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We were considering two possible prediction scenarios:
1) instantaneous vector of attributes Fx,Fy,Fz,Vx,Vy,Vz,Ae at some time t
with the expectation that we predict wear at time t2) a trajectory of the attributes from time 1 up to time t,
with the expectation that we predict wear at time tWould you be able to clarify the expected paradigm?
Regards,
Bob.
We were considering two possible prediction scenarios:
1) instantaneous vector of attributes Fx,Fy,Fz,Vx,Vy,Vz,Ae at some time t
with the expectation that we predict wear at time t2) a trajectory of the attributes from time 1 up to time t,
with the expectation that we predict wear at time tWould you be able to clarify the expected paradigm?
Regards,
Bob.
Another clarification question:
We are assuming that we will be getting at least an entire cut file of observations as input so that statistics and spectral features can be calculated.
Regards,
Bob.
Each training record contains one “wear” file that lists wear after each cut in 10^-3 mm, and a folder with approximately 300 individual data acquisition files (one for each cut). The data acquisition files are in .csv format, with seven columns, corresponding to:
Column 1: Force (N) in X dimension
Column 2: Force (N) in Y dimension
Column 3: Force (N) in Z dimension
Column 4: Vibration (g) in X dimension
Column 5: Vibration (g) in Y dimension
Column 6: Vibration (g) in Z dimension
Column 7: AE-RMS (V)The spindle speed of the cutter was 10400 RPM; feed rate was 1555 mm/min; Y depth of cut (radial) was 0.125 mm; Z depth of cut (axial) was 0.2 mm. Data was acquired at 50 KHz/channel.
Attribute (feature) extraction per se is up to the competitors. If you download the data on the main competition page:
https://www.phmsociety.org/competition/phm/10You will find that there are actually 1890 cut files available to work with!
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