Orchestrates vibration analysis workflow for bearing fault diagnosis using predictive-maintenance-mcp server: statistical screening, FFT, characteristic frequencies, envelope analysis.
How this skill is triggered — by the user, by Claude, or both
Slash command
/predictive-maintenance:bearing-diagnosisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Evidence-based bearing fault detection using vibration signals. Orchestrate MCP
Evidence-based bearing fault detection using vibration signals. Orchestrate MCP tools in a precise diagnostic sequence with decision gates at each step.
Prerequisite: The predictive-maintenance-mcp MCP server must be connected.
Call list_stored_signals() to check cached signals, or list_signals() to
browse the data/ directory. If no signal is loaded, ask the user for a file path
and call load_signal(file_path=..., signal_id=...). Ask which signal to
analyze if multiple are available.
Call extract_features_from_signal(signal_id=...).
Evaluate screening flags (excess kurtosis, Fisher convention):
| Indicator | Threshold | Meaning |
|---|---|---|
| Kurtosis > 0 | Mild | Non-Gaussian content (possible impulses) |
| Kurtosis > 3 | Moderate | Significant impulsive content |
| Kurtosis > 6 | Severe | Likely bearing damage |
| Crest Factor > 4 | Mild | Impulsiveness present |
| Crest Factor > 6 | Strong | Strong impulsiveness |
Decision gate: If Kurtosis < 0 AND Crest Factor < 3 the signal shows no impulsive content. Report "No bearing fault indicators in time-domain screening" and ask if the user wants to continue anyway.
Call analyze_fft(signal_id=...).
Identify:
If operating speed is unknown, ask the user for RPM or shaft frequency.
If bearing designation is known, call
lookup_bearing_and_compute_tool(bearing_query=..., shaft_rpm=...) to look up
the bearing in the catalog AND compute fault frequencies in one step.
Otherwise call calculate_bearing_characteristic_frequencies(...) with manual
geometry (n_balls, d_ball, d_pitch, contact_angle, shaft_rpm).
If the bearing designation is unknown, ask the user or try
extract_manual_specs(manual_name=...) to pull it from a machine manual.
Expected frequencies:
Call analyze_envelope(signal_id=..., filter_low=..., filter_high=...).
Adjust the filter band based on Step 3:
Call plot_envelope(...) for a visual report.
Compare envelope peaks against characteristic frequencies using
check_bearing_faults_direct(signal_id=..., shaft_rpm=..., ...).
| Peak matches | Harmonics | + High Kurtosis | Diagnosis |
|---|---|---|---|
| BPFO +/- 2% | 2x, 3x present | Yes | Possible outer race fault |
| BPFI +/- 2% | + sidebands at shaft freq | Yes | Possible inner race fault |
| BSF +/- 2% | 2x present | Yes | Possible ball defect |
| FTF +/- 2% | Irregular spacing | Moderate | Possible cage fault |
| No matches | — | — | Inconclusive |
Confidence levels:
Call evaluate_iso_20816(signal_id=..., machine_group=2, support_type="rigid").
Report vibration zone (A/B/C/D) and urgency level. Confirm signal units with the user before calling — wrong units invalidate the result.
Call generate_envelope_report(...) and generate_fft_report(...).
Optionally call generate_diagnostic_report_docx(...) for a full DOCX report.
Inform the user of report file locations.
No peaks in envelope spectrum — Wrong filter band or signal too short. Try a wider band (200–10000 Hz) or analyze the full signal without segmentation.
Unknown bearing type — Ask for bearing designation (e.g., SKF 6205) and call
search_bearing_catalog(). If unavailable, check the machine manual with
extract_manual_specs().
Kurtosis high but no envelope peaks — Impulses may come from a non-bearing source (gear mesh, electrical). Suggest using the gear-diagnosis skill or check for electrical frequencies (line frequency harmonics).
npx claudepluginhub lgdimaggio/predictive-maintenance-mcp --plugin predictive-maintenanceDiagnoses gear faults from vibration signals using spectral analysis of GMF harmonics, sidebands, envelope demodulation, spectrograms, PSD, and ISO 20816 evaluation via predictive-maintenance-mcp server.
Systematically identifies failing suspension and steering components through symptom analysis, visual inspection, and component-specific tests to prevent misdiagnosis.
Characterizes RF spectrum captures using PSD, signal detection, and spectrogram analysis to identify and summarize signals on the air.