Predictive Maintaince (PdM) with Machine Learn- ing (ML) techniques is becoming increasingly vital for industrial equipment upkeep. The collection of comprehensive data from sensors and devices installed on machinery, ensuring both quality and quantity of data. Relevant features are then extracted from this data to build predictive models.
active 2024-05-03 → 2025-04-23 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 356 days from 2024-05-03 to 2025-04-23. Pushes: 3 total, peak 3 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 1 total, peak 1 in a day. Comments: 1 total, peak 1 in a day. Stars: 1 total, peak 1 in a day.
- Pushes
- Pull requests
- Issues
- Comments
- Stars
Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| Saima-786 | 5 | 3 | 0 | 1 |
Recent activity
Latest issues, pull requests and releases
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 1 stars here means stars gained during the window, not the repo's star count.