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CLUSTER MAINTENANCE-SENSOR-WEB · 4 ENTRIES · EARLIEST ENTRY 2026-08-14

Railway sensor maintenance analytics

This cluster covers web applications that analyze sensor data from railway tracks and train subsystems (doors, HVAC, rail corrugation, structural health) to detect faults and predict maintenance needs. The dated history before the index start is too thin to read a shape from (0 dated entries), so the cluster predates the index or its members are undated. PS3-App leads on traction with 2 stars.

Entry timeline

ENTRY TIMELINE

AUG 2026: 2SEP 2026: 2
AUG 2026SEP 2026

Each bar is the month a member carries, which is a creation date where the source published one and a crawl date otherwise. Nothing in the index was found before 11 August 2026, and coverage broadened sharply on 14–15 August, so a bar from then on reports how much we found that month, not how much was built; an earlier bar reports how far back a member's own source dated it — not how long we have been watching.

Members by activity

Activity signals, not revenue.

Build origin · 4 unlabelled

Operator · 3 hobby · 1 unlabelled

Two labels per entry, never collapsed into one: how it was built, and who stands behind it. Counted over the 4 listed below. Unlabelled means nothing has decided yet — not that the answer is small.

  1. 01PS3-App2★1 mo—hobbyWeb app for railway maintenance staff that analyzes sensor recordings from door, air conditioning, rail corrugation, and structural health subsystems and outputs condition reports.
  2. 02hackathon0★22 days—hobbyWeb app for train maintenance teams that uploads sensor recordings, reviews predicted subsystem faults, and downloads results.
  3. 03railway_qr_fittings1★2 mo—Web app that uses QR code scanning to identify railway track fittings and track their inspection, maintenance, and warranty records.
  4. 04Railway_Health_Predictor1★2 mo—hobbyWeb dashboard that analyzes railway track and vehicle sensor data, including vibration and temperature readings, to detect faults and predict maintenance needs.

Ranked by traction, highest first, and paged 20 at a time — this is a window on the cluster, never the whole of it. Ties are broken by a stable id so a product cannot appear on two pages or fall between them. Browse every cluster.