AI Anomaly Detection With Reconstruction Error Explanation
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Solution Overview
Problem
Existing AI-based anomaly detection systems, particularly those using auto-encoders, lack transparency in explaining which input variables contribute to high reconstruction errors, hindering trust and effective error correction in industrial processes.
Innovation Solution
A method utilizing an auto-encoder for anomaly detection in technical devices, combined with an evaluation device and analysis device, to determine and classify reconstruction errors, and generate rules for improving the system based on historical data and variable importance, enabling accurate cause analysis and automated correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an auto-encoder AI model is used for anomaly detection, then the detection effectiveness is improved, but the interpretability and explainability of the detection results deteriorate
Solution Approach 1:
The patent introduces an intermediary explanation module that acts as a bridge between the auto-encoder's internal representations and human interpreters. This module generates natural language explanations that translate the model's reconstruction error analysis into understandable insights about which input variables and their relationships caused the anomaly, thereby maintaining detection effectiveness while restoring interpretability.
Solution Approach 2:
The patent segments the anomaly explanation into distinct components: identification of problematic input variables, analysis of their states/values, examination of relationships between variables, and generation of actionable insights. This segmentation allows the complex interpretation task to be broken down into manageable parts that can be systematically processed and presented.
2Loss of information
If simpler AI models like decision trees are used in parallel with deep learning models, then the interpretability is improved, but the device complexity increases
Solution Approach 1:
The patent extracts the interpretability function from separate parallel models and integrates it directly into the auto-encoder architecture through explanation modules. This extraction eliminates the need for maintaining separate simpler models, as the explanation capabilities are now embedded within the deep learning model itself, reducing overall system complexity while preserving interpretability.
3Measurement precision
If more detailed analysis of input variables and their relationships is performed, then the diagnostic accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary analysis during the training phase, where the model learns not only to minimize reconstruction error but also to identify and weigh the importance of different input variables and their relationships. This preliminary action embeds diagnostic knowledge into the model weights and structures, enabling accurate variable prioritization during runtime without requiring complex real-time computations.
Solution Approach 2:
The patent changes the parameters being optimized during training to include not only reconstruction error but also metrics related to variable importance and relationship strength. By modifying the objective function and training parameters, the model automatically learns to prioritize the most diagnostic variables and relationships, achieving high diagnostic accuracy with computationally efficient inference.
Data Source
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AI summary
The invention relates to a computer-implemented method for anomaly detection during the control of a technical device (TD) by a control means (PLC), comprising the following steps: a) providing input data (IN), coding the input data (IN) with the aid of an auto-encoder to form coded data (C), and decoding the coded data (C) with the aid of the auto-encoder to form output data (OUT), and determining and classifying a reconstruction error (RE), b) determining a target variable (TV) from the reconstruction error (RE) if the reconstruction error (RE) exceeds a predefined reconstruction error threshold (RET), c) providing a model on the basis of artificial intelligence to an evaluation device (XPL), d) determining, by the evaluation device (XPL), a prediction for the target variable (TV) using the model and the classification of the reconstruction error (RE), and providing the prediction to an analysis device (REA), e) determining, by the analysis device (REA), a rule for improving the reconstruction error (RE) from the prediction and a predefined rule set, providing the rule to the control means (PLC) and controlling, by the control means (PLC), the technical device (TD) with the aid of the rule.