Fleet Finding Matcher

Matches new maintenance findings to similar findings across the fleet.

Industry	Level	Difficulty	Recommended mode
Aerospace	3 of 5	Advanced	flat

Coming soon

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

The problem

• Findings on one aircraft recur on others.
• Records are siloed.
• Fixes are rediscovered.

How SrvDB powers Fleet Finding Matcher

1. Findings are embedded with tail number and fix.
2. New findings retrieve similar fleet findings.
3. Snapshots shared between bases.
4. Updated continuously.

What changes

Today	With Fleet Finding Matcher on SrvDB
Per-aircraft records	Fleet view
Rediscovered fixes	Known fixes
Base silos	Shared snapshots

How it works

1. Embed findings.
2. Match new ones.
3. Show fixes.
4. Share snapshots.
5. Track trends.

SrvDB features it uses

• Exact search (flat): every query compares against every stored vector, so results are exact and explainable.
• 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.
• Several databases: each database is a directory, so different data types or tenants stay separate.

Use cases

• Corrosion findings
• Avionics faults
• Engine findings
• Structural issues
• Cabin defects
• Repeat defects

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("./fleet-finding-matcher-db", dimension=384)

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

Limits and honest notes

• Engineering approves actions.

Caution

SrvDB is not certified software. Use it for decision support and knowledge retrieval, never in flight-critical or certified systems.

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 Aerospace projects

Level	Project	What it does
1 · Beginner	Maintenance Knowledge Assistant	Cited answers from manuals and service bulletins for technicians.
2 · Intermediate	Safety Report Analyzer	Clusters safety reports and links similar events.
3 · Advanced	Fleet Finding Matcher (this page)	Matches new maintenance findings to similar findings across the fleet.
4 · Expert	Borescope Image Search	Find similar borescope images and the findings they led to.
5 · Frontier	Mission Knowledge System	Offline mission and ground-segment knowledge for space operations.

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