Predictive Maintenance

Machine data is securely streamed from equipment sensors to a central repository using industrial data protocols and gateways. Seebo predictive analytics are applied to anticipate and predict failures before they arise.

Implementing predictive maintenance typically starts with rule-based alerts until sufficient data is collected, at which time machine-learning algorithms can be applied to identify complex behavior patterns and anomalies.

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IoT Use Cases: Predictive Maintenance
IoT Use Cases: Predictive Maintenance
  • Configure rule-based analytics in the IoT Model to define use-cases for asset failure
  • Monitor the occurrences of these rules to deploy immediate rule-based predictive maintenance
  • Apply advanced predictive analytics and anomaly detection algorithms once enough data has been collected
  • Leverage predictive maintenance to lower service costs and improve productivity - to impact the bottom line
IoT Use Cases: Remote Asset Monitoring
IoT Use Cases: Remote Asset Monitoring

Remote Asset Monitoring

Factories and machinery OEMs get deep visibility into their equipment health and actionable insights to maximize overall equipment effectiveness (OEE), reduce maintenance costs, and cut downtime.

Seebo Condition Monitoring solution includes data acquisition, data analytics, dashboards, and alerts – delivering business outcomes with unmatched speed-to-market and predictable ROI.

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Remote Asset Monitoring

Factories and machinery OEMs get deep visibility into their equipment health and actionable insights to maximize overall equipment effectiveness (OEE), reduce maintenance costs, and cut downtime.

Seebo Condition Monitoring solution includes data acquisition, data analytics, dashboards, and alerts – delivering business outcomes with unmatched speed-to-market and predictable ROI.

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IoT Use Cases: Remote Asset Monitoring
IoT Use Cases: Remote Asset Monitoring
  • Maintenance becomes proactive and timely, and repairs are done before critical damage occurs - reducing downtime
  • Leverage digital twin visualization for remote diagnostics and to quickly identify root cause of equipment failures
  • Improve compliance adherence with continuous logging and monitoring of conditions affecting your assets
  • Understand equipment behavior patterns to affect future iterations of product design and engineering

IoT Prototyping

Empower rapid, iterative, and collaborative prototyping to deliver product concepts for market validation – at the lowest cost and risks.

Leverage digital prototyping – ahead of physical prototyping – to simulate product concepts, gain internal buy-in, and minimize discarded physical prototypes.

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IoT Use Cases: IoT Prototyping
IoT Use Cases: IoT Prototyping
  • Validate the functionality and completeness of your concepts with a fully-functional digital prototype
  • Collaborate with all relevant stakeholders to get buy-in, leveraging embedded-discussions and easy sharing
  • Facilitate Design Thinking and support Stage Gating for new product development
  • Leverage an IoT Marketplace with pre-vetted external partners and suppliers for quickest speed-t0-market
IoT Use Cases: Digital Twin
IoT Use Cases: Digital Twin

Digital Twin

Compare design to actual performance with a Digital Twin software that accurately tracks products, processes, and systems in real time. In this IoT use case, engineering teams accurately test optimization ideas by adjusting parameters in the twin, without risking harm to production.

Leverage runtime and usage data collected by the twin by feeding it into the development and manufacturing process, increasing uptime and production throughput.

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Digital Twin

Compare design to actual performance with a Digital Twin software that accurately tracks products, processes, and systems in real time. Accurately test optimization ideas by adjusting parameters in the twin, without risking harm to production.

Leverage runtime and customer usage data collected by the twin by feeding it into the development and manufacturing process, increasing product margins, customer satisfaction, and market share.

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IoT Use Cases: Digital Twin
IoT Use Cases: Digital Twin
  • Construct a digital twin of a product by visually modeling and simulating its behaviors
  • Monitor the product’s behavior in-market, to gain real-time visibility into performance and highlight critical areas that require immediate attention
  • Provide engineers, product managers, and designers with a better understanding of machines and processes, leading to better product design
  • Construct processes that are more efficient, saving time and resources, especially those involved in creating prototypes and testing them

Root Cause Investigation

Uncover the early causes of process disruption to reduce unplanned downtime, increase throughput, and minimize quality issues.

Process flow and production batch data are fused with your historical and real-time machine data. Machine learning tools then trace correlations between the consolidated data and the disruption events.

Quality and maintenance engineers use these automated lists of prioritized suggestions to quickly find and mitigate the root causes of process disruptions and machine failure.

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IoT Use Cases: Root Cause Investigation
IoT Use Cases: Root Cause Investigation
  • Understand the complex conditions that lead to production failure using a visual production line modeler
  • Speed up the most time-consuming part of root cause investigation with an automated list of probable root causes
  • Detect early indicators of failure and take corrective action to reduce unplanned downtime and improve production quality
  • Leverage root cause investigation results to create prediction alerts that help mitigate quality issues
IoT Use Cases: Predictive Quality
IoT Use Cases: Predictive Quality

Predictive Quality

Minimize scrap, rework and warranty claims by forecasting and preventing quality issues and avoiding recalls.

Plant decision-makers and quality teams use real-time anomaly detection and predictive analytics to detect and eliminate potential quality issues at their source.  

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Predictive Quality

Minimize scrap, rework and warranty claims by forecasting and preventing quality issues and avoiding recalls.

Plant decision-makers and quality teams use real-time anomaly detection and predictive analytics to detect and eliminate potential quality issues at their source.  

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IoT Use Cases: Predictive Quality
IoT Use Cases: Predictive Quality
  • Model your production line processes and visualize your quality control and machine data within the model to track and control overall quality
  • Machine learning algorithms ingest your production line, OT, and quality control data, detecting lead indicators of quality issues
  • Visually drill down into any asset and its sensors within your production floor to understand its impact on overall quality levels
  • Quality engineers get alerts whenever early indicators of known or probable causes of quality issues are detected