Benchmarking Deep Neural Network Architectures for Machining Tool Anomaly Detection

What is it about?

With the democratization of cyber-physical systems, edge computing, and large-scale data infrastructure, the volume of operational data available is continuously increasing. One of the significant challenges in current industrial research is defining a robust and scalable approach for machine health monitoring and anomaly detection. This paper presents a benchmarking of deep neural network architectures for the identification of machining tool anomalies on a lathe machine. The ability of different architectures to identify incipient anomalies in tool quality is compared. Finally, a recommendation is provided based on the results obtained on the type of architecture that is appropriate for the identification of machining tool anomalies.

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