To transition to predictive diagnostics in the maintenance of critical equipment, we propose starting with an economically viable step, namely, diagnosing using Statistical Process Control (SPC) methods.
Advantages:
Minimal data volume requirements. Monitoring data and diagnosing using SPC methods require incomparably less data than diagnosing with machine learning (ML) methods. For diagnostic and forecasting purposes, it does not matter whether the data represents 100% of the data series or just a part of it; however, the data must be organized rationally and with consideration for temporal distribution.

Data collected manually will suffice. As a result, there are minimal requirements for the computational power of the equipment and the storage of historical data (continuous data collection is not required).