DataProphet DETECT alerts technicians to process faults and their causes within minutes, enabling corrective action in hours instead of weeks.
DataProphet DETECT monitors real-time data from your PLC and SCADA for any anomalous behavior indicating a manufacturing fault, guiding corrective action within reaction times that are impossible under traditional feedback strategies. It achieves this using an integrated edge-to-cloud system. Optimized AI algorithms run on purpose-built industrial hardware to detect anomalies within seven seconds. Data from a detected anomalous cycle is fast-tracked for analysis by the AI-driven Diagnostic Algorithm in the cloud. Technicians on the factory floor are then alerted to the issue and its likely causes, and guided to corrective action, saving machine owners costly process faults.
Significantly improve system uptime and achieve measurable cost savings with DataProphet DETECT.
Process anomalies become detectable in large volumes of raw factory data. In connectivity-constrained factory environments, it becomes impossible to stream these data volumes to the cloud where powerful AI algorithms normally live. DETECT solves this thanks to our highly optimized AI algorithms that run on purpose-built DataProphet EDGE devices, to detect both known and unknown process anomalies in raw factory data. When an abnormal cycle is detected, its data – a small fraction of the total volume of process data – is packaged and uploaded to the cloud for real-time corrective action and machine-expert analysis.
Security is built into every aspect of DataProphet DETECT. Our mature security architecture is one of the characteristics that sets DataProphet’s product suite apart. DataProphet’s industrial EDGE devices are 100% centrally managed, through a robust infrastructure-as-code framework. Each device is given a cryptographic identity and a secure channel for all management and data-stream communication. Data is encrypted in transit and at rest. Every tenant is given an isolated cloud environment. User permissions are enforced through industry best-practice, role-based access control methods.
DETECT can tell the difference between abnormal machine behavior and abnormal human operator adjustments. This means fewer false alarms when an operator is trying out a new pattern or an unusual but valid configuration.
For a given anomalous machine cycle, DataProphet DETECT can tell which stages of the cycle contained the fault. Let us take an example to illustrate this: DETECT might identify an abnormal cycle, and specify that the hopper pressure was abnormal in the first stage of the cycle, whereas the hydraulic temperature was abnormal in stages 2 and 3.
For an unknown event, the algorithm presents a machine expert with a technical report of the detected fault. The machine expert is asked to analyze the fault and provide appropriate recommendations to remedy the fault. The expert uses the UI to add descriptions, recommendations, and images. Once an expert has filled in this information, all matching events in the future are sent directly to the machine owner with the accompanying recommendations.