Fingerprints helping preventative maintenance

#Announcement(Startup) #Company [ via IoTForIndiaGroup ]
#Organizer : Aingura IIoT #City : San Antolín , Spain

Challenge
Spindle heads are a critical part of a machine-tool, in charge in a large percentage of the material removing operation. If the spindle head fails, the machine is stopped. Because the machine is part of a manufacturing system, a breakdown stops the complete production line. Depending on the production facilities, the unexpected downtime could cost around $50k per hour additional to the +$20k spindle spare. A broken spindle head could take up to 40 hours (five 8-hour shifts) to be replaced, if the damage is contained only at the spindle part. As these elements are rated from 14 to 24 kW, and unexpected failure could also cause collateral damages, increasing the costs of replacement.

Therefore, a continuous analysis on the spindle head health and other critical parts is important. Even during installation on a brand-new machine, as the useful life could be compromised during spindle manufacturing or stockage.
Solution
In order to have a general view of the spindle head health, the Aingura IIoT approach is to develop a data-based fingerprint where all the related variables are analyzed in a multivariate manner during real operation. Therefore, this fingerprint is developed for each spindle head during first stages of real production, such as capacity certification among others. Once the fingerprint is developed, it could be used to compare the behavior pattern itself along its useful life or between other spindles performing the same operation.

In order to get the required data to build the fingerprints, a proprietary embedded edge device called Oberon, is connected to the machine. Oberon system is able to gather process variables its Sinumerik 840D, PLC and CNC system, and other external sensors that can bring a clear idea of spindle head, such as accelerometers and high sampling rate energy measurements.

The data required for these type of analysis has the following details:

Sampling rate:
NCU: 4 milliseconds
Accelerometer: 32 microseconds
Energy: 250 microseconds
Number of variables: 29
Main variables: time stamp, machine states, power, angular velocity, torque, temperature, bearings frequencies and energy consumption at the three phases.
Sampling time: 10 machining cycles
Dataset size: 270 MB

Aingura IIoT is working to enable data stream processing using GMM, giving the option to monitor the fingerprint of an asset in real-time, as shown in our work titled: “Clustering of Data Streams with Dynamic Gaussian Mixture Models. An IoT Application in Industrial Processes”.

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