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Automatic Failure Detection:
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We develop integrated virtual inspection platforms powered by Artificial Intelligence, ensuring both your business performance and environmental sustainability

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Automatic Failure Detection, Artificial Intelligence Applied

The challenge lies in developing a service that, starting from the acquisition of aerial images of any infrastructure monitorable through images, is capable of analyzing the "health" status of the infrastructure itself. Specifically, this involves processing a "large" amount of data through intelligent cognitive agents based on machine learning, deep learning, and computer vision techniques. These agents will support and assist monitoring personnel in inspection activities, allowing for the interception, scheduling, and prioritization of maintenance interventions.

Let's do Predictive Maintenance! The solution in phases.

The development of a virtual infrastructure inspection platform, integrated with automatic failure recognition capabilities from aerial images, unfolds according to the following design phases:

  1. Data Acquisition and Storage

  2. Data Processing

  3. Data Labeling

  4. Machine Learning

  5. Results Processing

  6. User Engagement

Benefits: Digital Transformation and Sustainability

Today more than ever, we realize the multitude of positive impacts of Digital Transformation:

  • Shifting from scheduled and on-condition physical inspections to virtual inspections assisted by AI/ML models.
  • Transitioning from scheduled maintenance to maintenance supported by feedback from AI/ML models trained on aerial images.
  • Gaining a comprehensive view of infrastructure failures.

Moreover, the creation of intelligent infrastructure serves as a lever for environmental sustainability, as the introduction of these processes:

  • Reduces technician travel, thereby limiting CO2 emissions.
  • Increases the ability to identify problems and helps schedule maintenance that optimizes resource usage, thus reducing waste.

The main areas of service include:

  • Machine learning (ML) through which data scientists and developers can quickly and securely create, train, and deploy machine learning models in a hosted environment ready for production.

  • Utilization of human-in-the-loop functionality, allowing harnessing the power of human feedback throughout the ML lifecycle to enhance model accuracy and relevance. This enables completing a range of human tasks, from data generation and annotation to model review, customization, and evaluation.

  • Integration and customization of the geographic information system (GIS), used for map creation and usage, compiling geographic data, map analysis, sharing geographic information, and managing geographic information in a database.

  • Custom Python developments for use in data science and machine learning (ML).