Graph-Based Predictive Maintenance for Temporal Failure Detection
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Solution Overview
Problem
Current deep learning methods are inadequate for predicting system device failure in complex systems, as they fail to effectively utilize historical records and profiles to identify maintenance-ready components in a timely and efficient manner.
Innovation Solution
The implementation of graph-based predictive maintenance (GBPM) using a trained ensemble classification model that processes node features and graph features from attributed temporal graphs to predict components requiring maintenance, thereby scheduling maintenance optimally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current deep learning methods are used for predicting system device failure, then the system can process data, but it fails to effectively utilize historical records and profiles to identify maintenance-ready components in a timely and efficient manner
Solution Approach 1:
The patent segments the system into multiple specialized components: graph construction module that builds attributed temporal graphs from historical records, feature extraction module that derives node and graph features, ensemble classification model that integrates multiple algorithms (Random Forest, Gradient Boosting, XGBoost), and prediction module that generates maintenance recommendations. This segmentation allows each component to specialize in specific tasks, improving both detection accuracy and processing efficiency.
Solution Approach 2:
The system performs preliminary actions by pre-processing historical records and profiles to construct attributed temporal graphs before actual prediction tasks. Node features (component attributes, operational parameters) and graph features (topological structure, temporal evolution) are extracted and stored in advance, enabling rapid prediction when maintenance decisions are needed without re-processing raw historical data.
2Measurement precision
If graph-based predictive maintenance with ensemble classification models is implemented, then detection accuracy of maintenance-ready components is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The patent merges multiple classification algorithms (Random Forest, Gradient Boosting, XGBoost) into an ensemble classification model that combines their strengths. The system also merges different feature types (node features representing component attributes and graph features representing system relationships) into a unified prediction framework, achieving higher accuracy than any single algorithm or feature type alone.
Solution Approach 2:
The attributed temporal graph serves as an intermediary structure that bridges raw historical records and the ensemble classification model. The graph construction module transforms complex historical data into structured graphs with meaningful node and edge relationships, while the feature extraction module converts graph structures into numerical features suitable for classification, simplifying the overall system architecture.
3Reliability
If historical records and profiles are extensively processed to quantify failure risk, then reliability prediction is enhanced, but data processing time and computational resources increase
Solution Approach 1:
The system extracts only the most relevant information from extensive historical records by identifying and extracting node features (component attributes, operational parameters, maintenance history) and graph features (topological structure, temporal evolution patterns). This selective extraction focuses computational energy on critical data elements rather than processing all historical data uniformly, improving reliability prediction while reducing computational burden.
Solution Approach 2:
The patent transforms historical records into different parameter representations through feature engineering. Historical data is converted from raw operational logs into structured node features and graph features with specific mathematical properties suitable for machine learning algorithms. This parameter transformation enables efficient processing by the ensemble classification model while preserving the essential information needed for reliable failure prediction.
Data Source
AI summary
Systems and methods for predicting system device failure are provided. The method includes performing graph-based predictive maintenance (GBPM) to determine a trained ensemble classification model for detecting maintenance ready components that includes extracted node features and graph features. The method includes constructing, based on testing data and the trained ensemble classification model, an attributed temporal graph and the extracted node features and graph features. The method further includes concatenating the extracted node features and graph features. The method also includes determining, based on the trained ensemble classification model, a list of prediction results of components that are to be scheduled for component maintenance.