Neural Network Ensembles for Reproducible Maintenance Predictions
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Solution Overview
Problem
Deep learning models used in predictive maintenance face challenges in reproducibility due to inherent randomness, leading to inconsistent repair actions and increased equipment downtime, especially when re-trained with new data, affecting both equipment availability and customer satisfaction.
Innovation Solution
The creation of an ensemble of neural network models that combines outputs from multiple models trained on different data generations, using techniques such as sampling and varying architectures, to improve reproducibility and accuracy without increasing training time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning models are re-trained with new data to incorporate recent maintenance actions, then the model accuracy and adaptability improve, but the reproducibility and consistency of repair actions deteriorate due to randomness in training and data distribution changes
Solution Approach 1:
The patent combines multiple independently trained deep learning models into an ensemble system. Each model is trained on different data generations or with different random seeds, and their predictions are aggregated through voting or averaging. This merging approach maintains adaptability to recent maintenance actions while improving reproducibility by reducing the impact of random variations in any single model.
Solution Approach 2:
The patent changes the parameter of model architecture by creating multiple models with different architectures or training configurations. By varying architectural parameters and training parameters across multiple models, the system achieves better generalization and reproducibility while maintaining adaptability to new maintenance data through ensemble aggregation.
2Reliability
If multiple models are created to improve reproducibility, then the reliability and consistency of predictions improve, but the training time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by training multiple models in advance on historical data before deployment. These pre-trained models are then combined into an ensemble that can make consistent predictions without requiring additional training time during operational use. The preliminary training phase distributes the computational burden over time rather than concentrating it during critical decision-making moments.
Solution Approach 2:
The patent creates copies of base model architectures and trains them with different random seeds or on different data subsets. These copied models serve as ensemble members that can be deployed simultaneously. The copying approach allows parallel training of multiple models, which reduces total training time compared to sequential training, while maintaining the reliability benefits of ensemble diversity.
3Reliability
If ensemble methods are used to improve reproducibility, then the reliability of repair recommendations improves, but the system complexity increases
Solution Approach 1:
The patent segments the ensemble system into distinct modular components: individual base models, an aggregation layer, and a unified interface. Each model is an independent module that can be developed, trained, and maintained separately. The aggregation layer provides a standardized interface that combines predictions from multiple models without requiring complex integration logic, thereby managing system complexity while maintaining reliability.
Solution Approach 2:
The patent designs the ensemble system with universal components that can handle multiple functions. The same base model architecture serves as a template for creating multiple ensemble members, and the aggregation mechanism works universally across different model types and data generations. This multi-functionality reduces system complexity by reusing proven components rather than creating specialized solutions for each function.
Data Source
AI summary
Example implementations described herein involve systems and methods for generating an ensemble of deep learning or neural network models, which can involve, for a training set of data, generating a plurality of model samples for the training set of data, the plurality of model samples generated from deep learning or neural network methods; and aggregating output of the model samples to generate an output of the ensemble models.