Factory Acceptance Testing: Data Signals and Condition Monitoring

Factory acceptance testing (FAT) is the final controlled rehearsal before a material handling system leaves the build floor. For warehouse operators, the FAT is often viewed as a pass/fail gate: does the conveyor line, sorter, or storage buffer meet the contractual throughput and functionality requirements? In practice, the most valuable outcome of a well-executed FAT is not a signature on a certificate but a body of data signals and condition monitoring reference points that will support commissioning, troubleshooting, and lifecycle maintenance long after the equipment is installed. This article explains what data signals are generated during a FAT, how components interact to produce those signals, how to interpret common symptoms, and how to avoid the interpretation errors that turn useful evidence into misleading conclusions.

Purpose and Boundaries of Factory Acceptance Testing #

A FAT demonstrates that the equipment performs according to the design intent and the agreed contractual specification while it is still in a controlled factory environment. The machine is typically tested with simulated loads, temporary cabling, and a test control system that may differ from the final site configuration. The goal is to confirm that functional sequences operate, that throughput targets can be reached, and that safety-related behavior is present before shipment.

Data signals are central to this evidence. Motor currents, photoeye transitions, encoder positions, frequency converter status words, and cycle timings are recorded to show that the machine behaves correctly. But the FAT is not an in-situ performance guarantee. Site conditions differ: floor flatness, ambient temperature, and the interaction with upstream and downstream equipment all change the machine’s behavior. The data collected at FAT should therefore be treated as a reference fingerprint, not as the sole prediction of site performance.

It is also important to state a boundary that applies to all testing activities: nothing in this article overrides site procedures, lockout requirements, OEM documentation, or competent engineering judgment. Safety devices must never be bypassed to achieve a test result. If a test cannot be run safely in the factory, the appropriate action is to stop the test and resolve the issue formally.

Data Signal Architecture in the Test Environment #

Every automated warehouse system relies on a chain of signal flow. Physical sensors generate raw electrical signals; controllers filter, scale, and convert them into logical tags; and visualization or logging systems store those tags for later review. During a FAT, the same architecture is present, but it is often assembled more crudely than on the production floor. Temporary panels, test harnesses, and simulators may be used to mimic field wiring that does not yet exist.

Understanding this temporary architecture is essential before making judgments. A signal that appears at the HMI represents the end of a path that includes the sensor, the input module, the communication protocol, and the processor’s scan time. If the path is different during FAT than it will be on site, the transmission delays and filtering should be recorded.

Typical signals available during a FAT include:

• Motor current and power draw from variable frequency drives
• Drive status words, running feedback, and fault codes
• Photoeye state changes along accumulation and induction zones
• Encoder counts or positioning values on linear axes and sorters
• Pneumatic pressure and valve command times
• Cycle timestamps for divert actions, stops, and releases
• Control system scan time and I/O update rates
• Alarm and event logs with time stamps

The most common problem in FAT data analysis is poor time alignment. A motor current trend stored on a drive’s internal memory and a photoeye log stored on the PLC may not share a common clock. Before drawing conclusions, confirm that all signals can be aligned to a single timeline. If they cannot, the test window should be designed around a common event marker, such as a start command or a specific product ID.

Component Interactions That Determine Test Validity #

A conveyor system is not a collection of independent components; it is a coupled set of mechanical, electrical, and control elements. The validity of a FAT depends on how realistically those interactions are reproduced. For example, an accumulation conveyor depends on the interaction between photoeye placement, zone controller logic, and the mechanical backpressure created by a stopped carton. If the test load consists of empty cartons with a different coefficient of friction than the production packaging, the accumulation behavior may be misleading.

Consider a sorter with multiple divert units. The electrical interaction between a diverter solenoid, the pneumatic supply, and the control output is not visible in a simple pass/fail test. The current draw of the solenoid gives a response time, the air pressure gives a force margin, and the photoeye timing gives the actual material trajectory. All three signals must be read together to determine whether the divert is robust or marginal.

Component interactions are often observable as small signal distortions. A motor current trace on a linear conveyor will show a periodic ripple if a roller is failing or a belt seam is uneven. A pneumatic pressure trend will dip slightly on every divert command, then recover. The FAT baseline captures these patterns, and later deviations from these patterns become a condition monitoring trigger. This is why the FAT data package must include not only the “pass” results but also the raw, time-resolved signals that produced those results.

Condition Monitoring Signals Worth Capturing #

Not all signals are equally useful for condition monitoring. The aim is to capture data that changes meaningfully as components wear, misalign, or lose performance. The following signal families are worth logging during the FAT and later referencing during commissioning and lifecycle reviews.

Motor current and power draw are the most accessible indicators of mechanical load. A baseline current profile at constant speed and with a defined load state reveals expected amplitude and ripple. Later, an unexplained rise in current with the same product mix can indicate belt drag, bearing wear, or mechanical misalignment.

Cycle timing is another powerful baseline. This includes how long a sorter takes to move from home to divert position, how long a lift table takes to rise and lock, and how much time passes between a photoeye input and a motor start command. These timings reflect the performance of mechanical brakes, pneumatic cylinders, and control logic. Small increases over time often precede mechanical failure.

Vibration and temperature signals are valuable if the equipment has built-in condition sensors. Motor winding temperature, gearbox housing temperature, and externally mounted accelerometers are common in high-value equipment. At FAT, these establish the thermal and vibrational baseline under defined load. Environmental differences between the factory and the site, such as higher ambient temperature or a concrete floor with different damping, will shift these values, but the shape of the trend remains comparable.

Pneumatic system pressure and flow should also be logged. The pressure drop during simultaneous divert commands, the recovery time after a burst, and the timing of valve responses are all indicators of the air treatment and distribution system. A FAT baseline that shows a healthy pressure margin will help site teams distinguish between a local valve problem and a supply-side deficiency.

Practical Symptom-to-Signal Diagnostic Table #

The table below maps common FAT symptoms to the signal patterns that typically accompany them. It is intended as a diagnostic aid for the test engineer’s first pass, not as a definitive root-cause analysis. Always verify with direct inspection and respect the OEM documentation.

For factory acceptance testing: data signals and condition monitoring, the matrix should be completed with evidence from the same event window. Mixing observations from unrelated shifts can create a convincing but false causal story. If timestamps are inconsistent, establish which controller, server or operator record is authoritative before comparing event order.

Trend evidence is more useful when the measurement definition remains stable. Record units, sampling interval, filtering, equipment mode and product family. A rising fault count may reflect increased throughput rather than deteriorating equipment, while a stable count can hide deterioration if production volume has fallen.