ML4Industry Blog
Insights, tutorials, and case studies on machine learning for industrial applications
Latest insights
From Data to Decision in Seconds: The Autonomous Engine for Industrial Excellence
You're data-rich, but knowledge-poor. The gap between possessing data and possessing actionable knowledge—Decision Latency—is the defining operational challenge of our time, and the key to unlocking your next wave of growth.
The Prognostics & RUL Cheat Sheet: A Guide for Real-World Assets
Ready to move from theory to reality with predictive maintenance? The first step is choosing the right RUL model—a choice that depends entirely on your data. This comprehensive cheat sheet demystifies the options, detailing four key methodologies.
Beyond the OEM Manual: A Self-Correcting AI for Real-World RUL
An OEM manual says your motor lasts 13 years. But has it had an easy life or a hard one? This report details a modern, self-correcting AI strategy that doesn't need the full history. Discover how a hybrid model uses live sensor data to uncover an asset's true health and deliver a RUL forecast that adapts to the real world.
Predicting the Future with an Incomplete Past: A Modern Guide to Asset RUL
An OEM manual gives a 13-year lifespan, but your motor has lived a unique life. This report details a modern, self-correcting AI strategy to predict Remaining Useful Life with limited historical data.
The goldilocks metric: why your AI needs to stop crying wolf
What if a smoke detector with 99.9% accuracy could burn your house down? Welcome to the paradox of precision and recall—where being too good makes you terrible.
The truth about AI's report card: why your machine learning model might be lying to you!
Your AI model has 95% accuracy? So does a plane navigation system that only fails during landing. Not all errors are created equal.
Industry-specific insights
AI-Driven Anomaly Detection for AHU Supply Airflow
We taught an AI model to understand the 'healthy' behavior of an Air Handling Unit (AHU) by predicting its normal airflow. The project was a success, developing an XGBoost model with over 96% accuracy, establishing a strong foundation for predictive maintenance by enabling earlier and more reliable fault detection.
From an Avalanche of Alerts to a Dialogue of Diagnosis: The C101 Compressor Story
We analyzed over 242,000 sensor records from a critical compressor, transforming a constant stream of noisy alerts into a clear, diagnostic language. This is the story of how we used machine learning to discover the hidden 'fingerprints' of inefficiency and failure, creating a data-driven roadmap to proactive maintenance and quantifiable business impact.
Your Data Is Talking. Here’s How to Listen.
We were handed a raw sensor dataset and a simple question: 'Can you do something with this?' In less time than a coffee break, we transformed that noisy data into a library of actionable 'Anomaly Fingerprints' that can predict failures before they happen. Here’s the step-by-step story.
The Blueprint or the Gamble: Why a Clear Project Plan Is Your Greatest Asset
As a manager, you want successful outcomes. Discover why a vague project plan is a recipe for failure, while a detailed blueprint empowers your team, de-risks your investment, and guarantees measurable success. Learn to distinguish between a gamble and a guaranteed win.
Smart Manufacturing best practices: Framework for production facilities
Smart Manufacturing: Why Your Framework Is Probably Backwards. Think you've got a solid smart manufacturing strategy?
Smart factory ROI measurement framework: A comprehensive guide
What if I told you that most smart factory initiatives are being measured completely wrong? While executives celebrate marginal OEE improvements, the
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