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The primary challenge lay in the colossal volume of data generated by these components. The objective was to develop a modern software solution capable of efficiently storing, analyzing, and interpreting this data to generate forecasts, detect anomalies, and issue advisories, alerts, and alarms. Despite the complexity, the international team working on the project has demonstrated resilience and problem-solving prowess, ensuring that communication remains a linchpin in addressing every challenge encountered.
Software developers and Project Manager.
The culmination of the team’s efforts resulted in A-class data processing solutions. Leveraging extensive knowledge in data analysis, client-server communication, and building expert systems, the project produced meticulously tested software. This software not only facilitates the monitoring process but also empowers users to customize configurations and introduce additional analyses tailored to their specific needs.
🔍 Collects data from strategically positioned sensors on critical drilling platform components, 🔍 Operates in parallel time intervals, 🔍 Records and analyzes data for real-time sensor monitoring.
This data is then recorded and subjected to analysis, enabling real-time sensor monitoring.
Technologies used in this project
Python, along with its related libraries, and Cloud technologies formed the backbone of our solution.
This tech stack was instrumental in creating a robust and adaptable system capable of meeting the demands of real-time data processing and analysis.
The collaboration with a non-disclosure agreement (NDA) partner has been a key success factor. INTechHouse is actively engaging in dialogues to enhance the functionality of the product, ensuring it exceeds expectations. The team’s commitment extends beyond development to encompass system configuration, testing, and the creation of automated analyses and advisories, offering comprehensive support for the entire product lifecycle.
Furthermore, the gathered data undergoes thorough analysis to predict and record any anomalies or outliers, providing a basis for user alerts.
Our application is available in two versions for data storage: in the cloud and on a local enterprise disc.
As we look ahead, our predictive maintenance software anticipates integrating advanced machine learning and AI solutions, ensuring an evolving, state-of-the-art system for underwater drilling platform monitoring.
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If you have any question or you want to talk about your project do not hesitate to contact us.
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