Saliency Map Root Cause Analysis for Prediction Residuals
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Solution Overview
Problem
Existing machine learning systems struggle to accurately identify root causes of anomalies and industrial faults due to limitations in model architectures, leading to inefficiencies in fault detection and increased computational costs.
Innovation Solution
A two-model architecture is employed, where a first machine-learned model predicts a value of interest, and a second model generates a saliency map to identify inputs contributing to prediction residuals, thereby pinpointing likely root causes of high prediction errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single machine learning model is used for prediction, then the model architecture is simple, but the accuracy in identifying root causes of anomalies is insufficient
Solution Approach 1:
The system divides the root cause analysis task into two separate models: a prediction model that forecasts target values and an explanation model that generates saliency maps to identify root causes. This segmentation allows each model to specialize in its specific function, improving overall root cause identification accuracy while maintaining reasonable individual model complexity.
Solution Approach 2:
The explanation model acts as an intermediary between the prediction model and the root cause analysis. It takes the prediction residuals and input features as inputs and produces saliency maps that highlight the contribution of each input feature to prediction errors, thereby enabling accurate root cause identification without requiring the prediction model itself to be overly complex.
2Measurement precision
If advanced model architectures are used to improve root cause detection accuracy, then the detection accuracy improves, but the computational costs increase
Solution Approach 1:
The system extracts the explanation and root cause identification function from the complex prediction model and places it in a separate, lighter explanation model. This explanation model generates saliency maps using gradient-based methods that are computationally efficient, thereby maintaining high anomaly detection accuracy while reducing the overall computational burden compared to using a single complex model.
Solution Approach 2:
The explanation model uses gradient-based saliency map generation that leverages the existing parameters and gradients from the prediction model without requiring additional heavy training. By reusing the prediction model's learned parameters and computing gradients efficiently, the system achieves accurate anomaly detection with reduced computational costs compared to training a single complex end-to-end model.
3Reliability
If a single model performs both prediction and explanation, then the system is simpler to operate, but the reliability of root cause analysis is reduced
Solution Approach 1:
The system segments the prediction and explanation functions into separate models, which improves the reliability of root cause analysis by allowing each model to optimize for its specific task. The prediction model focuses on accurate forecasting while the explanation model focuses on generating reliable saliency maps, thereby enhancing overall system reliability despite increased operational steps.
Solution Approach 2:
The explanation model uses prediction residuals as feedback to identify root causes. By continuously monitoring the difference between predicted and actual values and using this residual information to generate saliency maps, the system creates a feedback loop that improves the reliability of root cause analysis. This feedback mechanism ensures that the explanation is grounded in actual prediction errors, enhancing reliability.
Data Source
AI summary
Systems and methods are provided. A method includes providing, by a computing system comprising one or more computing devices, a plurality of input values to a first machine-learned model. The method includes generating, by the computing system using the first machine-learned model based on the plurality of input values, a saliency map. In the method, the first machine-learned model is a model that was trained to predict a prediction residual associated with a second machine-learned model.