Adaptive Anomaly Detection Algorithm for Sensor Data
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Solution Overview
Problem
Existing anomaly detection systems in digital computer systems face challenges in accurately identifying anomalies in real-time data from various sources, such as sensor networks and smart grids, leading to inefficiencies and potential system disruptions.
Innovation Solution
A dynamically adaptable anomaly detection method that utilizes an anomaly detection algorithm to identify unexpected values in time series data, sends anomalous data to a root cause analysis system for feedback, and adapts the algorithm based on the feedback to improve accuracy over time, seamlessly integrating with existing root cause analysis systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a static anomaly detection algorithm is used, then the system structure is simple, but the detection accuracy decreases over time as data patterns change
Solution Approach 1:
The anomaly detection algorithm is transformed from a static to a dynamic system that automatically adapts its parameters based on incoming data. The system continuously updates detection thresholds and patterns by comparing new data against historical patterns, allowing the algorithm to evolve with changing data characteristics while maintaining reasonable computational complexity through pattern-based adaptation rather than complete retraining.
Solution Approach 2:
A feedback loop is implemented where the anomaly detection system receives input from root cause analysis systems and uses this feedback to refine its detection patterns. The system incorporates feedback about false positives and false negatives to adjust its detection sensitivity and patterns, creating a self-improving mechanism that increases accuracy over time without requiring manual intervention.
2Speed
If real-time anomaly detection is performed on all sensor data, then detection speed is fast, but processing resources are consumed excessively
Solution Approach 1:
Instead of applying full anomaly detection analysis to all sensor data points, the system performs partial analysis by first identifying candidate anomalies using lightweight threshold checks, then applying more computationally intensive pattern matching only to suspected anomalies. This selective approach maintains fast detection speed for obvious cases while conserving processing resources for complex analysis.
Solution Approach 2:
The detection process is segmented into multiple stages: initial data validation, threshold-based anomaly identification, pattern-based verification, and root cause analysis. Each stage filters data progressively, ensuring that resource-intensive operations are performed only on data that requires detailed analysis, thereby reducing overall processing resource consumption while maintaining detection speed.
3Measurement precision
If the anomaly detection algorithm is frequently updated with new data, then detection accuracy improves, but system stability decreases
Solution Approach 1:
The system implements periodic updates to detection patterns rather than continuous updates, processing data in batches and updating the anomaly detection model at scheduled intervals. This periodic approach allows the system to incorporate new patterns and adapt to changing conditions while maintaining stability during intervals between updates, preventing excessive fluctuations in detection behavior.
Solution Approach 2:
The system carefully manages parameter changes during adaptation, adjusting detection thresholds and pattern weights gradually rather than making abrupt changes. By controlling the rate and magnitude of parameter updates, the system can improve detection accuracy through learning from new data while minimizing instability and maintaining consistent detection performance during transitions.
4Measurement precision
If multiple anomaly detection techniques are used, then detection accuracy improves, but device complexity increases
Solution Approach 1:
Multiple anomaly detection techniques are merged into a unified framework that uses pattern matching as the core mechanism. Different detection methods (statistical analysis, machine learning, rule-based detection) are integrated and coordinated through a common pattern-based system, allowing the benefits of multiple approaches to be realized while avoiding the complexity of maintaining separate independent systems.
Data Source
AI summary
Aspects of the present invention disclose a method and system for anomaly detection for a data source. The method includes one or more processors identifying unexpected values of monitoring measurands in a monitored time series utilizing an anomaly detection algorithm. A data source provides sensor data, including values of a first group of measurands, which include monitoring measurands. The method further includes determining that values of a second group of one or more of the measurands of a subset of sensor data indicates an anomaly utilizing the anomaly detection algorithm. The method further includes sending anomalous data indicative of the subset of sensor data to a root cause analysis system and receiving corresponding feedback that is indicative of a result of a root cause analysis of the subset of sensor data and comprises a third group of the measurands. The method further includes adapting the anomaly detection algorithm.