Modular Encoder for Vehicle Anomaly Detection
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Solution Overview
Problem
Modern vehicles with complex electronic control units face challenges in predicting and preventing faults across various systems, leading to safety issues and repair costs, as existing methods require extensive individual data collection for effective anomaly detection.
Innovation Solution
A modular encoder model using neural network blocks with selectively enabled connections, controlled by a local system-specific policy model, is trained with heterogeneous data from multiple vehicles to detect anomalies by encoding and decoding sensor data, generating anomaly scores, and performing corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a traditional anomaly detection system is used that requires extensive individual data collection, then detection accuracy may be improved, but the time required for data collection and system deployment increases significantly
Solution Approach 1:
The modular encoder model is pre-trained on heterogeneous data from multiple vehicle types before deployment. This preliminary training action allows the model to learn general anomaly patterns across different vehicle systems, enabling effective anomaly detection without requiring extensive individual data collection from each specific vehicle
Solution Approach 2:
The encoder model is designed with universal applicability across heterogeneous vehicle types through its modular architecture trained on diverse data sources. The same model can detect anomalies in different vehicle systems (engine, transmission, battery) and across different vehicle models, eliminating the need for separate extensive training for each individual system
2Measurement precision
If a customized anomaly detection model is trained for each specific vehicle system, then detection precision for that system is improved, but the device complexity and training requirements increase
Solution Approach 1:
The anomaly detection system is segmented into a modular encoder model with distinct functional blocks that can be selectively enabled. Each module can be independently configured for specific vehicle systems while maintaining overall system coherence, reducing training complexity compared to a fully customized monolithic model
Solution Approach 2:
The model architecture incorporates dynamic selective enabling of connections between neural network blocks based on the specific vehicle type and system being monitored. This dynamic configuration allows the same base model to adapt to different detection precision requirements without requiring complete retraining, thereby reducing overall device complexity
3Reliability
If a comprehensive anomaly detection system is implemented that monitors all vehicle systems, then safety and reliability are improved, but the computational resources and processing time required increase
Solution Approach 1:
The system implements partial monitoring by selectively enabling specific neural network blocks based on the vehicle type and operational context. Rather than always processing all possible anomaly types at full detail, the system dynamically activates only the necessary detection capabilities, reducing computational energy consumption while maintaining adequate safety monitoring
Solution Approach 2:
The comprehensive monitoring system is segmented into modular functional blocks that can be independently activated. This segmentation allows the system to distribute computational load across different modules based on current needs, enabling thorough safety monitoring without requiring all computational resources to be simultaneously active
Data Source
AI summary
Methods and systems for training and deploying a neural network mode include training a modular encoder model using training data collected from heterogeneous system types. The modular encoder model includes layers of neural network blocks and a selectively enabled connections between neural network blocks of adjacent layers. Each neural network block includes neural network layers. The modular encoder model is deployed to a system corresponding to one of the heterogeneous system types.