How Gousto reduces food waste and increases workforce productivity: Watch on-demand. >
How Gousto reduces food waste and increases workforce productivity: Watch on-demand. >
The Industrial Internet of Things (IIoT) has been a game changer for operational maintenance. Real-time, sensor-fed condition monitoring allows manufacturers to shift their asset health strategy from a reactive to proactive approach, so they can prevent failures before they occur. Key reasons to consider a predictive maintenance model include:
More manufacturers see predictive asset maintenance as a competitive differentiator, with the market for predictive maintenance technologies expected to grow to $47.8 billion by the end of 2029, a CAGR of 35.1% during the forecast period. To fully leverage the benefits of predictive asset maintenance, access to data from disparate systems and legacy equipment is required.
Poor integration between historians and analytics. Correlating and associating these two data streams is often cumbersome. Many manufacturers rely on custom-coded integrations between historians and applications in the cloud, which require substantial maintenance and are not easily adaptable or scalable.
Identifying assets nearing failure. Poor visibility into motor performance can lead to costly line shutdowns and excess scrap.
Monitoring motor power quality. Limited performance visibility can impact real-time insights into critical performance variables, such as vibration, temperature, or lubrication analysis, which can impact machine quality and productivity.
Pull the CSV file from the industrial gateway. Using HighByte Intelligence Hub, users can easily pull high-resolution data from the gateway and the historian and then integrate those streams into a single data model.
Leverage off-the-shelf connectivity to PI System. The PI System Connector for HighByte Intelligence Hub enables users to easily access PI System data, contextualize and merge this data with data from other systems, and then publish data payloads to the Cloud.
Apply data modeling at the Edge. The Intelligence Hub is typically deployed on-premises—close to the data’s source—so operators who are most familiar with this data can contextualize, standardize, and model the data before it is streamed to the Cloud. This approach ensures data lands in the Cloud in a ready-to-use format to analyze at scale.
Collect data at a consistent, high-frequency rate. The Intelligence Hub can be configured to publish at any interval from tens of milliseconds to multiple days.
Faster access to maintenance analytics. As users iterate and improve their analytical models, they can use the Intelligence Hub to quickly and easily curate the incoming data set.
Less custom coding. The Intelligence Hub provides a low-code interface to easily synchronize high-frequency data and publish clean, standardized data models directly to multiple cloud services.
Data science team has access to high-resolution data. Data science teams can significantly reduce the time spend curating and cleansing data, and more time analyzing the data and building more intelligent analytic models to predict required maintenance.
