Preventive maintenance avoids both the extremes and maximizes the use of its resources while detecting the anomalies and failure patterns and providing early warnings. Maximize your equipment efficiency and capacity to accelerate your journey to the Factory of the Future.
Provides predictive capability to forecast failure and determine remaining useful life. Current prescriptive rule based solutions are unable to maximize equipment capacity. Preventive Maintenance ML uses Google Cloud BigQuery, Kubeflow and CMLE as key solution components.
This AI solution focuses on identifying patterns in both sensor and yield data that indicate changes in equipment condition. It leverages machine learning and predictive analytics to determine the remaining value of assets and accurately determine when a manufacturing plant, machine, component or part is likely to fail, and thus needs to be replaced.
Google’s IoT core, Google Cloud machine learning models leverage sensors that take in data from sound vibration, noise, data signals, temperature, and visual signals to identify and predict replacement.
Plan ahead your operational costs by predicting whether equipment will fail in a given period or not and improve your production efficiency from 45% to 80% while reducing your operational costs.
Preventive Maintenance ML not only provides cost savings, but it enables new business models and makes sure your operations, equipment and ultimately your supply chain is running smoothly.