Knowledge-Based AI Architecture for Industrial Prediction
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Solution Overview
Problem
Industrial systems face challenges in developing artificial intelligence techniques due to the lack of comprehensive training data sets, particularly for rare failure situations, leading to inaccurate predictions by machine learning models.
Innovation Solution
A knowledge-based AI system combines a rule-based knowledge model with a machine learning model, using an ensemble model to generate predictions and incorporating domain expertise through a knowledge translator that converts natural language into a domain-specific language for improved training and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained using available training data sets, then the models can make predictions for common operational situations, but the models are likely to fail in rare failure situations due to incomplete training data
Solution Approach 1:
The system performs preliminary action by using the knowledge model to generate synthetic failure data before the machine learning model is deployed. This synthetic data is then used to pre-train or fine-tune the machine learning model, ensuring it has exposure to failure scenarios even though these scenarios are rare in actual operational data. This preliminary preparation improves the model's reliability in handling failure situations.
Solution Approach 2:
The knowledge model serves as an intermediary between domain expertise and the machine learning model. It translates expert knowledge about failure modes into synthetic training data, which then bridges the gap between the limited real-world failure data and the machine learning model's need for comprehensive training examples. This intermediary enables the ML model to learn from both real and synthesized failure scenarios.
2Reliability
If a rule-based knowledge model is used to incorporate domain expertise, then predictions can be made using expert knowledge, but the system complexity increases due to integrating multiple modeling approaches
Solution Approach 1:
The system segments the prediction task into two distinct components: a knowledge model that handles rule-based expert knowledge and a machine learning model that handles pattern recognition from data. Each model operates independently on the same input features but produces separate predictions. This segmentation allows each component to specialize in what it does best while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system merges the outputs of the knowledge model and machine learning model through an ensemble mechanism. The predictions from both models are combined, typically through voting or weighted averaging, to produce a final prediction. This merging leverages the strengths of both approaches: the interpretability and rule-based reasoning of the knowledge model and the pattern recognition capabilities of the machine learning model, achieving improved reliability despite increased complexity.
Data Source
AI summary
A system makes predictions using a machine learning model combined with a knowledge model. The system provides input data to a knowledge model and a machine learning based model. The machine learning based model is trained to make predictions based on input data. The system provides the outputs of the machine learning based model and the knowledge model to an ensemble model configured to combine results of the knowledge model and the machine learning based model. The system can be used for several applications. For example, the system may classify an input text based on a hierarchy of categories. The system may perform fault detection in time series data by identifying an anomaly data point and predicting whether the anomaly data point is a fault.