Vibration Signature Matcher

Matches vibration signatures to known fault patterns.

Industry	Level	Difficulty	Recommended mode
Mechanical engineering	3 of 5	Advanced	sq8

Coming soon

A complete, runnable Vibration Signature Matcher reference implementation is in progress. The architecture and core code below already work with SrvDB 0.3.

The problem

• Vibration analysis needs experts.
• Fault libraries are tribal knowledge.
• Monitoring data is huge.

How SrvDB powers Vibration Signature Matcher

1. Spectra are converted to feature vectors and stored in SrvDB.
2. New measurements retrieve similar known faults.
3. Distance from healthy signatures flags anomalies.
4. sq8 keeps libraries small.

What changes

Today	With Vibration Signature Matcher on SrvDB
Expert-only analysis	Assisted matching
Tribal fault libraries	Shared library
Data overload	Compact vectors

How it works

1. Compute spectral features (at least 128 values).
2. Store labelled signatures.
3. Match new data.
4. Review.
5. Grow the library.

SrvDB features it uses

• SQ8 compression (sq8): stores each value in one byte, 4× smaller than full precision.
• True cosine scores: scores in [-1, 1] mean the same in every mode, so thresholds are stable.
• upsert: inserts new items and replaces changed ones, so the knowledge base stays current.
• Crash-safe persist(): after it returns, data survives a crash or power loss and reopens exactly as committed.

Use cases

• Bearing faults
• Imbalance
• Misalignment
• Gear wear
• Looseness
• Resonance

Core code

embed() is your embedding model (for example sentence-transformers, or a function that turns your records into feature vectors).

import json
import srvdb

db = srvdb.Database("./vibration-signature-matcher-db", dimension=128, mode="sq8")

def ingest(items):
    """items: list of (id, text, metadata dict)"""
    db.upsert(
        ids=[i for i, _, _ in items],
        embeddings=[embed(t) for _, t, _ in items],
        metadatas=[json.dumps(m) for _, _, m in items],
    )
    db.persist()                      # crash-safe checkpoint

def known_faults_like(signature, k=5):
    hits = db.search(embed(signature), k)
    return [(json.loads(db.get(i)), round(score, 3)) for i, score in hits]

Limits and honest notes

• Feature engineering drives accuracy.

Build roadmap

1. Prototype: load a sample of real data with flat mode and check that searches return what an expert expects.
2. Measure: if you move to an approximate mode, compare its results with flat on the same queries (recall).
3. Pipeline: add continuous upsert and decide when to call persist().
4. Harden: test restarts and crashes, add logging, review every output path with domain experts.
5. Deploy: ship the database directory with the application or service.

All Mechanical engineering projects

Level	Project	What it does
1 · Beginner	Part Finder	Find existing parts by describing what they do.
2 · Intermediate	FMEA Assistant	Drafts and reviews FMEAs grounded in past FMEAs and failures.
3 · Advanced	Vibration Signature Matcher (this page)	Matches vibration signatures to known fault patterns.
4 · Expert	Geometric Design Search	Search CAD models by shape using 3D shape embeddings.
5 · Frontier	Simulation Lookup	Find the nearest previously simulated cases before running a new simulation.

See also: Use cases · Live examples (coming soon)