Resources
Reliability knowledge—including AI and Machine Learning—for better decisions.
Short, practical notes that explain how condition information and outputs from AI and Machine Learning become useful—and where common interpretation mistakes begin.
Why operating condition matters
A change in speed, load, process, or mounting can change the vibration signature. Comparable measurements make trends more meaningful.
Read field note →Screen first, diagnose second
Ultrasound can quickly identify exceptions. Confirmation and context are still needed before deciding what the signal means.
Read field note →Partial discharge is a risk indicator
Detection is the beginning of an assessment—not a complete diagnosis. Location, pattern, trend, and asset context shape priority.
Read field note →Use electrical and mechanical evidence together
Motor symptoms can originate in supply, load, rotor, stator, bearings, coupling, or process conditions. Combined evidence reduces blind spots.
Read field note →A sample is only as good as the method
Sampling location, cleanliness, timing, and handling all affect whether oil-analysis results represent the equipment.
Read field note →More sensors do not automatically mean more insight
Monitoring succeeds when sensor placement, thresholds, workflow, ownership, and response are designed together.
Read field note →Trustworthy use of AI and Machine Learning starts before the model
Define the decision, consequence, data quality, validation evidence, human oversight, and model-monitoring plan before scaling.
Read field note →Quick glossary
Reliability terms, including AI and Machine Learning.
- RCM
- Reliability-centered maintenance: selecting strategies around functions, failures, and consequences. Read definition →
- FMEA
- Failure modes and effects analysis: a structured review of how an asset can fail and what follows. Read definition →
- Anomaly detection
- Finding behavior that differs from an established or learned operating pattern. Read definition →
- Model drift
- Performance risk created when equipment, process, data, or operating behavior changes. Read definition →
- Human in the loop
- Qualified review, challenge, override, and accountability inside a workflow supported by AI and Machine Learning. Read definition →
These resources are general educational information and are not a substitute for an equipment-specific engineering or safety assessment.
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