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  • Predictive analytics is one of the most important big data trends

    SR::SPC – your early warning system for detecting process and environmental changes

    The highly specialized system technologies we have developed to monitor, analyze, diagnose, and optimize technical processes all have one goal: to maximize plant efficiency. Our SR series of applications includes everything from central data management to solutions for performance and condition monitoring of components that are subject to high stress levels. We leverage methods like machine learning and simulations, which we also use for predictive plant maintenance.

    Constant performance monitoring plays an essential role because components can wear or become filthy as a result of continuously changing operations in energy and industrial facilities. That has an impact on process performance, resulting in financial losses from higher fuel and raw material costs as well as lower production rates and qualities. A complete system failure is often associated with higher repair costs, which at worst can lead to lost profits due to production stops.

    Process monitoring is now child’s play

    As an operator, you know that process monitoring can be particularly challenging because you lack concrete benchmarks for comparison. Our solution is SR::SPC – an intelligent early warning system for automatically monitoring process performance and the condition of technical systems and processes.

    This field-tested application is built on state-of-the-art data analysis. By leveraging one of today’s most important big data trends – predictive analytics – SR::SPC compares the actual condition of your plant and its components with benchmark values from the past. With automated process performance and condition monitoring, you can continuously record and recall the history and reciprocal effects of your most important key performance indicators (KPIs).

    Simple and clear analysis

    To map out the target condition, the tool uses neural networks and a physical model. The target value is calculated based on the operating mode and the current environmental conditions. By comparing live data against target values, SR::SPC determines KPIs that are associated exclusively with the performance or component condition and not with the operating mode.

    The data enables simple and clear analysis, which automatically isolates error patterns and detects substandard components. SR::SPC has the capability to monitor the entire system for abnormal conditions or only specific parts of it. It flags unusual values in the plant’s digital fingerprint and assigns causes of damage.

    An early warning system used around the world

    Advanced technology is allowing us to detect deviations early and reliably. How do you benefit? You’ll experience fewer false alarms, be able to order spare parts in advance, install them when needed, and carry out maintenance at the right times. The warning system, which complements our predictive analytics solutions, is in use all over the world for preventive maintenance. Operators everywhere are finding relief from the growing pressure of today’s complex technical environments.

    SR::SPC capabilities at a glance:

    • Automatically and reliably detect weaknesses in your processes early
    • Convert unplanned downtime into planned downtime
    • Predict maintenance requirements and schedules in advance
    • Automatically monitor a wide range of power plant KPIs
    • Provide your operators with continuous plant management support