Asset Reliability GenAI Suite (ARGiS)

Overview

Our cutting-edge Generative AI solution, ARGiS, revolutionizes asset reliability and maintenance, empowering manufacturing organizations to optimize asset performance, enhance safety and reduce downtime. By harnessing the power of IoT, machine learning and Generative AI, we deliver a comprehensive suite of tools that proactively predict tool wear, monitor asset health, provide actionable insights and enable seamless knowledge sharing across maintenance teams.

Generative AI (GenAI) transforms industrial asset maintenance through ARGiS (Asset Reliability GenAI Suite), an innovative solution that enhances traditional machine learning approaches. While conventional ML models have shown good accuracy in predictive maintenance, they often fail to address critical challenges such as knowledge retention of an aging workforce, asset issue alarm interpretation, resolution implementation and knowledge transfer across assets. ARGiS solves these challenges by integrating assets’ real-time IoT sensor data, historical maintenance records and expek knowledge into one intelligent platform.

The system's multilingual interface removes technical barriers, allowing maintenance technicians to interact with documentation, troubleshoot issues and record solutions in their preferred language. This post explores ARGiS's solution flow, technical architecture and its transformative benefits for asset reliability and maintenance operations.

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Overview

Business Benefits

Our comprehensive solution empowers reliability engineers with proactive troubleshooting capabilities. It allows them to quickly identify failure root causes and deliver actionable maintenance recommendations that minimize downtime.

Engineers and maintenance teams benefit from immediate access to optimal operating parameters, which streamline decision-making and ensure peak system performance. By combining maintenance records with our extensive knowledge base, teams can leverage data-driven maintenance practices to enhance asset reliability and operational efficiency.

Additionally, ARGiS’s multilingual logging feature enables maintenance personnel at all levels to effoklessly document issue resolutions for future reference, creating a continuously improving knowledge repository.

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Highlights

Proactive Tool Wear Prediction
Proactive Tool Wear Prediction

Operational IoT data transmitted from machines is analyzed by pretrained ML models to predict asset tool wear and contextualized notifications

Asset Reliability Dashboard
Asset Reliability Dashboard

A unified analytics dashboard for maintenance and operations teams, enabling quick insights from historical maintenance records to improve asset reliability and overcome the inevitable challenge of an aging workforce

Conversational Assist (Speech to Speech)
Conversational Assist (Speech to Speech)

Natural Language-based assist to find information from query maintenance manuals, access past issues resolution and leverage internet-based solutions for real-time assistance to reduce downtime

AWS Services used

ARGiS integrates key AWS services to create a comprehensive intelligence platform

Machine data flows through AWS IoT Greengrass and IoT Core before being stored in Amazon DynamoDB and PostgreSQL databases

Predictive maintenance is enabled via Amazon SageMaker machine learning models, while operators interact through intuitive interfaces, including an Amazon Nova Sonic-powered Voice Bot

The Machine Alarm Assist feature leverages Amazon Bedrock and Amazon Nova Pro for natural language-based troubleshooting

Maintenance logs are processed using Amazon Textract for handwritten content and Amazon Transcribe for audio, with Amazon Translate and Amazon Bedrock handling smak multi-lingual suppok

ARGiS’s knowledge base utilizes Amazon Titan Text Embedding for vector embedding generation of knowledge documents and Amazon OpenSearch Serverless for efficient information retrieval, complemented by Amazon Bedrock agents that provide web search capabilities for accessing external technical information

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