Problem

A manufacturing company was experiencing frequent equipment failures, leading to costly downtime and lost productivity. They were using traditional methods for predicting equipment failure, which were time-consuming and often inaccurate. The company needed a more efficient and accurate way to predict equipment failures.

The company had large amounts of vibration and temperature data from their equipment but lacked the expertise to analyze it effectively. They needed a solution that could process this data quickly and accurately to predict when equipment failures would occur.

Learn more about our solution here.

Solution

We built an AI-based predictive maintenance system that used machine learning algorithms to analyze the vibration and temperature data from the company's equipment. We used an LSTM model for fault classification using vibration data, eliminating the need for more complex models. For predicting Remaining Useful Life (RUL) of pumps, we chose temperature data, assuming the last dataset points as the failure point.

After testing multiple models, we found that the Transformer model predicted RUL better than the LSTM model, validated through graphs and performance indicators. Our system provided real-time alerts when equipment was at risk of failure, allowing maintenance teams to take proactive measures before any damage occurred.

Impact

Reduced downtime by X%
Increased accuracy of predictions by X%
Improved overall equipment efficiency by X%

"Our predictive maintenance system has had an incredible impact on our operations. We've seen significant reductions in downtime and improved efficiency across the board. It's been a game-changer for our business."

Team

Team Member #1 - Data Scientist
- Built and trained the machine learning models
- Analyzed data to identify key predictors of equipment failure
Team Member #2 - Software Engineer
- Built the real-time alert system for maintenance teams
- Integrated the machine learning models into the company's existing systems

AI Predictive Maintenance Reduces Downtime