With the core elements (covered in Part I of this article series) in place, the predictive maintenance system can be deployed with the industrial IoT platform acting as a Human-Machine Interface (HMI) for continual monitoring and control over the assets…


Industrial IoT Platform (Deployment Phase)

Function: Deployment and continued interaction with the predictive maintenance solution

The IoT Platform acts as the predictive maintenance hub, notifying management and relevant personnel about any issues that require attention in real time.

Alerts are sent automatically with relevant repair information allowing for teams to collaborate and form a plan of action long before the failure is predicted to take place.

A condition monitoring dashboard of the Seebo platform showing predictive alerts.
A screenshot of the Seebo platform – a condition monitoring dashboard shows sensor information and predictive alerts.

Data Analytics

Function: Pattern detection for actionable insights that improve OEE and output quality

Through the deep analysis of historical and real-time machine data, management can be given accurate insight into the operation’s performance. Unplanned downtime nears zero since personnel is notified ahead of time of any impending issues and is given detailed instructions on the type of repair needed to prevent malfunction.

AI algorithms provide an unbiased evaluation of all aspects of the production process and can perform advanced root cause analysis to reveal dependencies that can be difficult for even very experienced professionals to detect.

The fact that the analysis is continual means that informed decisions can be made in real-time to cut the loss of defective output caused by quality issues.



Function: Optimize data flow throughout the network, allowing for problem-free scaling

Workflows are a strategic predictive maintenance tool that network managers use to help the manufacturing facility make optimal use of the system.

Workflows offer two main functions:

Device management – device registration, protocol interoperability, authentication, and access.
Event processing – the polling of data events, and the routing of data to the required destinations eg. feeding existing ERP and CRM systems.


PdM Pilots

Function: Provide a testing phase to ensure stakeholder buy-in and significant ROI.

For the successful deployment of a predictive maintenance system, it’s important to first launch well-structured and monitored pilot programs to test the waters before full adoption.

This is an opportunity to gauge the ROI of predictive maintenance for the specific operation, and to give stakeholders the chance to weigh in to the project.

While the pilot is being run, modifications can be made via the industrial IoT platform until the predictive maintenance system reaches a satisfactory level of performance.


Learn more about predictive maintenance tools and use cases by reading our complete guide to predictive maintenance or download our extensive white paper for free here.