Adaptive Alarm Dispatch Using Incremental Regression Updates
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Solution Overview
Problem
Existing regression-based anomaly detection systems in operational systems require extensive manual data selection and rebuilding after system modifications, leading to delayed automated monitoring capabilities and increased susceptibility to undetected anomalies during data collection periods.
Innovation Solution
Automating the regression model rebuilding process and initial anomaly detection by incorporating newly received sensor data into the model, allowing for semi-automatic data evaluation and model updates, enabling early anomaly detection before full calibration is achieved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If regression models are rebuilt manually after system modifications using collected measurement data, then the model calibration accuracy is improved, but the time required to restore automated monitoring capability increases
Solution Approach 1:
The system performs preliminary actions by automatically collecting and storing measurement data during system operation before model rebuilding is needed. When a modification occurs, the pre-collected data is immediately available for rapid model recalibration, eliminating the need to start data collection from scratch and significantly reducing the time to restore monitoring capability
Solution Approach 2:
The system performs self-service by automatically selecting datasets and rebuilding regression models without requiring manual expert intervention. The automated dataset selection process evaluates candidate datasets based on quality metrics and systematically identifies suitable data for model calibration, transforming a previously manual expert task into an autonomous system capability that restores monitoring rapidly
2Reliability
If manually selected good datasets are used to create regression models, then the anomaly detection reliability is improved, but the operational complexity and expert dependency increase
Solution Approach 1:
The system performs self-service by automatically selecting appropriate datasets for model calibration without requiring expert intervention. The automated selection process evaluates multiple candidate datasets using quality metrics and systematically identifies suitable data, transforming a complex manual expert task into an autonomous system capability that maintains reliability while reducing operational complexity
Solution Approach 2:
The system replaces the mechanical process of manual expert dataset selection with an automated computational system. The automated selection algorithm uses predefined quality metrics and evaluation criteria to objectively assess candidate datasets, substituting human expert judgment with a systematic computational process that reduces operational complexity while maintaining or improving selection consistency
3Adaptability or versatility
If a wide range of measurement data is collected to capture various ambient conditions, then the regression model robustness is improved, but the data processing time and manual effort increase
Solution Approach 1:
The system performs self-service by automatically evaluating and selecting the most appropriate datasets from the collected measurement data. The automated selection process uses quality metrics to identify datasets that capture the necessary range of ambient conditions without requiring manual review of all available data, significantly reducing processing time while maintaining model robustness
Solution Approach 2:
The system extracts only the necessary subsets of data needed for model calibration from the larger collected dataset. The automated selection process identifies and extracts specific candidate datasets that contain the required range of conditions, eliminating the need to process and analyze the entire collected dataset while still capturing the necessary variability for robust model calibration
Data Source
AI summary
Systems and methods for monitoring an operational system. An initial set of sensor data is accumulated from a system over a substantially shorter time than is required to collect data to characterize a regression model for an operating parameter of the system. An initial regression model is created based on the initial set of sensor data. A subsequent set of sensor data is received from the at least one sensor after creating the initial regression model. An expected dependent value for the subsequent independent value is determined using the initial regression model. An operator is prompted to update the initial regression model based on a difference between a subsequent dependent value and the expected dependent value. The initial regression model is updated to incorporate the subsequent set of sensor data. A notification is provided based on a difference between presently received sensor data and the updated regression model.