Back to papers
0
Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Sign in to save

Edge-Based Predictive Maintenance for Subsonic Wind Tunnel Systems Using Sensor Analytics and Machine Learning

2025 1 citation ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 43 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Shohanur Rahaman Sunny

Summary

Despite its title referencing wind tunnel maintenance and sensor analytics, this paper studies an edge-computing machine learning system for predicting equipment failures in wind tunnel testing facilities — not microplastic pollution. It examines vibration and thermal sensor data for predictive maintenance and is not relevant to microplastics or human health.

This paper presents a practical, low-cost predictive maintenance system for subsonic wind tunnel facilities, leveraging edge-based sensor analytics and machine learning. The system integrates real-time data from vibration and thermal sensors deployed

Read via DOI Download PDF

Share this paper

Post Share Share Pin Email