Solution
We set out to identify the exact dates for the spindle changes using a data-driven approach, as we were advised the datetimes could be weeks or months out. With more reliable dates in place, the next step was to develop a technique to detect and diagnose a developing spindle failure ahead of these change dates.
Our solution involved using our existing ML-stack, and building features without any reliance on the machine manufacturer (OEM). We built new health indicators that could detect failures due to high vibration levels, runout above tolerance, bearing failure, and more.