Hybrid-Conditional Anomaly Detection for Cyber-Physical Systems
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Solution Overview
Problem
Cyber-physical systems with hidden status conditions pose challenges in identifying normal dynamics and detecting anomalies, as these conditions are not directly measured by sensors and can vary across different users and operational states, leading to incorrect identification of abnormal behavior.
Innovation Solution
A hybrid condition encoder-decoder model is used to recover hidden status conditions, employing deep temporal clustering and a similarity-based mechanism to decompose hybrid conditions into basic ones, with encoders and decoders learning embedding vectors and reconstructing sequences to determine anomaly likelihood based on reconstruction errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional anomaly detection methods are used without considering hidden states, then the detection process is simple and fast, but the accuracy is poor because hidden conditions are not accounted for
Solution Approach 1:
The patent introduces hidden state variables as intermediaries between observable sensor data and anomaly detection. These hidden states act as mediators that capture unobserved system conditions, enabling more accurate anomaly detection by accounting for factors not directly measurable by sensors.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based anomaly detection systems with a neural network-based encoder-decoder model. This substitution enables the system to automatically learn and represent hidden states from sensor data, achieving superior detection accuracy without manual feature engineering.
2Measurement precision
If multiple hidden states are distinguished and modeled separately, then detection accuracy improves, but the time to process and reconstruct data increases
Solution Approach 1:
The patent segments the anomaly detection process by training separate encoder-decoder models for each distinguished hidden state. This segmentation allows the system to specialize each model for specific system conditions, improving overall detection accuracy while maintaining efficient processing through modular architecture.
Solution Approach 2:
The patent performs preliminary action by pre-training multiple state-specific encoder-decoder models during an offline phase. This allows the system to have ready-to-use specialized models for each hidden state, reducing online processing time when actual anomaly detection is performed.
3Reliability
If hidden states are not distinguished, then the system is easier to operate and maintain, but anomalies are incorrectly identified due to varying operational states
Solution Approach 1:
The patent applies dynamics by making the anomaly detection system adaptive to different operational states through hidden state variables. The system dynamically adjusts its detection behavior based on the current hidden state, allowing it to reliably identify anomalies across varying conditions without requiring manual reconfiguration.
Solution Approach 2:
The patent changes the parameter space by introducing hidden state variables that capture unobserved system conditions. This parameter expansion allows the model to account for varying operational states, improving reliability in anomaly identification while the automated nature of the approach keeps operational complexity manageable.
Data Source
AI summary
Methods and systems for training a model include distinguishing hidden states of a monitored system based on condition information. An encoder and decoder are generated for each respective hidden state using forward and backward autoencoder losses. A hybrid hidden state is determined for an input sequence based on the hidden states. The input sequence is reconstructed using the encoders and decoders and the hybrid hidden state. Parameters of the encoders and decoders are updated based on a reconstruction loss.