Digging into Noodle.ai’s Asset Health AI Application
If you’re a manufacturing company looking to increase revenue by integrating a system of intelligence driven by improved failure prediction, diagnosis, and prevention, our Asset Health AI application is designed for you.
You likely deal with complex machinery and low excess capacity on a regular basis, but that system is flawed. It’s missing important data points and insights that can help significantly reduce waste produced by manual monitoring alone.
Some key features of our Asset Health AI application are:
- Anomaly detection
- Failure prediction
- Causal-factor Identification
- Asset Life Expectancy Prediction
- Downtime Duration & Productivity Impact
- Maintenance Recommendation
What does this all mean?
In a not-so-simple recap, AHAI monitors a variety of data points such as asset health summaries, detects risks such as failure prediction, and creates a maintenance schedule then makes recommendations based on that data.
What sets us apart? Noodle.ai uses advanced AI and ML techniques to detect anomalies and provide supervised learning for failure prediction, and our applications can be integrated into your current system to enhance, not detract, for your current operating system. We also have the ability to deploy the same model across a variety of assets and failure modes.
*Benefits shown have been seen by several of our customers, but can not be guaranteed for all customers.
- Sustainability for Steel: Redefining the Perception of an Industry
- 2019 SDCExecutive Green Supply Chain Award Winner Noodle.ai
- Breaking the Mold: Nestlé’s Mission to be Profitable and Sustainable
- Deep Probabilistic Decision Machines (DPDM) for Building a Causally Generative Process Model-based Action Control in Enterprise AI
- Ding Dong! Avon’s Calling… for Streamlining of its Supply Chain