Dynamic Control Anomaly Detection Under Sensor Noise Uncertainty
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Solution Overview
Problem
Current anomaly detection methods for dynamic control systems face challenges in achieving high true positive rates and low false positive rates, particularly due to small fault data volumes, complex nonlinear dynamic behaviors, and unknown sensor noise and model errors.
Innovation Solution
A neural network-based system identification method combined with a Bayes filter for anomaly detection, which initializes a hidden state distribution, predicts and updates probability distributions, and determines anomalies based on real-time measurement values, effectively handling sensor noise and model uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If residual-based anomaly detection methods are used with neural network prediction models, then the system can detect anomalies by comparing predicted and actual measurement values, but the detection performance degrades when sensor noise level or prediction error is high and unknown
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network model using normal operating data before actual anomaly detection. The model learns the normal behavior patterns and establishes a baseline prediction capability in advance, enabling it to distinguish anomalies more reliably even when noise levels are unknown. This pre-learning phase prepares the system to handle uncertain noise conditions during operational monitoring.
2Reliability
If density-based anomaly detection methods like Kalman filter are used, then the system can handle sensor noise more robustly, but high prior knowledge and complex mathematical modeling are required which limits practical application
Solution Approach 1:
The patent substitutes the traditional mechanical/mathematical modeling approach (Kalman filter requiring system identification and mathematical models) with a data-driven neural network approach. Instead of relying on predefined mathematical models of the physical system, the neural network learns the system's dynamic behavior directly from operational data, eliminating the need for complex prior knowledge and mathematical modeling while maintaining robustness to sensor noise.
3Ease of operation
If one-class classification or rule-based methods are used, then the system can provide high explainability, but they cannot handle the curse of dimensionality and high nonlinearity of dynamic control systems
Solution Approach 1:
The patent addresses the curse of dimensionality by using the neural network's hidden layers to transform the high-dimensional input space into a lower-dimensional latent representation space. This dimensionality transformation allows the model to capture essential patterns while filtering out redundant information, enabling effective anomaly detection in high-dimensional dynamic control systems without sacrificing the ability to handle nonlinearity.
Data Source
AI summary
Various embodiments of the teachings herein include anomaly detection methods for a dynamic control system. An example method includes: using a g network to initialize a hidden state distribution of the system; receiving a measurement value of a sensor and a state value of a trigger at a current point of time; receiving a sampling point into an f network to perform a prediction to obtain a second sampling point; using an h network to map the second sampling point into a sensor measurement value space to perform a prediction to obtain a probability distribution of a measurement value of the sensor in the dynamic control system at the current point of time; and determining whether an anomaly exists in the dynamic control system by comparing the measurement value obtained from real-time monitoring and the probability distribution obtained from a prediction.