Automated Maintenance Pipeline Selection for Asset Risk Optimization
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Solution Overview
Problem
Existing asset maintenance techniques are myopic, tailored for specific asset classes and regions, and lack system-wide scope, requiring deep optimization skills and being time and effort intensive, especially when considering operator objectives, constraints, and operational dynamics.
Innovation Solution
A computer-implemented method that automatically selects a maintenance solution pipeline based on obtained information, utilizing artificial intelligence to generate risk estimations and optimize maintenance planning and scheduling for physical assets, allowing for reuse of the selected pipeline and integration of real-time data for updated risk models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customized predictive models are created for specific asset classes, regions and network structures, then measurement precision and reliability are improved, but device complexity and time consumption increase
Solution Approach 1:
The system segments the asset maintenance optimization into multiple independent pipelines, each specialized for specific asset classes, regions, or network structures. This allows customized predictive models to be created and maintained separately for different segments, improving accuracy for each segment while reducing the overall complexity of managing a single monolithic model across all assets.
Solution Approach 2:
The system enables dynamic adjustment of model parameters and configurations based on specific asset characteristics, regional requirements, and network structures. By allowing parameter changes rather than creating entirely new models, the system achieves customized predictions while reducing the time and effort required for model development.
2Ease of operation
If maintenance solutions are tailored for sub-networks instead of system-wide, then ease of operation and local adaptability are improved, but loss of information about system-wide interdependencies occurs
Solution Approach 1:
The system merges multiple sub-network maintenance pipelines into a unified system-wide optimization framework. Each sub-network maintains its own customized pipeline for local ease of operation, while the system-wide view integrates these pipelines to capture interdependencies between sub-networks, preventing information loss about system-wide relationships.
Solution Approach 2:
The system adds a hierarchical dimension to maintenance optimization, allowing analysis at multiple levels simultaneously - sub-network level for local operations and system-wide level for overall coordination. This dimensional approach enables local adaptability while preserving awareness of system-wide interdependencies through multi-level integration.
3Productivity
If automated pipeline selection is implemented, then productivity and time efficiency are improved, but device complexity increases
Solution Approach 1:
The system implements self-service automation where the pipeline selection process is autonomous and does not require deep optimization skills from operators. The system automatically selects appropriate maintenance pipelines based on input data characteristics, asset types, and operational requirements, improving productivity while keeping the complexity managed through standardized selection criteria and automated decision logic.
Data Source
AI summary
A maintenance solution pipeline is automatically selected from a plurality of maintenance solution pipelines, based on obtained information. The maintenance solution pipeline is to be used in providing a physical asset maintenance solution for a plurality of physical assets. Code and model rendering for the maintenance solution pipeline automatically selected is initiated. Output from an artificial intelligence process is obtained. The output includes an automatically generated risk estimation relating to one or more conditions of at least one physical asset of the plurality of physical assets. Code and model rendering for the maintenance solution pipeline is re-initiated, based on the output from the artificial intelligence process. The maintenance solution pipeline automatically selected is reused.