Streamed Sensor Data Processing With Adaptive Modes for Anomaly Detection
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Solution Overview
Problem
Existing control systems for industrial automation processes face challenges in efficiently processing streamed sensor data in real-time, leading to delays in monitoring performance and determining control actions.
Innovation Solution
The implementation of smart sensor devices equipped with processing components that employ learning algorithms for anomaly detection, preventative maintenance, diagnostics, and sensor data validation, allowing these devices to toggle between different operational modes based on sensor data discrepancies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If sensor data is processed in real-time using traditional control systems, then monitoring speed is improved, but processing efficiency deteriorates due to the time-consuming model generation process
Solution Approach 1:
The system performs preliminary actions by generating processing models offline before real-time operation. The processor generates a processing model during an initial phase, stores it in memory, and then uses this pre-generated model for rapid real-time data processing, eliminating the need to generate models during critical monitoring periods.
Solution Approach 2:
The processing system is segmented into distinct operational phases: an offline model generation phase where processing models are created and stored, and an online execution phase where pre-generated models are applied to sensor data. This segmentation allows computationally intensive model generation to occur separately from time-critical data processing.
2Measurement precision
If continuous high-level processing is applied to sensor data, then anomaly detection accuracy is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts its processing level based on operational conditions. The processor monitors sensor data and automatically selects between different processing modes: standard processing for normal operations and enhanced processing only when anomalies are detected or suspected, thereby optimizing energy consumption while maintaining detection accuracy.
Solution Approach 2:
The system changes processing parameters adaptively. During normal operation, less computationally intensive processing is applied. When anomalies are detected, the system transitions to more intensive processing modes with adjusted parameters such as increased sampling rates, deeper analysis algorithms, or expanded monitoring scopes.
3Power
If comprehensive sensor data processing is performed externally, then processing capability is improved, but system complexity increases
Solution Approach 1:
The system extracts and isolates the computationally intensive model generation function from the real-time processing chain. The processing model is generated externally during offline operations and then applied as a pre-packaged solution during real-time operation, removing the complexity of continuous model generation from the active system.
Solution Approach 2:
The system creates a copy of the processing model during offline operations and stores it for reuse during real-time processing. Instead of regenerating the complete processing model for each data set, the system replicates and applies the stored model multiple times, significantly reducing computational complexity during operation.
Data Source
AI summary
A system may include sensor device comprising a sensor configured to measure sensor data indicating an operational parameter of industrial automation equipment associated with an industrial automation process. The system may also include communication circuitry configured to transmit the sensor data. Additionally, the system includes a processor configured to receive the sensor data. Further, the system includes a non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause the processor to perform operations including identifying an operational state of the industrial automation equipment based on the sensor data. The operations may also include determining a discrepancy between the sensor data and the operational state. Further, the operations may include modifying an operation of the processor from a first operational mode to a second operational mode of a plurality of operational based on the comparison.