Continuous structural monitoring streams millions of rows of continuous strain, stress, and acoustic data that break standard analytics infrastructure. This project delivers a high-fidelity data analysis pipeline that processes continuous sensor data, applying physical engineering principles to catch structural micro-deviations before they lead to mechanical failure.
Steps:
Sensor Signal Cleaning & Diagnostics
Import the raw sensor logs, isolate and remove background noise or faulty sensor spikes, and align the timestamps to ensure data consistency across all monitoring points.
Physical Stress & Strain Feature Extraction
Convert raw data into critical engineering indicators. Calculate cumulative material fatigue, isolate peak load events, and analyze structural vibration frequencies.
Automated Structural Anomaly Detection
Develop and train a targeted machine learning model to continuously screen data streams, automatically flagging micro-deviations or structural deformations before they pose a safety risk.