Unified Deep Learning for Consistent Maintenance Predictions
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Solution Overview
Problem
Existing predictive maintenance approaches rely on separate architectures for failure prediction and Remaining Useful Life (RUL) estimation, leading to inconsistent model outputs and decreased decision-maker confidence, which complicates maintenance scheduling and increases operating costs.
Innovation Solution
A single deep learning architecture that supports multiple modes, including failure prediction, RUL estimation, and a unified mode, allowing for consistent predictions by learning parameters through historical data and applying transformation functions to generate maintenance recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate architectures are used for failure prediction and RUL estimation, then each model can be optimized independently, but the predictions become inconsistent and decision-maker confidence decreases
Solution Approach 1:
The patent merges separate failure prediction and RUL estimation architectures into a single unified deep learning architecture. This unified model processes sensor data through shared layers and produces both failure probability predictions and RUL estimates simultaneously, ensuring internal consistency between the two prediction types while maintaining the ability to optimize each objective function independently through multi-task learning.
Solution Approach 2:
The unified deep learning architecture is designed to perform multiple functions: it can operate in failure prediction mode, RUL estimation mode, or both simultaneously. The model accepts sensor data as input and generates multiple types of maintenance predictions through a single architecture, eliminating the need for separate specialized models while maintaining optimization capabilities for each prediction type.
2Measurement precision
If multiple separate models are used for different maintenance predictions, then each prediction type can be specialized, but maintenance scheduling becomes complicated and operating costs increase
Solution Approach 1:
The patent combines multiple specialized prediction models into a single unified architecture that handles both failure prediction and RUL estimation. This reduces the overall system complexity by eliminating the need to manage, train, and deploy separate models, while still providing specialized predictions through dedicated output layers and objective functions within the unified framework.
3Productivity
If separate independently developed models predict failure and RUL, then each model can be optimized for its specific objective, but predictive maintenance scheduling becomes difficult due to inconsistent predictions
Solution Approach 1:
The unified deep learning architecture integrates failure prediction and RUL estimation into a single coherent system that produces consistent predictions. By sharing underlying features and processing pathways, the model ensures that failure probability and RUL estimates are internally consistent, making maintenance scheduling straightforward while maintaining optimization efficiency through multi-task learning objectives.
Data Source
AI summary
Example implementations described herein involve a system for maintenance predictions generated using a single deep learning architecture. The example implementations can involve managing a single deep learning architecture for three modes including a failure prediction mode, a remaining useful life (RUL) mode, and a unified mode. Each mode is associated with an objective function and a transformation function. The single deep learning architecture is applied to learn parameters for an objective function through execution of a transformation function associated with a selected mode using historical data. The learned parameters of the single deep learning architecture can be applied with streaming data from with the equipment to generate a maintenance prediction for the equipment.