Predictive Maintenance


Predictive Maintenance


Predictive Maintenance


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

Current limitations

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

AI-Surge is a powerful ModelOps platform that enables your organization to build and deploy production-ready data products and services. It includes all the components necessary to your project in a single, fully-fledged environment, from raw data collection to the deployment of an application or an API, through the critical steps of data preparation and storage.

Once your data sources have been connected to AI-Surge, create, and orchestrate data workflows to make data ready for further analysis, perfectly fitted to your business. With pipelines directly integrated into your data production line, you can now build, train, score, and run maintenance models. Bring your algorithms to real-life by embedding them into meaningful dashboards or feeding internal systems through APIs to proactively alert when deficiencies or failures happen.

 

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

Current limitations

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

AI-Surge is a powerful ModelOps platform that enables your organization to build and deploy production-ready data products and services. It includes all the components necessary to your project in a single, fully-fledged environment, from raw data collection to the deployment of an application or an API, through the critical steps of data preparation and storage.

Once your data sources have been connected to AI-Surge, create, and orchestrate data workflows to make data ready for further analysis, perfectly fitted to your business. With pipelines directly integrated into your data production line, you can now build, train, score, and run maintenance models. Bring your algorithms to real-life by embedding them into meaningful dashboards or feeding internal systems through APIs to proactively alert when deficiencies or failures happen. 

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

Current limitations

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

AI-Surge is a powerful ModelOps platform that enables your organization to build and deploy production-ready data products and services. It includes all the components necessary to your project in a single, fully-fledged environment, from raw data collection to the deployment of an application or an API, through the critical steps of data preparation and storage.

Once your data sources have been connected to AI-Surge, create, and orchestrate data workflows to make data ready for further analysis, perfectly fitted to your business. With pipelines directly integrated into your data production line, you can now build, train, score, and run maintenance models. Bring your algorithms to real-life by embedding them into meaningful dashboards or feeding internal systems through APIs to proactively alert when deficiencies or failures happen.