Self-Organizing Maps for Abnormal State Detection
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Solution Overview
Problem
Complex systems face challenges in detecting transitions from normal to abnormal states due to their immense state space, making it difficult for operators and monitoring functionality to recognize such transitions promptly, which can lead to system failure.
Innovation Solution
The implementation of self-organizing maps (SOM) and moving-average self-organizing maps (MASOM) to characterize normal system behavior and identify abnormal transitions by mapping high-dimensional state vectors into lower-dimensional representations, facilitating early detection of abnormal states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to track system states, then system operators can monitor operational characteristics, but the immense state space of complex systems makes it difficult to recognize transitions from normal to abnormal states promptly
Solution Approach 1:
The patent segments the immense state space by introducing abstraction layers that divide the monitoring task into manageable components. State vectors are segmented into discrete dimensions, and the monitoring system is divided into state-vector generation modules, SOM processing modules, and anomaly detection modules, each handling specific aspects of the monitoring task
Solution Approach 2:
Self-organizing maps serve as an intermediary between the complex system state space and the monitoring interface. The SOM transforms high-dimensional state vectors into lower-dimensional topological representations, acting as a mediator that preserves relational information while reducing complexity for analysis
2Measurement precision
If detailed state vectors are monitored to improve detection accuracy, then abnormal transitions can be identified more precisely, but the computational complexity and data processing requirements increase significantly
Solution Approach 1:
The patent applies dimensionality reduction by mapping high-dimensional state vectors into the lower-dimensional topology of the self-organizing map. This transforms the problem from tracking numerous individual state dimensions to monitoring positions and transitions within the SOM's simplified spatial representation
Solution Approach 2:
The system creates simplified copies of the state space through the SOM representation. Instead of processing the full complexity of the original state vectors, the monitoring system works with the topological map copy that preserves essential relational information while reducing computational burden
3Reliability
If the system monitors all possible state transitions, then complete coverage of abnormal states is achieved, but the response time for detecting critical transitions is delayed
Solution Approach 1:
The self-organizing map is pre-trained during an initialization phase to learn the normal operational patterns and structure of the system state space. This preliminary action enables the monitoring system to quickly recognize deviations from normal behavior without requiring extensive real-time analysis of all possible states
Data Source
AI summary
The current application is directed to monitoring subsystems, and monitoring methods incorporated within the monitoring subsystems, that monitor operation of devices and systems in order to identify normal states and to quickly determine when a device or system transitions from a normal state to an abnormal state. The methods and monitoring components to which the current application is directed employ self-organizing maps and moving-average self-organizing maps to both characterize normal system behavior and to identify transitions to abnormal system behaviors.