Runnable simulator field guide
PLC sortation diverter scenario: implementation, evidence and troubleshooting
Direct answer
PLC sortation diverter scenario becomes useful when it connects entry edge, product identity, class, conveyor state, travel delay, divert window, actuator feedback, destination sensor, counts and reject state with sensor event through tracking record, time or position model, classification decision, output command, mechanism response and destination confirmation, then proves normal products and divert products each receiving one decision and one confirmed destination without duplicated counts under normal, boundary, fault and recovery conditions. The objective is a repeatable engineering or learning result, not merely activity inside a page or tool.
This guide is written for pLC learners programming a conveyor diverter with classification input, product tracking and destination proof. The intended result is specific: the learner can create one event per product, schedule one actuation, prevent double counts and recover tracking from stops or missing confirmation.
System map / 02
Six concepts that control the result
Treat these as connected checkpoints. Each checkpoint has an expected state, an observable state and a boundary to the next part of the system. That structure prevents a software indication from being mistaken for physical proof.
Define the operating contract
entry edge, product identity, class, conveyor state, travel delay, divert window, actuator feedback, destination sensor, counts and reject state. For sensor-triggered package classification, divert timing, confirmation and count reconciliation, record the initial condition, actor, requested change, observable result and stopping condition before selecting a tool or implementation.
Map the evidence path
sensor event through tracking record, time or position model, classification decision, output command, mechanism response and destination confirmation. Separate request, internal state, output or service, physical or user-visible result and independent feedback so each boundary can be inspected.
Prove normal operation
normal products and divert products each receiving one decision and one confirmed destination without duplicated counts. Run more than one cycle from a known state and retain the values, timings or artifacts that demonstrate repeatability.
Exercise a boundary case
sensor chatter, back-to-back products, missing class, late response, failed confirmation, jam, stop during transit, restart and manual removal. Choose minimum, maximum, simultaneous, delayed or restart conditions that reveal assumptions hidden by the happy path.
Diagnose a controlled fault
an event, identity, classification, tracking, timing, output, actuator, destination, count or recovery mismatch. Preserve the first symptom, divide the system at a measurable boundary and change one condition only after predicting the result.
Transfer and hand over
the model compared with measured conveyor travel, actuator response, sensor placement and safe recovery on target equipment. Restore normal state, remove temporary changes, repeat affected checks and document which claims remain limited to the learning environment.
Procedure / 03
A six-step practice and commissioning workflow
Run the steps in order the first time. Later, the same structure becomes a diagnostic loop: define the expected condition, observe the boundary, interpret the difference and choose one proving action.
- 01
Write the acceptance case
Convert entry edge, product identity, class, conveyor state, travel delay, divert window, actuator feedback, destination sensor, counts and reject state into initial conditions, one stimulus and observable pass criteria.
Evidence: Another person can repeat the case without guessing the intended result.
Avoid: Using page completion or an animation as the acceptance criterion.
- 02
Build the map
Document sensor event through tracking record, time or position model, classification decision, output command, mechanism response and destination confirmation and name who owns each state or decision.
Evidence: Every request and result has a source, destination and useful inspection point.
Avoid: Using the same value as command, status and independent feedback.
- 03
Run the baseline
Apply normal products and divert products each receiving one decision and one confirmed destination without duplicated counts from a clean start and record the expected evidence.
Evidence: Repeated runs produce the same bounded result.
Avoid: Changing several parameters before a baseline exists.
- 04
Challenge assumptions
Test sensor chatter, back-to-back products, missing class, late response, failed confirmation, jam, stop during transit, restart and manual removal without changing the acceptance contract.
Evidence: Limits, timing and restart behavior reach defined states.
Avoid: Testing only one ideal sequence.
- 05
Isolate one failure
Introduce or analyse an event, identity, classification, tracking, timing, output, actuator, destination, count or recovery mismatch and locate the first disagreement.
Evidence: The proving action distinguishes the leading hypotheses.
Avoid: Resetting, forcing or replacing before evidence is retained.
- 06
Close the evidence loop
Complete the model compared with measured conveyor travel, actuator response, sensor placement and safe recovery on target equipment and repeat the affected regression cases.
Evidence: A run is complete only when the requested behavior, stop behavior, fault response and recovery are observable from a fresh initial condition.
Avoid: Treating an acknowledged message or one successful rerun as handover.
