Problem

One of the biggest challenges faced by engineers is identifying and labeling equipment and instruments on Piping and Instrumentation Diagrams (P&IDs). This process is time-consuming, tedious, and prone to errors. Manual identification of labels can lead to inconsistencies in labeling across different diagrams, which can cause confusion and errors during maintenance or upgrades.

To address this problem, we set out to develop an algorithm that could automatically identify labels on P&IDs with high accuracy and speed up the labeling process.

Learn more about our solution here.

Solution

We developed a deep learning-based algorithm that uses computer vision techniques to automatically identify equipment and instrument labels on P&IDs. The algorithm was trained using a large dataset of annotated P&IDs, which allowed it to learn how to recognize different types of labels with high accuracy.

The algorithm was integrated into a software tool that allows engineers to upload their P&IDs and automatically generate labeled diagrams. The tool also allows users to review and edit the generated labels if necessary.

Impact

Reduced labeling time by X%
Improved labeling accuracy by X%

"Our new labeling tool has had an incredible impact on our engineering team's productivity. It has saved us countless hours of manual labor while also improving the accuracy of our labeling process."

Team

Team Member #1 - Machine Learning Engineer
- Developed and trained the deep learning algorithm
- Optimized the algorithm for speed and accuracy
Team Member #2 - Software Developer
- Built the software tool that integrates with the algorithm
- Designed the user interface for the labeling tool

Deep Learning Algorithm Automates P&ID Labeling