A good data acquisition system defines the signal source, measurement method, scaling, sampling or event rule, communication path, timestamp and validation rule. It should also make communication failure visible instead of silently producing misleading data.
Signal types
Factory machines may expose digital states, pulses, analog signals, serial registers or network data. The acquisition design should match the signal instead of forcing every machine into one interface.
Measurement is more than reading a value
A raw voltage, pulse or register often needs calibration, scaling, filtering and unit conversion. For example, a pulse stream may need a defined pulses-per-revolution relationship before it becomes RPM.
Sampling versus events
Fast-changing values may need periodic sampling. Counts, alarms and state changes are often better represented as events. Choosing the right model reduces unnecessary data while preserving the information needed for analysis.
Store trustworthy metadata
Every measurement should have a machine identity, timestamp, unit and clear meaning. If calibration or configuration changes, those changes should also be traceable.
Design for expansion
A small pilot should leave room for additional machines and parameters. Consistent naming, device IDs and communication conventions make later expansion much easier.