giuseppe's research at M.T. in collaboration with the University of Ferrara and focuses on estimating the Remaining Useful Life (RUL) of toolholder bearings using Condition Monitoring, vibration analysis, and Machine Learning Techniques.

This innovative approach combines the reconstruction error of a sparse autoencoder with exponential degradation modeling. This method helps make predictive maintenance more applicable to real-world production environments, thanks to the use of features extracted from vibration analysis and requiring only an initial period of healthy operation to train the model.

Cooperation between business and academia allows for the analysis of critical issues faced in the manufacturing world and the transformation of these issues into new knowledge and concrete solutions.

Read the full article here.

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