b'\x1b\x1a\x19 \x1f\x18\x1d\x17\x16Machine Technologybased on a proprietary AI algorithmAn initial 60-second, fixed-cycleviewspindlehealthmetricson to model a spindles signature. test run establishes the baselinescreens.Each spindle has its own signa- value for comparison during sub- The system tells you how the turelike a signal fingerprint sequent tests. Machine operatorsspindle compares to the golden and no two are alike, Sanders said. can run these tests anytime andmodel that came from the data re-corded, Sanders said.Presented metrics include spin-dle unbalance, lubrication condi-tion and bearing health. Each is displayed as a percentage of re-maining spindle life based on the individual metric, as shown in the image on Page 13. The lowest of these percentages is used as the overall estimate of remaining spin-dle life.Over time, a neural network self-organizing map adds to the profile of each spindle, learning to better assess its health using information gleaned from an increasingly large dataset.We are taking terabytes of data every time we do sampling of these spindles, Sanders said, and every iteration improves the model. So the more times you sample, the more accurate it is.Certain types of data can help spot impending failures. Vibration analysis, for example, may reveal abnormal patterns that indicate po-tential problems.When you starve a spindle of lu-brication, you get a different vibra-tion signature, Sanders said. Or if a bearing has been crushed, that will change the signature of that bearing.about the authorWilliam Leventon is a contributing writer for CTE. Contact him at 609-926-6447 or [email protected] 2020 MachineTech.indd 14 2/14/20 12:22 PM'