IoT Predictive Modeling With Hybrid Ensemble Fault Consensus
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Solution Overview
Problem
Existing analytics models for IoT devices and machinery often result in inaccurate failure detection and a high rate of false positives due to their inability to distinguish between real and non-failure instances, which is exacerbated by the limitations of single physics-based or statistical models operating in isolation.
Innovation Solution
A hybrid ensemble model combining physical and statistical models, including machine learning and artificial intelligence, is used to generate predictions and consensus decisions, with a confidence threshold to filter out false positives and improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single physics-based or statistical analytics model is used to interpret sensor data, then the system can process data and generate predictions, but the failure detection accuracy is low and false positive rate is high
Solution Approach 1:
The patent combines multiple analytics models (physics-based models and statistical models) into a hybrid ensemble system. The system processes sensor data through multiple models simultaneously and aggregates their predictions, thereby improving failure detection accuracy while reducing false positives through consensus decision-making among diverse model types.
Solution Approach 2:
The patent creates a composite analytics system that integrates different types of models (physics-based and statistical) similar to how composite materials combine different substances. This hybrid ensemble leverages the strengths of each model type to achieve superior prediction reliability compared to individual models.
2Reliability
If multiple analytics models are combined to improve prediction accuracy, then the system can reduce false positives, but the device complexity increases
Solution Approach 1:
The patent segments the analytics system into distinct model components (physics-based models and statistical models) that operate independently but contribute to a unified prediction. This modular segmentation allows the system to manage complexity by organizing multiple models into structured, manageable units with clear interfaces.
Solution Approach 2:
The patent designs a universal ensemble framework that can accommodate multiple types of analytics models through a common architecture. This multi-functional system processes data through various model types using standardized interfaces, reducing the operational complexity despite the diversity of underlying models.
Data Source
AI summary
A computer implemented method for predicting equipment failure by monitoring equipment data, the method comprising: generating a first set of predictions by processing equipment data via a plurality of first models of data analysis and machine learning techniques; generating a second set of predictions by processing equipment data via a plurality of second models of data analysis and machine learning techniques; generating, using machine learning techniques, a consensus decision by comparing the first set of predictions and the second set of predictions; estimating, using machine learning techniques, a level of confidence for the consensus decision; and selectively disclosing the consensus decision qualifying a confidence threshold.