Worksheet

Predictive Maintenance Sensor Mapping & Priority Worksheet

A practical, saveable worksheet to map assets, failure modes, candidate signals, expected lead time, pilot readiness, and a simple priority score. Use single-row structured entries (good for stepwise pilot planning) or paste a bulk CSV (good for scanning many assets). Stores submissions for later review and dashboards.

Interactive Tool

Predictive Maintenance Sensor Mapping & Priority Worksheet

Use this worksheet to identify assets suitable for predictive-maintenance pilots, record dominant failure modes, candidate signals, estimated lead times, and a simple priority score. You can enter one structured asset at a time (handy for focused planning) or paste a bulk CSV of many assets. Read the scoring guidance below before submitting.

Scoring guidance

Priority combines equipment criticality, expected lead time (how early a signal appears before failure), and pilot readiness (data availability, access, and maintenance process). Use the 0–10 priority scale where 10 is highest priority for a pilot. Suggested quick formula: Priority ≈ (Criticality weight) + (Lead-time weight) + (Readiness weight). See the example below.

Example

Asset: Compressor A
Criticality: Critical (4)
Dominant failure: Bearing wear
Candidate signals: Vibration RMS (high freq), temperature trend, oil-particle count
Expected lead time: 7–14 days
Pilot readiness: Partially Ready (sensor available but historical data limited)
Priority (0–10): 8 — good candidate for a short-term pilot.

Choose how you want to add asset data: single structured row or bulk CSV paste.
Human-friendly name. e.g., 'Compressor A (Line 2)'. Leave blank if using bulk paste.
Unique asset identifier in your CMMS or asset registry. Leave blank if using bulk paste.
Operational impact if the asset fails. Choose the option that best fits. You can map these to numeric weights when scoring.
List the primary ways this asset fails (bearing wear, seal leakage, electrical winding insulation, misalignment, clogging, etc.).
Potential predictive signals to monitor. Give examples and sensor types, e.g., 'vibration RMS 1–10 kHz, high-frequency envelope,-bearing temperature, acoustic emission, motor current signature, oil debris count, pressure drift'.
Practical suggestion (e.g., 1s burst every minute, continuous 10 kHz, hourly temperature), helpful to plan sensor and storage needs.
Who will own the sensor data, field engineer or system team? Include name, role, or team.
How many days before a typical failure you expect to see an actionable signal. Enter an integer if known. Leave blank if unknown.
How ready this asset is for a pilot given access, data availability, and maintenance process maturity.
Your judgment of overall priority for a predictive-maintenance pilot. 0 = lowest priority, 10 = highest. Use the scoring guidance above.
1.0 10.0
Anything else to record: required access, supplier constraints, expected pilot duration, estimated cost, risks, or links to work orders and failure history.
If you have many assets, paste CSV with header row. Required columns (recommended order): asset,asset_tag,criticality,dominant_failure_modes,candidate_signals,sampling_rate_suggestion,data_owner,expected_lead_time_days,pilot_readiness,priority_score,remarks. Example row: Compressor A, C-A-01, critical, bearing wear, vibration RMS|temp|oil, continuous 10kHz, Reliability Team, 7, partially_ready, 8, 'easy access'. If CSV is used, single-row structured fields may be left blank.
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