SR::SPC
Predictive Analytics System
Early Warning System for Detecting Process and Environmental Changes
SR::SPC is an intelligent early warning system for automatically monitoring process quality and the condition of the technical systems and processes in your plant
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The Predictive Analytics System SR::SPC is used to define performance values of technical processes in such a way that the generated data permits reliable statements about the current status of the monitored component. In addition, these data can be used to detect emerging faults of the monitored components at an early stage, so that, for example, the shutdown necessary for the repair or for preventive maintenance can be planned and carefully prepared.
Easily Detecting Changes in Condition and Process
SR::SPC's main goal is to reliably identify critical deviations in components or processes from preset norms - By comparing actual values with reference values through key performance indicators (KPIs).
The trends of the KPIs over time are analyzed by the so called “control chart”, a technical tool for quality control. This chart highlights essential feature of the monitored process, so that, on the basis of the chronological sequence of this feature, deviations from the current reference value and thus emerging faults can be detected early.
SR::SPC - A Smart Maintenance Planning for Your Facilities
Advanced technology enables us to detect early and reliable deviations. But what's in it for you?
SR::SPC at a Glance
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Reliable and automatic early detection of process weak spots in real time
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Overview of the assets and components health, condition and performance
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Conversion of unplanned into planned downtimes
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Forward-looking organization of maintenance measures
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Ability to monitor varied technical process parameters automatically and provide continuous support to employees operating the plant
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Automated and continuous AI-driven prediction of trends
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Browser-based user interface supporting decision making and knowledge transfer
Enhance Plant Performance with AI
By leveraging neural networks and physical models, SR::SPC's straightforward analysis automatically recognizes error patterns and identifies substandard components, facilitating early and reliable deviation detection. With its advanced technology, process monitoring becomes more intuitive.
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SR::SPC operates through two approaches to predictive maintenance - Pre-engineered KPI using supervised machine learning (ML) algorithms, and autonomous and unsupervised ML.
In the February 2024 issue, EES highlights a transformative strategy to maximize plant efficiency by leveraging AI in the development of digital twins for sustainable energy generation. With the features of SR::SPC, digital twins can seamlessly identify even the most subtle changes.
Read more in page 48.
SR::SPC Featured in
The Singapore Engineer Magazine
