PREDICTIVE MAINTENANCE USING MACHINE LEARNING EXPLAINED.
HOW TO IMPLEMENT MACHINE LEARNING AND BIG DATA FOR IOT PREDICTIVE MAINTENANCE.
Modern industrial IoT analytics and ML enables equipment failure prediction before they happen. AI-Surge enables users to create as many models as there are product lines or situations to monitor, leveraging structured, unstructured sensor (IoT) data for predictive maintenance.
What is Predictive Maintenance?
Predictive maintenance has become trendy due to several factors:
Sensors are cheaper than ever, making them more widely available
Sensors are becoming increasingly accurate and specific
Low-bandwidth networks are now widespread
What greater visibility on the operating machinery means for companies:
Better maintenance and repair operations planning
Fewer business interruptions
Optimization of infrastructure lifetime
In fine, improve customer satisfaction and operational efficiency!
Figures only confirm these attractive promises. According to McKinsey’s well-known strategy consulting firm, predictive maintenance in manufacturing could have a potential economic impact of nearly $630 billion per year in 2025*.
*The Internet of Things: Mapping the value beyond the hype, McKinsey Global Institute – June 2015
If you’re reading this page today, it probably means that you’re already aware of the many challenges faced by organizations trying to set up a preventive environment.
Feed your models with multiple types of data to enhance the dataset, from sensor data to images, video, or even audio
Collect data for an extended period to watch the system running throughout its degradation process
Predict the most high-risk failures to be able to differentiate and act on the most business-critical operations
Using AI-Surge for intelligent predictive maintenance
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