AI-based OTDR Testing Automation

1. To design and implement an AI-powered solution that integrates with OTDR testing systems.

2. The system will automatically identify and classify fiber, tube, and cable colors across diverse Optical Fiber Cable (OFC) types, improving accuracy, speed, and reliability in OTDR testing of various stages of operations.

The AI-based OTDR testing system is installed and running at our Hyderabad plant on a dedicated NVIDIA Jetson AI unit. It was commissioned and tested on site on 10–11 August 2026. Multi-Tube and IBR cables were tested end to end; Flat Ribbon testing is to be completed by our QA team.

1. Ribbon ID exact match 96.5%.

2. 96.2% fiber color matching.

3. 95.4% tube color matching.

1. High resolution camera - alvium c2460 with 25mm lens

2. NVIDIA Jetson Orin NX

3. Standalone AI server (A VM with Ubuntu 24.04.5, 8 vCPU, 1TB storage, 32 GB RAM)

4. New workstations with >90 CRI LED lighting

5. Vimba X Module

6. Docker, Node.js, SQL server

1. Ring marking identification on the fibers.

2. Color and marking on OFC having 6912 fibers.

AI Model Development

·       Training computer vision models to recognize fiber/tube/cable colors under varied conditions.

·       Incorporating global standards (e.g., TIA-598 color codes) and HFCL-specific variations.

Data Acquisition

·       Collecting high-resolution images of fibers/tubes/cables for all types of OFCs manufactured at HFCL Plants across locations.

·       Building a labeled dataset for supervised learning.

OTDR Integration

·       Synchronizing color identification with OTDR event data (faults, splices, reflections).

·       Enabling color-coded mapping of fiber paths in OTDR reports.

User Interface

·       Developing a technician-friendly dashboard.

·       Features: OTDR trace visualization, color-coded fiber mapping, automated reporting.

Reporting & Documentation

·       Exportable reports with visual color identification.

Our plants in 3 locations Hyderabad, Chennai and Goa.