Diagnostic matrix / 04
Symptoms, proving points and next actions
The table is a reasoning aid, not a parts-replacement chart. Preserve the initial symptom, inspect the named boundary and use the interpretation to choose the next controlled test. Site safety procedures and equipment manuals remain authoritative.
| Observed symptom | Inspect | Interpretation | Next proving action |
|---|---|---|---|
| The expected result is unclear | Requirement, initial state, actor, stimulus, units and pass condition | The operator, programmer and reviewer may be solving different versions of the task. | Rewrite one observable acceptance case before continuing. |
| Internal state changes but the outcome does not | Request, final owner, output or service boundary and independent feedback | A software or interface indication proves intent at one layer, not the complete outcome. | Trace the first boundary after the changing state. |
| Normal case passes but an edge case fails | Limits, timing, simultaneous events, reset and restart assumptions | The implementation contains a hidden assumption exposed by the changed condition. | Add the failed boundary as a permanent regression case. |
| The failure disappears after reset | Original symptom, histories, diagnostics, timestamps and active cause | Reset changed evidence or state without proving the initiating cause. | Reproduce under a controlled condition and preserve pre/post-event data. |
| Simulator and target disagree | Model boundary, software version, task timing, I/O behavior, data types and configuration | A learning model and the intended target do not share one of the recorded assumptions. | Reduce the case and verify against current target documentation. |
| The result cannot be explained | Prediction, observation, proving action, alternative hypotheses and limitations | Activity occurred but the evidence is not yet transferable or reviewable. | Have the learner defend the signal path and repeat a changed case. |
Product evidence / 05
What the browser practice can actually demonstrate
The browser runtime joins editable control state to visible I/O and machine or process behavior, allowing the same initial conditions and stimuli to be replayed.
Where simulation stops
The browser model is not a digital twin of a selected sorter and cannot validate physical timing, pneumatic response, guarding or throughput.
Commissioning notebook / 06
Six cases that turn the concepts into evidence
Use these as written briefs rather than click-through instructions. For every case, state the expected condition before acting, retain the first useful observation and explain why the final result proves the requirement. A different program or component choice can still be correct when it produces the same bounded behavior and evidence.
Case 01
predict → observe → prove
Prove define the operating contract
Engineering context. entry edge, product identity, class, conveyor state, travel delay, divert window, actuator feedback, destination sensor, counts and reject state. For sensor-triggered package classification, divert timing, confirmation and count reconciliation, record the initial condition, actor, requested change, observable result and stopping condition before selecting a tool or implementation. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Write the acceptance case” stage of the workflow: convert entry edge, product identity, class, conveyor state, travel delay, divert window, actuator feedback, destination sensor, counts and reject state into initial conditions, one stimulus and observable pass criteria. The acceptance record should show this result: another person can repeat the case without guessing the intended result. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “The expected result is unclear” as one bounded deviation. Inspect requirement, initial state, actor, stimulus, units and pass condition The working interpretation is that the operator, programmer and reviewer may be solving different versions of the task. The next proving action is to rewrite one observable acceptance case before continuing. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is using page completion or an animation as the acceptance criterion. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: How do I time a PLC conveyor diverter? A defensible short answer is: Measure or model travel from the identification sensor to the diverter, retain the product decision and command inside a window that includes speed and actuator-response tolerances.
Case 02
predict → observe → prove
Prove map the evidence path
Engineering context. sensor event through tracking record, time or position model, classification decision, output command, mechanism response and destination confirmation. Separate request, internal state, output or service, physical or user-visible result and independent feedback so each boundary can be inspected. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Build the map” stage of the workflow: document sensor event through tracking record, time or position model, classification decision, output command, mechanism response and destination confirmation and name who owns each state or decision. The acceptance record should show this result: every request and result has a source, destination and useful inspection point. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “Internal state changes but the outcome does not” as one bounded deviation. Inspect request, final owner, output or service boundary and independent feedback The working interpretation is that a software or interface indication proves intent at one layer, not the complete outcome. The next proving action is to trace the first boundary after the changing state. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is using the same value as command, status and independent feedback. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: Why does a sortation count drift? A defensible short answer is: Common causes include level-based counting instead of edges, sensor bounce, duplicated writers, missing destination proof, manual product removal and unreconciled restart state.
Case 03
predict → observe → prove
Prove prove normal operation
Engineering context. normal products and divert products each receiving one decision and one confirmed destination without duplicated counts. Run more than one cycle from a known state and retain the values, timings or artifacts that demonstrate repeatability. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Run the baseline” stage of the workflow: apply normal products and divert products each receiving one decision and one confirmed destination without duplicated counts from a clean start and record the expected evidence. The acceptance record should show this result: repeated runs produce the same bounded result. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “Normal case passes but an edge case fails” as one bounded deviation. Inspect limits, timing, simultaneous events, reset and restart assumptions The working interpretation is that the implementation contains a hidden assumption exposed by the changed condition. The next proving action is to add the failed boundary as a permanent regression case. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is changing several parameters before a baseline exists. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: What should I learn first about sensor-triggered package classification, divert timing, confirmation and count reconciliation? A defensible short answer is: Start with the operating contract and evidence path: entry edge, product identity, class, conveyor state, travel delay, divert window, actuator feedback, destination sensor, counts and reject state, followed by sensor event through tracking record, time or position model, classification decision, output command, mechanism response and destination confirmation. Add advanced features only after the baseline is predictable.
