Great question! You're absolutely right that AI-powered predictive maintenance is the future, and starting with the right data foundation is crucial. For your Allen-Bradley ControlLogix systems, here are the key data points you should be capturing today:

1. **Operational Parameters**: Motor current/voltage, speed, torque, power consumption, and runtime hours. These give you baseline performance metrics.

2. **Environmental Sensors**: Temperature (motor windings, bearings, cabinets), vibration levels, pressure readings, and flow rates. These are early failure indicators.

3. **Process Variables**: Analog inputs like level, pH, conductivity, and any specialized process measurements relevant to your equipment.

4. **Digital Status Flags**: Equipment on/off states, alarm conditions, fault codes, safety interlocks, and maintenance mode indicators.

5. **Cycle Counters**: Production counts, start/stop cycles, and operational cycles to track wear patterns.

6. **Timing Data**: Cycle times, response times, and processing durations to detect performance degradation.

7. **Quality Metrics**: Reject rates, dimensional measurements, or any quality control data that correlates with equipment health.

Start by implementing data logging for these points at appropriate intervals (seconds to minutes depending on the parameter). The key is consistency - capture the same data points across similar equipment to build comparable datasets for your machine learning models. This foundation will let you detect patterns, establish normal operating ranges, and eventually predict failures before they happen!