Multi-Task Predictive Maintenance with Incomplete Labels
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Solution Overview
Problem
Predictive maintenance models for equipment often produce inconsistent results when applied simultaneously due to the requirement of complete labels for multi-task learning, which is impractical in industrial settings with incomplete label information, especially for tasks like failure prediction, remaining useful life estimation, and performance degradation detection.
Innovation Solution
A novel multi-task learning methodology that handles incomplete label information by using a unified approach with generic and task-specific layers, allowing for simultaneous learning of failure prediction, remaining useful life estimation, fault detection, and performance degradation detection within a single model, and incorporates a novel constraint loss to utilize non-failure data without pre-prediction steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If separate models are used for different predictive maintenance tasks, then each model can be trained independently, but the models produce inconsistent results when applied simultaneously
Solution Approach 1:
The patent combines multiple separate predictive maintenance models into a single unified multi-task learning model that simultaneously performs failure prediction, remaining useful life estimation, fault detection, and performance degradation detection. This integration ensures consistent predictions across all tasks while maintaining the ability to train on independent data sources for each task type.
Solution Approach 2:
The unified model is designed to perform multiple predictive maintenance functions simultaneously through a shared backbone network with task-specific output layers. This multi-functional architecture allows the system to handle diverse prediction tasks (failure prediction, RUL estimation, fault detection, performance degradation) within a single model framework, ensuring consistency across all predictions.
2Reliability
If multi-task learning is used to ensure consistent predictions, then prediction consistency is improved, but complete label information is required for every task which is impractical in industrial settings
Solution Approach 1:
The patent implements a multi-task learning framework where the model can operate with partial label availability. Each task can contribute to the overall learning process based on the data that is available, rather than requiring complete labels for all tasks. This allows the system to function effectively in industrial settings where only certain tasks have labeled data while still maintaining prediction consistency across all tasks.
Solution Approach 2:
The system dynamically adjusts the contribution of different tasks to the loss function based on label availability. When certain tasks lack complete labels, their corresponding loss components are adjusted or weighted differently, allowing the model to learn from available data while maintaining the structural integrity needed for consistent predictions across all tasks.
3Reliability
If unified multi-task model is used, then prediction consistency is achieved, but the model complexity increases
Solution Approach 1:
The unified model is segmented into a shared backbone network that extracts common features and task-specific output layers that generate predictions for each task. This segmentation allows the model to share computational resources across tasks while maintaining specialized processing for each prediction type, reducing overall complexity compared to fully independent models.
Solution Approach 2:
The shared backbone network serves multiple functions by extracting features that are useful for all predictive maintenance tasks simultaneously. This universal feature extraction reduces redundancy and simplifies the overall model architecture while maintaining the ability to perform diverse prediction functions through task-specific output layers.
Data Source
AI summary
Example implementations described herein involve, for data having incomplete labeling to generate a plurality of predictive maintenance models, processing the data through a multi-task learning (MTL) architecture including generic layers and task specific layers for the plurality of predictive maintenance models configured to conduct tasks to determine outcomes for one or more components associated with the data, each task specific layer corresponding to one of the plurality of predictive maintenance models; the generic layers configured to provide, to the task specific layers, associated data to construct each of the plurality of predictive maintenance models; and executing the predictive maintenance models on subsequently recorded data.