Industrial Grinding Machine

Self-diagnostic cycle reports that:

• Shows the value and evaluation of the last measurement whether it is in range or not
• The evolution of the bearings and analysis capable of detecting imbalances and phenomena that are detected at lower frequencies

Input(s):

• Accelerometers
• Spindles
• Controller
• Gateway
• CNC
• Arrowhead compatible interfaces
• Feature analysis and extraction algorithms
• Diagnostic and decision algorithms

Main feature(s):

• Super-efficient proactive maintenance
• Increased production efficiency
• Ensure better product quality and increased machines health and safety
• Making use of data to improve manufacturing efficiency
• Smart services such as maintenance based artificial intelligence techniques that subsequently the results of data analysis offers valued-services to companies

Output(s):

• Self-diagnostic cycle reports
• Architecture that allows the acquisition and processing of data from the machine to know its current state of health and predict possible failures in system to justify the predictive maintenance
• Operating models and behaviour patterns of critical parts of machines
• Most suitable analysis techniques to perform an analysis of machine tools focused in a predictive maintenance system

Hardware requirements:

• Windows (7,8,10), macOS (10.7-10.15), Linux, Controller (PLC), A gateway
• A solution for acquisition of data from sensor deployed in the machine
• The quality of the data collection from the machine through sensors

Customers, End user.

Licensing