Predictive Maintenance Models for Power Grid Safety
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Solution Overview
Problem
Electric power grid maintenance often faces challenges in ensuring worker safety and operational effectiveness due to varying conditions such as equipment failures, environmental hazards, and personnel training levels, which existing methods fail to adequately address.
Innovation Solution
A computing system uses machine learning models trained on historical maintenance data to predict outcomes of future maintenance actions, identifying critical factors and recommending specific equipment and procedures to mitigate risks, thereby enhancing the likelihood of successful and safe maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are used to predict maintenance outcomes, then safety and effectiveness of maintenance operations are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical maintenance data before actual maintenance operations. The models are pre-trained to predict outcomes, safety risks, and effectiveness metrics, allowing the system to provide informed recommendations in advance of maintenance activities without adding operational complexity during critical maintenance windows
Solution Approach 2:
The patent introduces an intermediary layer between historical data and maintenance decisions through machine learning models. These models act as mediators that process complex historical maintenance records, environmental conditions, and outcome data to generate simplified predictions and recommendations, reducing the complexity burden from raw data while maintaining high reliability
2Measurement precision
If comprehensive historical data from multiple utilities is used for training, then prediction accuracy is improved, but data privacy and security concerns increase
Solution Approach 1:
The system implements a universal training approach where historical maintenance data from multiple different utility companies is aggregated to train a single multi-functional machine learning model. This universal model learns from diverse data sources including different maintenance types, environmental conditions, and outcomes, improving prediction accuracy while the centralized controlled training process manages data privacy and security through standardized protocols
3Productivity
If real-time recommendations are provided to workers, then maintenance effectiveness is improved, but response time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-computing prediction models and maintaining them in a ready state before maintenance operations begin. When workers need recommendations during maintenance activities, the system leverages the pre-trained models to generate real-time suggestions without requiring extensive computational resources or time, as the heavy lifting of model training was completed in advance
Solution Approach 2:
The system implements feedback mechanisms where the machine learning models continuously learn from actual maintenance outcomes and update their predictions. This feedback loop improves maintenance effectiveness over time by refining recommendations based on real-world results, while the incremental learning approach minimizes computational overhead compared to complete re-training
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable storage devices, for predictive models for electric power grid maintenance. In some implementations, one or more computers receive data indicating a maintenance action to be performed for a utility system. The one or more computers generate outcome scores for the maintenance action using a model generated from records for maintenance performed by multiple utilities. The one or more computers select one or more maintenance plan elements based on the outcome scores, provide an indication of the selected one or more maintenance plan elements for display, and store a record corresponding to the maintenance action. In some examples, the selected one or more maintenance plan elements can include recommended plan elements to improve the safety and efficiency of the maintenance action.