Predictive Maintenance in Manufacturing & Utilities
Reducing Unplanned Downtime by 50% Using PdM for Critical Assets
Recapture Up to 80% of Lost Revenue in Your Manufacturing Operations.
When the car engine light goes off in your car as you're on the way to an important meeting, you lose precious time from your life.
For manufacturing and utility companies, one hour of unplanned downtime takes away $20,000 to $100,000+ from the life of their business, depending on the scale of their operations.
Now, imagine knowing exactly when a critical piece of machinery is likely to fail, giving you enough time to fix the issue before it disrupts your entire operation. That's the reality we offer with our predictive maintenance system.
By reducing unplanned downtime by up to 50%, we help recapture 50%-80% of revenue that would otherwise be lost due to equipment failures and breakdowns.
Think of it as a 24/7 veteran team of electrical and mechanical engineers listening to machinery from sensor data all hours of the day.
With the power of a reliable predictive maintenance system that is developed iteratively based on feedback from plant mangers and engineers, we achieve and guarantee at least a 50% reduction in downtime for qualifying manufacturing plant owners and operators.
With our predictive maintenance system we deliver up to a 10x to 15x return return in some deployments. See our ROI breakdown below or contact us for a free systems audit & estimate.
But we don't stop at just detecting faults.
We also focus on Remaining Useful Life (RUL) Prediction, a key component of predictive maintenance. By accurately predicting how much longer your equipment will operate efficiently, we enable you to plan maintenance activities just in time, avoiding unnecessary repairs and preventing unexpected failures.
Here's how we make it happen:
We use advanced algorithms to spot unusual patterns in your equipment's sensor data that could indicate potential issues.
Techniques like Local Outlier Factor and Isolation Forest help us identify anomalies with near-perfect recall rates.
By analyzing historical data and current operating conditions, our models predict the remaining useful life of your machinery. This allows you to schedule maintenance proactively, avoiding both premature servicing and catastrophic failures.
We deploy machine learning classifiers to forecast potential failures before they occur. In our projects, we've achieved over 86% accuracy in predicting equipment failures, giving you the confidence to make informed decisions.
The ROI:
Unplanned Downtime Costs
Suppose your plant loses $100,000 per hour during unplanned downtime. If you experience 10 hours of such downtime per month, that's $1 million lost monthly.
Predictive Maintenance Savings
Working with us, you can reduce unplanned downtime by 50%, saving $500,000 per month.
Annual Savings
That's $6 million saved in a year.
Why This Matters:
Our Expertise
We combine deep knowledge in electrical and mechanical engineering with real-world data science skills.
Our anomaly detection models achieved nearly 100% recall and over 70% F1 scores, ensuring that potential issues are caught early.
Here are some of the tools we currently use:
Machine Learning Algorithms
Local Outlier Factor, Isolation Forest, Support Vector Machines, Random Forest Classifiers, and more.
Evaluation Metrics
Precision, recall, F1 score, and accuracy to comprehensively assess model performance.
We work with industry experts who are having a real-world impact and adhere to industry best practices and continuously update our models based on the latest advancements and feedback from plant managers and engineers. Our solutions are not off-the-shelf; they are tailored to fit your specific needs, operation, and goals.
If you're interested in learning more or trying our systems to evaluate their impact to your manufacturing plant or utility company, reach out to us using our contact form on our contact us page!
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