Asset Performance Management System Operational Risk Assessment
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Solution Overview
Problem
APM systems face challenges in accurately identifying the causes of events and trends in physical assets within facilities, particularly in regulated environments like power plants, leading to insufficient reporting and inadequate maintenance prioritization.
Innovation Solution
The technology enhances APM systems by integrating event learning, operational risk assessment, and equipment health scoring to capture detailed event data, analyze root causes, and prioritize maintenance based on asset criticality and health scores, enabling better decision-making for equipment management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional APM systems are used for monitoring physical assets, then basic asset monitoring is achieved, but the ability to accurately identify causes of events and trends is insufficient
Solution Approach 1:
The system segments event data collection into multiple dimensions including event characteristics, asset information, environmental conditions, and maintenance history. This segmentation allows comprehensive capture of relevant information without overwhelming the system, enabling accurate cause identification through structured analysis of discrete data elements.
Solution Approach 2:
The system introduces an event learning module as an intermediary between raw sensor data and maintenance decision-making. This module processes and synthesizes data from multiple sources, identifying patterns and root causes that would be difficult to detect through traditional monitoring alone, thereby improving measurement precision of event causes.
2Reliability
If comprehensive event data is collected and analyzed, then root cause identification improves, but system complexity increases
Solution Approach 1:
The event learning module serves multiple functions simultaneously: it collects data from diverse sources, analyzes patterns, identifies root causes, and generates maintenance recommendations. This multi-functionality consolidates what would otherwise require separate systems into a single integrated solution, improving reliability without proportionally increasing complexity.
Solution Approach 2:
The system automatically learns from historical event data and continuously improves its analytical capabilities without requiring manual reconfiguration. The event learning module self-adjusts to identify emerging patterns and adapt to new equipment types, reducing the operational complexity of maintaining comprehensive monitoring systems.
3Ease of operation
If all assets are monitored with equal detail, then comprehensive asset visibility is achieved, but maintenance prioritization becomes difficult
Solution Approach 1:
The system applies different levels of monitoring detail to different assets based on their criticality and failure patterns. High-value or critical assets receive more intensive analysis and monitoring, while less critical assets receive standardized monitoring. This local differentiation enables effective maintenance prioritization by focusing resources on assets where detailed analysis provides the greatest value.
Solution Approach 2:
The system dynamically adjusts monitoring parameters and analysis depth based on asset condition, historical performance, and operational context. When an asset shows signs of degradation or enters a critical state, the system increases monitoring intensity and analytical depth for that specific asset, thereby optimizing the allocation of maintenance resources across the asset portfolio.
Data Source
AI summary
Disclosed are techniques for assessing and mitigating operational risk in a facility. The techniques can include accessing asset health scores for assets in a facility that indicate a likelihood that the assets will fail or be operationally impaired, identifying criticality scores that indicate a degree of importance of the assets to facility operations, determining, based on the asset health scores and the criticality scores, operational risk scores that indicate a risk posed to the ongoing operation of the facility or to the enterprise by the asset, determining actions and corresponding action prioritizations to recommend for the assets, ranking the plurality of assets based on the operational risk scores, and outputting, in a user interface, information identifying the assets ranked based on the operational risk scores.