Asset Management System Failure Mode Ranking
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Solution Overview
Problem
Managing the operational performance and maintenance of complex electromechanical assets, such as power generation systems, is challenging due to the complexity of mechanical and electrical components and the need for effective anomaly detection and failure prevention in stressful environments.
Innovation Solution
An asset management system that utilizes failure mode ranking to generate maintenance outputs by integrating operational data, failure mode models, and configuration data to recommend analytics configurations, reducing potential failure modes through data-driven adjustments to asset configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional sensor monitoring systems are used to track operational parameters, then basic anomaly detection is achieved, but the system cannot effectively predict or prevent failures in complex electromechanical assets
Solution Approach 1:
The system performs preliminary failure mode analysis by pre-configuring analytics for potential failure modes before they occur. Failure mode models and analytics configurations are established in advance, allowing the system to proactively predict and prevent failures rather than merely detecting anomalies after they occur.
Solution Approach 2:
The system dynamically adjusts analytics configurations based on operational data and failure mode rankings. By changing parameters such as sensor thresholds, monitoring frequencies, and analytics priorities according to asset conditions, the system optimizes failure prediction capability without requiring complete system redesign.
2Loss of information
If comprehensive sensor coverage is implemented to monitor all operational parameters, then detailed asset performance data is obtained, but the complexity of managing and analyzing the data increases significantly
Solution Approach 1:
The system extracts and focuses on only the most critical operational parameters and failure modes based on ranking analysis. Instead of attempting to analyze all sensor data equally, the system identifies and extracts the most significant data elements related to high-ranking failure modes, reducing analytical complexity while maintaining information completeness.
Solution Approach 2:
The patent introduces an intermediary layer of failure mode models and analytics configurations that mediate between raw sensor data and maintenance decisions. This intermediary layer processes and structures comprehensive operational data into meaningful failure mode assessments, simplifying the path from data collection to actionable insights.
3Reliability
If frequent maintenance is performed to prevent failures, then asset reliability is improved, but operational productivity decreases due to increased maintenance downtime
Solution Approach 1:
The system dynamically adjusts maintenance schedules and analytics monitoring intensity based on real-time asset conditions and failure mode rankings. When failure risk is low, maintenance frequency is reduced to maintain productivity. When failure risk increases, the system intensifies monitoring and schedules maintenance proactively, optimizing the balance between reliability and productivity.
Solution Approach 2:
The system implements continuous feedback loops where operational data informs failure mode rankings, which in turn guide maintenance decisions. This feedback mechanism allows the system to learn from actual asset performance and adjust maintenance strategies accordingly, preventing both over-maintenance and under-maintenance scenarios.
Data Source
AI summary
This disclosure provides systems for using failure mode ranking in an asset management system to generate asset maintenance outputs. Operational data, failure mode models, and configuration data for an asset, such as a complex electromechanical system, are related to failure prevention analytics configurations through failure mode rankings to enable the asset management system to reduce a future ranking of potential failure modes by changing the present configuration of the asset to include a recommended failure prevention analytics configuration.