Transfer Learning for Adaptive Anomaly Detection in Manufacturing
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Solution Overview
Problem
Traditional manufacturing systems face challenges in maintaining system dependencies and are vulnerable to cyber-attacks, with rule detection being inflexible and artificial intelligence models limited to single devices or manufacturers.
Innovation Solution
A transfer model training system that retrieves raw datasets from devices, trains feature extraction and classification models, and transfers them to manufacturers for adaptation with practical datasets to generate adaptive classification models that can detect abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional rule detection is used for cyber-attack prevention, then the detection process is simple and fast, but the adaptability to different devices and manufacturers is poor
Solution Approach 1:
The patent transforms fixed detection rules into adaptive models by changing parameters through continuous learning. The system adjusts detection parameters dynamically based on data from different devices and manufacturers, enabling the same system to adapt to various contexts while maintaining efficient detection operations.
Solution Approach 2:
The patent introduces dynamic adaptability by enabling the detection system to evolve over time. Instead of static rules, the system continuously learns from new data, allowing detection capabilities to adapt dynamically to different devices, manufacturers, and emerging threat patterns while preserving operational efficiency.
2Measurement precision
If artificial intelligence models are trained for each single device or manufacturer, then the detection accuracy is high, but the training time and resource consumption increase significantly
Solution Approach 1:
The patent implements preliminary action by pre-training a general detection model that can be quickly adapted to specific devices and manufacturers. This base model contains pre-learned detection capabilities that can be fine-tuned with minimal data, significantly reducing training time while maintaining high detection accuracy across different contexts.
Solution Approach 2:
The patent creates a universal detection model that serves multiple devices and manufacturers simultaneously. This multi-functional model can be adapted to different contexts through transfer learning, eliminating the need to train separate models for each device or manufacturer while preserving high detection accuracy through targeted fine-tuning.
3Reliability
If a detection model is trained from scratch for each new device type, then the model fits the device characteristics perfectly, but the entire training process must be re-executed consuming大量 resources
Solution Approach 1:
The patent applies preliminary action by pre-training a general model that captures common detection patterns across multiple devices. When a new device type is introduced, the system performs transfer learning by adapting the pre-trained model to the new device characteristics, achieving perfect model fit without re-executing the entire training process from scratch.
Solution Approach 2:
The patent uses copying by replicating the successful training approach across different device types. Instead of independently training each model from scratch, the system copies the learned representations and architectures from previously trained models and adapts them to new devices through transfer learning, significantly improving training efficiency while maintaining reliability.
Data Source
AI summary
A system for training transfer models that includes a memory and a processor coupling to each other. The memory stores instructions. The processor accesses the instructions to retrieve raw datasets from a first-type device and train a feature extraction model, a transfer feature and a preliminary classify model based on the raw datasets. The processor further transfers the feature extraction model, the transfer feature and the preliminary classify model to a host of a manufacturer so that the host directly applies the feature extraction model and the transfer feature, and retrains the preliminary classify model as an adapted classify model based on practical datasets.