Predictive Maintenance

Machine data is securely streamed from equipment sensors to a central repository using industrial data protocols and gateways. 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 maintenance costs and increase asset availability - to impact production profitability
IoT Use Cases: Predictive Quality
IoT Use Cases: Predictive Quality

Predictive Quality

Minimize quality control rejects by anticipating and preventing quality issues.

Production line quality teams get real-time alerts into upcoming quality issues and their root causes to minimize 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 leading 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

Predictive Waste

Identify causes for production waste and prescribe focused actions that minimize rework and scrap. Predictive analytics and automated root cause analysis are employed to anticipate process failures that yield wastage.

Predictive simulation is used to test production parameters to avoid the process failures while ensuring optimized throughput.

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  • Monitor current production performance
  • Predict excessive waste levels
  • Analyze the cause of waste issues
  • Prevent causes for waste and rework
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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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

Process Optimization

Maximize production throughput by leveraging machine learning to predict and prevent process disturbances – such as extreme pressures, leakages, blockages, asset cleaning, and more.

Anticipate when process disturbances will happen, know why they will happen, and determine how to avoid them.

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  • Uncover relationships between production parameters across all stages of the manufacturing process
  • Translate data into predictive insights without data scientists
  • Use Automated Root Cause Analysis for faster and more reliable results
  • Use Predictive Simulation to determine the optimal production parameters that will prevent process disturbances
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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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

Predictive Simulation

Determine optimum production parameters that will avoid process disturbances after understanding the primary causes from Root Cause Analysis.

Production teams can simulate how production processes will behave in different scenarios.

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  • Reduce production waste in food manufacturing by predicting process failures that yield wastage
  • Minimize process disturbances in chemical plants by understanding how specific production processes will behave in different scenarios
  • Optimize quality and throughput in refineries by continuously regulating production parameters 
  • Identify process control parameters that impact production disturbances and adjust only them
IoT Use Cases: Root Cause Investigation
IoT Use Cases: Root Cause Investigation

Root Cause Investigation

Uncover the early causes of process inefficiencies to reduce unplanned downtime, increase production throughput, and minimize quality and yield 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 process disruption events.

Quality and maintenance engineers use these automated lists of prioritized suggestions to quickly find and mitigate the root causes of process inefficiencies 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 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