Choose a CNC inspection order from three HIGH alerts
Act as a fictional FabriSense maintenance specialist and use sensor quality signals, health scores, and machine relationships to order three CNC inspections.
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A maintenance shift with three simultaneous alerts
The morning shift has started at the fictional CNC manufacturer FabriSense. CNC-03, CNC-05, and
CNC-09 all show HIGH alerts. There is only one maintenance crew, so they need a first stop without
discarding the other two warnings.
Today, you are the FabriSense maintenance specialist answering this question:
Which CNC machine should we inspect first to prevent a failure, and what evidence supports that order?

Decision criteria for this mission
- Perspectives in play — Maintenance analysis determines whether each alert came from a statistical threshold or an explicit condition signal. Field operations chooses the first stop for one crew while preserving the remaining inspection scope.
- Decision constraints —
CNC-03,CNC-05, andCNC-09are allHIGH, and each has a health score of 90. Choosing a first machine does not justify returning the other two to normal operation. - Completion signals — Explain the source of all three alerts, use the published Workshop tie-break
rule to place
CNC-03first, and leave a maintenance-event relationship that the next specialist can retrace.
Workshop goals
- Read sensor values together with
quality_flagto identify the source of an alert. - Explain in plain language what a three-sigma check finds.
- Compress three anomaly rows into machine health scores and an inspection order.
- Recheck the decision through machine, sensor, and maintenance-event relationships.
- Separate an operational policy from the workshop's tie-break rule.
Seven decisions
Loading the diagram. Mermaid source:
flowchart LR
accTitle: Seven decisions for ordering CNC inspections
accDescr: Start with three alerts, read the sensor evidence and three-sigma rule, set the inspection order, trace machine context, check the core reasoning, and explain the maintenance decision.
warnings["1. Confirm three alerts"] --> evidence["2. Read sensor evidence"]
evidence --> sigma["3. Understand 3-sigma"]
sigma --> order["4. Set inspection order"]
order --> context["5. Trace machine context"]
context --> check["6. Check understanding"]
check --> explain["7. Explain the decision"]Each chapter follows question → reason → action → observation → interpretation → next decision. Instead of memorizing every asset, narrow the evidence needed to choose the first inspection.
Before you begin
- An engineer or maintenance-analyst account with Editor access or higher
- Enough download space for a scenario ZIP of about 25 KB
- About 90 minutes
You do not need a terminal, Python, or a clone of dhub2-examples. If scenario import is new to you,
start with Import and tour a complete hands-on scenario.
Learning journey
- Start with three HIGH alerts15 minImport the IoT scenario and separate the evidence needed to order three machine inspections.
- Read sensor values and quality signals together15 minInspect 20 sensor rows and trace the three HIGH alerts to their source values, units, and quality signals.
- Understand the three-sigma check intuitively15 minCompare each sensor with its normal distribution and distinguish a statistical alert from an explicit quality signal.
- Turn alerts into an inspection order20 minCompress three HIGH alerts into machine health scores and use the operations dashboard to choose a first inspection.
- Trace machine, sensor, and maintenance-event context15 minMaterialize the IoT ontology and inspect sensor configuration separately from the maintenance event created by a HIGH alert.
- Check the core maintenance reasoning5 minBefore explaining the final decision, review the alert source, sigma rule, machine relationships, and inspection scope in four questions.
- Explain the maintenance decision with evidence5 minReturn to the shift question and explain both the inspection scope and the first stop among three HIGH machines.