AI/ML based Advance Analytics Systems

C2C Advanced Systems: Delivering excellence through innovations

Predictive Maintenance

AI/ML-based system aimed at the transition from Reactive & Preventive Maintenance to Predictive Maintenance

• Estimation of ‘Time to Failure’ of a machine based on Machine Learning Models

• Detection of anomalies in running machinery

• Classification of different types of faults

• Estimation of “Remaining Useful Life’ of various systems and equipment

Intelligent Threat Tracking

Enhancement of existing Threat Evaluation capability through incorporation of advanced Machine Learning Models

• Training of the model on existing threat signatures

• Use of real-time data from on-board sensors and Combat Management System (CMS) for identification of threatening targets

• Threat classification based on target history and present behavior

Classification of Navigational Objects

Use of recent advancements in Computer Vision and AI/ML technologies to enhance safe navigation of Naval ships and submarines

• Use of Video Cameras for Object Detection and Classification

•Data fusion of Radar, AIS, and Camera input feeds

• Track Identification (IFF)

• Enhanced Force Protection Measures using AI / ML

• Considerable improvement in ship handling capabilities during Station Keeping, Replenishment at Sea, and Entering/ Leaving Harbour

RAG Based ChatBoT

Incorporates latest advances in Generative AI and ChatBoTs for use by defense forces using captive information sources

• Training of the Retrieval-Augmented Generation (RAG) based model on domain-specific data

• Intelligent question–answer system based on trained data

• Enhanced capability for defect identification and defect rectification onboard ships, submarines, and forward positions

AI Enabled Autonomous Vehicle

Integration of different capabilities, viz. sensor interface, data fusion, smart navigation etc. into a system capable of Autonomous Navigation

• Integration of prime movers and other sensors into a consolidated model

• Use of sensors to build domain awareness of the platform in real-time

• Use of AI/ML to predict and recommend a course of action

• Integration of actions with actuators

• Refine and re-orient based on responses and feedback