Sold by: Sia Partners
AI-driven predictive maintenance enables energy and utilities companies to predict equipment failures before they occur, reducing downtime and cutting maintenance costs. By analyzing sensor data from assets like turbines and power grids, AI algorithms identify patterns that signal potential issues, allowing for timely repairs and extending the lifespan of critical infrastructure. This proactive approach enhances operational efficiency, improves reliability, and optimizes resource use, making it a key strategy for reducing costs and boosting performance in the industry.
Sold by: Sia Partners
Overview
With our AI-powered tools, we can leverage sensor data, other data sources, and operational patterns to accurately predict when equipment is going to fail, enabling timely interventions that minimize downtime, reduce maintenance costs, and extend asset life, while ensuring operational efficiency and enhancing overall system reliability.
Use cases:
- Predicting Electricity Network Failures: Improve network resilience by pro-actively repairing key assets on the network.
- Optimize Maintenance: Reduce costs by optimizing maintenance schedules based on asset risk profiles.
- Determine Long-Term Investment Strategy: Make data-driven decisions on when to invest in infrastructure or upgrades, optimizing capital allocation and extending asset life.
- Optimize inventory management: Accurately predict the need for spare parts to maintain the right stock levels & reduce excess inventory.
Our Success Stories:
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Predictive maintenance based on weather forecasting We implemented a solution to mitigate weather-related risks on the electricity network by predicting outages using weather forecasts. This enables the network to optimize resource and equipment deployment, reducing outages, improving network resilience and enhancing customer satisfaction.
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Predictive maintenance for a transport company– Time Series We developed and deployed a comprehensive predictive maintenance solution, integrating an NLP model into a client's platform for efficient data extraction and event prediction. Using agile methodology, the team built a system architecture with batch inference tasks, production testing APIs, and a model re-training process. This approach improved predictive accuracy and operational efficiency, enhancing the client's data management and forecasting capabilities.
We are working on various AWS infrastructures (Bedrock, Q, SageMaker...) and our products rely on differents AWS services when creating and deploying a platform or a software for a customer.
Highlights
- Predictive Maintenance & Reduced Downtime: With AI-powered predictive maintenance, we leverage sensor and operational data to foresee equipment failures, allowing proactive interventions that minimize downtime and reduce maintenance costs, ensuring both efficiency and reliability.
- Optimized Resource Allocation & Investment: Our tools facilitate data-driven decisions for long-term investment strategies, optimizing capital allocation, and supporting maintenance schedules based on risk profiles, extending asset lifespans and enhancing financial planning.
- Enhanced Inventory Management: By accurately forecasting spare part needs, we maintain optimal stock levels, reducing excess inventory, and streamlining inventory management, ultimately saving on storage costs and improving operational readiness.
Details
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