Development of OEE (Overall Equipment Effectiveness) Web-Platform for leading energy company using IoT sensors data

The Challenge

The client was facing challenges in monitoring their plant equipment’s performance and behaviors. Their existing commercial software provided a granular level of analysis for plant equipment but lacked higher-level asset monitoring features. The company required a web application to analyze various domain-specific tags and trends for chemical gasification processes in plant units using IoT sensors data for multi-level internal stakeholders from senior upper-level management. The web application would allow them to make smart decisions by ensuring the plant was running smoothly and minimizing downtimes.

Procogia’s Approach

Designed logical and physical databases (3NF) from scratch, considering business requirements for staging data and processed data for the web-app with triggers, stored procedures, and views.
Created ETL scripts that run every day in batch processing to load data from the historian database to the in-house staging database.
Developed a complex R Shiny web application for 8 plant units and its respected trains (Total: 8 Menu-Items and 56 Sub-Menu-Items) through shiny modules with various inputs to analyze time-series data.
Deployed the app on RStudio Connect with load-balancing and user controls, but it could be installed and run locally through a docker container or deployed as an R package (golem philosophy) with minor patchwork.

The Results

Provided a plant-wide web application to analyze various domain-specific tags and trends for chemical gasification processes in plant units using IoT sensors data for multi-level internal stakeholders from senior upper-level management.
Allowed users to make smart decisions by ensuring the plant is running smoothly and minimizing downtimes.
Reduced a significant amount of time of data preparation for manual reporting and sudden breakdown times.
Modernized the Shiny app, reducing user wait time, ensuring reproducibility, and improving the app’s performance.
Enabled the company to make data-driven decisions, thus improving operational efficiency and providing new insights.
Upskilled the team in using advanced technologies for analysis and decision-making.

Services Used

Data Science

We use open source technology to leverage the full potential of your data. Predictive and prescriptive results are actioned using AI and Machine Learning (ML).

Machine Learning

Drawing on statistical methods to enable improvement with experience, ProCogia’s machine learning algorithms predict outcomes and automate processes.

Data Engineering

ProCogia is partnered with the leading cloud providers, enabling our agnostic approach to focus on delivering tailored game-changing solutions for our clients.