EchoTrace

Inspiration

Industrial incidents rarely come with a single source of truth. A machine failure may leave evidence across CCTV footage, sensor telemetry, audio, and machine logs. Manually analyzing these sources and matching their timestamps can be slow and difficult, especially when an incident happens within a short time window.

We built EchoTrace to address this problem by bringing heterogeneous evidence together and reconstructing a probable sequence of events.

What We Built

EchoTrace is a multimodal AI prototype for industrial event reconstruction. It combines video, sensor telemetry, audio, and machine logs, synchronizes their timestamps, detects important events, and connects related evidence into a unified incident timeline.

The system separates observed evidence from AI-inferred relationships and provides confidence information so that users can understand why a particular sequence was reconstructed.

How It Works

Camera + Sensors + Audio + Machine Logs
→ Event Detection
→ Timestamp Normalization
→ Multimodal Event Fusion
→ Temporal Reasoning
→ Incident Timeline
→ Evidence Graph
→ Incident Reconstruction

The dashboard provides synchronized telemetry, detected events, keyframe evidence, an interactive temporal/causal graph, observed facts, inferred sequences, uncertainty boundaries, and ranked diagnostic hypotheses.

Technical Approach

The prototype uses computer vision and machine-learning based event detection together with structured sensor and machine-log analysis. Different evidence streams are normalized using their timestamps and then correlated to identify events occurring within the same incident window.

Instead of treating every relationship as certain, EchoTrace explicitly distinguishes observed facts from inferred relationships and attaches evidence and confidence information to the reconstruction.

Challenges

The main challenge was handling heterogeneous data sources that have different formats, sampling rates, timestamps, and levels of information. Another challenge was presenting AI-generated conclusions in a way that remains traceable to the original evidence.

What We Learned

We learned how multimodal systems can combine different types of evidence into a unified representation and how temporal alignment can help connect events across independent data streams.

Future Scope

EchoTrace can be extended with larger real-world industrial datasets, more advanced multimodal models, real-time streaming inputs, improved anomaly detection, and integration with industrial monitoring systems.

EchoTrace is designed as a decision-support and incident-analysis prototype rather than a system that claims certain causality or autonomous safety-critical decisions.

Built With

• ai
• computer
• data
• fastapi
• flow
• javascript
• learning
• machine
• multimodal
• numpy
• opencv
• pandas
• python
• react
• recharts
• scikit-learn
• sqlite
• vision
• vite
• yolo