Remaining Useful Life Modeling With Pretrained LSTM Transfer Learning
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Solution Overview
Problem
Existing methods for predicting the remaining useful life of assets are inefficient and require significant manual effort, lack domain expertise, and fail to effectively utilize sensor data, leading to challenges in accurately estimating the lifespan of critical assets.
Innovation Solution
Utilizing a pretrained Long Short-Term Memory (LSTM) neural network initialized with weights and layers from related asset classes, combined with a novel scoring function to penalize over-estimation, and integrating with enterprise asset management systems for optimized predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to predict remaining useful life of assets, then manual effort and domain expertise are required, but the process is inefficient and requires significant time and resources
Solution Approach 1:
The patent applies preliminary action by pre-training neural network models on diverse asset data before deployment. The pre-trained models contain learned features and patterns that can be transferred to specific asset prediction tasks, eliminating the need for extensive manual feature engineering and domain expertise during deployment. This preliminary preparation significantly reduces training time and improves prediction efficiency.
Solution Approach 2:
The patent uses copying by transferring knowledge from pre-trained models to new prediction tasks. Instead of training models from scratch for each asset, the system copies learned representations and patterns from source domains to target domains, enabling rapid adaptation and prediction with minimal additional training data and time.
2Measurement precision
If traditional prediction methods are used, then simple models can be trained quickly, but they fail to accurately estimate the lifespan of critical assets
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting model complexity and architecture based on the specific prediction task and available data. The system selects and configures neural network parameters such as layer depth, width, and activation functions to optimize both accuracy and training efficiency for different asset types and degradation patterns.
Solution Approach 2:
The patent uses segmentation by dividing the prediction problem into multiple stages: feature extraction, degradation pattern recognition, and remaining useful life estimation. This segmented approach allows the system to apply different model complexities to different aspects of the prediction task, improving overall accuracy while managing computational resources effectively.
3Measurement precision
If domain expertise is incorporated into the prediction system, then prediction accuracy improves, but the system becomes more complex and requires manual intervention
Solution Approach 1:
The patent applies self-service by enabling the system to automatically acquire and integrate domain expertise through unsupervised learning and data-driven feature engineering. The neural network models automatically learn degradation patterns and domain-specific features from operational data without requiring manual intervention or explicit domain knowledge input, maintaining high accuracy while ensuring ease of operation.
4Loss of information
If sensor data is extensively utilized for prediction, then more information is available for analysis, but data processing becomes more complex and time-consuming
Solution Approach 1:
The patent applies extraction by selectively extracting relevant features and patterns from sensor data using the pre-trained neural network models. Instead of processing all raw sensor data, the system extracts only the most informative features related to asset degradation, significantly reducing data processing time while maintaining comprehensive information utilization for accurate predictions.
Data Source
AI summary
Methods, computer program products, and/or systems are provided that perform the following operations: obtaining asset data; determining an asset class associated with the asset data; initializing a new neural network model, wherein the new neural network model is initialized based on a pretrained model associated with the asset class; training the new neural network model based, at least in part, on the asset data to obtain a trained remaining useful life model; and deploying the trained remaining useful life model to generate prediction data for one or more assets as output of the trained remaining useful life model.