Some Next-Generation of Spindle Intelligence: Real-Time Condition Monitoring and Diagnosis

As the era of Industry 4.0 (i.e., the fourth-generation industrial revolution) develops, machine tools in particular are becoming interconnected, forming a collaborative community in smart factories. “Smart manufacturing” is becoming the norm, in a world where intelligent machines, systems, and networks are able to exchange information between them and respond independently, with autonomy to information, with the goal to manage industrial production processes. An important challenge is the transformation of traditional machines into intelligent machines, respectively intelligent or smart spindle. The purpose of this paper is to analyze the spindle using the intelligent models in in-situ conditions. The historical evolution, recent challenges and future trends of machine tool spindles were analyzed, noting that further development would be necessary to enable sensor/AI module integration to make the spindle unit an inherent quality assurance system. This study proposes a deep learning-based approach to spindle health monitoring based on multi sensor vibration signal analysis. The two proposed AI methods is based on the analysis of acceleration and synchronous envelope vibrations by demodulating the signal based on the Hilbert transform to identify critical bearing defects and specific defects at high frequencies.