Case 04
predict → observe → prove
Prove exercise a boundary case
Engineering context. sensor chatter, back-to-back products, missing class, late response, failed confirmation, jam, stop during transit, restart and manual removal. Choose minimum, maximum, simultaneous, delayed or restart conditions that reveal assumptions hidden by the happy path. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Challenge assumptions” stage of the workflow: test sensor chatter, back-to-back products, missing class, late response, failed confirmation, jam, stop during transit, restart and manual removal without changing the acceptance contract. The acceptance record should show this result: limits, timing and restart behavior reach defined states. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “The failure disappears after reset” as one bounded deviation. Inspect original symptom, histories, diagnostics, timestamps and active cause The working interpretation is that reset changed evidence or state without proving the initiating cause. The next proving action is to reproduce under a controlled condition and preserve pre/post-event data. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is testing only one ideal sequence. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: How do I practise sensor-triggered package classification, divert timing, confirmation and count reconciliation effectively? A defensible short answer is: Use short cases with known initial conditions, a written prediction, one action and an observable result. Then alter a boundary or fault and explain why the evidence changed.
Case 05
predict → observe → prove
Prove diagnose a controlled fault
Engineering context. an event, identity, classification, tracking, timing, output, actuator, destination, count or recovery mismatch. Preserve the first symptom, divide the system at a measurable boundary and change one condition only after predicting the result. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Isolate one failure” stage of the workflow: introduce or analyse an event, identity, classification, tracking, timing, output, actuator, destination, count or recovery mismatch and locate the first disagreement. The acceptance record should show this result: the proving action distinguishes the leading hypotheses. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “Simulator and target disagree” as one bounded deviation. Inspect model boundary, software version, task timing, I/O behavior, data types and configuration The working interpretation is that a learning model and the intended target do not share one of the recorded assumptions. The next proving action is to reduce the case and verify against current target documentation. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is resetting, forcing or replacing before evidence is retained. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: What counts as proof of competence? A defensible short answer is: A repeatable artifact or system result plus an explanation of the signal path is stronger than time spent, screenshots or a copied answer. Physical competence requires separate supervised evidence.
Case 06
predict → observe → prove
Prove transfer and hand over
Engineering context. the model compared with measured conveyor travel, actuator response, sensor placement and safe recovery on target equipment. Restore normal state, remove temporary changes, repeat affected checks and document which claims remain limited to the learning environment. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Close the evidence loop” stage of the workflow: complete the model compared with measured conveyor travel, actuator response, sensor placement and safe recovery on target equipment and repeat the affected regression cases. The acceptance record should show this result: a run is complete only when the requested behavior, stop behavior, fault response and recovery are observable from a fresh initial condition. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “The result cannot be explained” as one bounded deviation. Inspect prediction, observation, proving action, alternative hypotheses and limitations The working interpretation is that activity occurred but the evidence is not yet transferable or reviewable. The next proving action is to have the learner defend the signal path and repeat a changed case. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is treating an acknowledged message or one successful rerun as handover. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: Why test faults and restart behavior? A defensible short answer is: Because an event, identity, classification, tracking, timing, output, actuator, destination, count or recovery mismatch or sensor chatter, back-to-back products, missing class, late response, failed confirmation, jam, stop during transit, restart and manual removal can expose assumptions that never appear during ideal startup and steady operation.
Answer surface / 07
Questions people ask about PLC sortation diverter scenario
These concise answers define the operating, training and product boundaries most often missed in broad summaries. The full workflow and diagnostic table above provide the evidence behind them.
How do I time a PLC conveyor diverter?
Measure or model travel from the identification sensor to the diverter, retain the product decision and command inside a window that includes speed and actuator-response tolerances.
Why does a sortation count drift?
Common causes include level-based counting instead of edges, sensor bounce, duplicated writers, missing destination proof, manual product removal and unreconciled restart state.
What should I learn first about sensor-triggered package classification, divert timing, confirmation and count reconciliation?
Start with the operating contract and evidence path: entry edge, product identity, class, conveyor state, travel delay, divert window, actuator feedback, destination sensor, counts and reject state, followed by sensor event through tracking record, time or position model, classification decision, output command, mechanism response and destination confirmation. Add advanced features only after the baseline is predictable.
How do I practise sensor-triggered package classification, divert timing, confirmation and count reconciliation effectively?
Use short cases with known initial conditions, a written prediction, one action and an observable result. Then alter a boundary or fault and explain why the evidence changed.
What counts as proof of competence?
A repeatable artifact or system result plus an explanation of the signal path is stronger than time spent, screenshots or a copied answer. Physical competence requires separate supervised evidence.
Why test faults and restart behavior?
Because an event, identity, classification, tracking, timing, output, actuator, destination, count or recovery mismatch or sensor chatter, back-to-back products, missing class, late response, failed confirmation, jam, stop during transit, restart and manual removal can expose assumptions that never appear during ideal startup and steady operation.
Can browser practice replace official software or hardware?
No. It can build concepts and diagnostic reasoning. Exact firmware, I/O electrical behavior, networking, safety and commissioning require current official tools, documentation and target equipment.
How should progress be documented?
Keep the requirement, initial state, program or configuration, observed values, fault hypothesis, proving action, recovery result and a concise limitations statement.
Continue the signal path / 08