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Predictive Maintenance — Sensor Selection & Signal Readiness Checklist

A checklist to evaluate which assets and signals are good candidates for predictive maintenance pilots and which engineering actions are needed first.

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Predictive Maintenance — Sensor & Signal Readiness Audit

Welcome — what this audit helps you do

This interactive audit helps maintenance and reliability teams capture structured, repeatable assessments for a specific asset (or asset class) to decide whether it's a practical candidate for a predictive maintenance pilot. Save responses to create an audit trail, compare assets, and prioritize pilots.

How to use this form

  1. Complete one audit per asset (or asset class). Answer each question and add brief notes where helpful.
  2. Use the Quick Readiness Score field at the end — instructions are included on how to calculate it from the Yes/Partial/No answers.
  3. Target assets that combine high criticality and good technical readiness.

This interactive audit preserves the checklist guidance but records specifics you can review later.

Unique identifier or tag for the asset.
Short description (pump, motor, gearbox, compressor, press, etc.).
Where the asset is installed (building, line, cell).
Rate how critical this asset is to production, safety, or cost. Add notes with examples of lost hours or scrap where helpful.
Examples: bearing wear, imbalance, seal leaks, valve sticking.
Briefly list or link to documentation. Leave blank if not applicable.
Good history helps label and validate model outputs.
Where is the log stored? How searchable is it?
Consider vibration, temperature, current, pressure, acoustic, oil condition, PLC signals.
Choose one or more based on failure mode.
Optional.
Record planned sampling rates and rationale (e.g. vibration 8kHz, temp 30s).
Include a photo reference or location for the saved image below.
If your system doesn't store files here, paste a link or reference where the photo is saved.
Note connector types and IP ratings if known.
Consistency helps model generalization.
Target percent of successful captures during the pilot window (e.g., 95).
Document clock drift handling if applicable.
Visual check of time series and spectra recommended.
If no, capture examples and consider mounting/cabling or sensor upgrades.
Metadata is essential for good modeling and auditability.
Example: inspect -> record -> hold -> repair playbook.
This enables labeling and validation.
Labeling is often required to tune models.
Small, focused pilots reduce wasted spend.
Record specifics if available.
Helps with security and vendor sharing decisions.
Define retention length and permitted sharing.
How far in advance should the model ideally flag the issue? Choose realistic values based on asset.
A measurable target for the pilot period.
Document whether recall or precision is prioritized and acceptable bounds.
E.g., 95 for planned captures during the pilot window.
E.g., 80 means 80% of alerts lead to useful actions.
High-level expected benefit that would justify scaling if achieved.
E.g., Missing failure history -> start manual failure-capture protocol; Poor signal quality -> re-evaluate mounting or upgrade sensor.
Select items you've captured.
Select items you will do next if you proceed.
Score each radio or select item as: Yes = 1, Partial = 0.5, No = 0. Sum the scores and divide by the number of applicable scored items to get a readiness percentage. >=80% = Ready; 50-79% = Remediation recommended; <50% = Not ready.
Enter the percentage calculated using the instructions above. This field is stored for comparison across audits.
Add any short actions or observations for stakeholders.
You can explore this tool now. Sign in or create an account to save your responses and return to them later.
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