Asset Performance Benchmarking Using Condition-Aware Reference Curves
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Solution Overview
Problem
Conventional benchmarking methods often compare asset performance against ideal conditions that differ from actual operating conditions, leading to unfair rankings and inefficient maintenance, as they fail to account for specific operating conditions and deviations from design conditions.
Innovation Solution
A processor-implemented method that uses condition-aware reference curves to compute inter-asset and intra-asset metrics based on the first and second laws of thermodynamics, allowing for real-time monitoring and analysis of asset performance in two dimensions, enabling fair comparisons across different domains and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If asset performance is benchmarked against ideal design conditions, then the benchmarking process is simple and standardized, but the benchmarking results are unfair and inaccurate because actual operating conditions differ from design conditions
Solution Approach 1:
The patent transforms the benchmarking approach by changing the reference parameter from fixed design conditions to dynamic condition-aware reference curves that adapt to actual operating conditions. These curves are generated by adjusting parameters such as load, ambient temperature, and operating conditions to match real-time asset performance, enabling accurate benchmarking that reflects actual operating environments rather than idealized design conditions.
2Ease of operation
If conventional benchmarking methods are used, then the implementation is straightforward, but the system fails to account for deviations from design conditions leading to inappropriate asset rankings
Solution Approach 1:
The patent introduces dynamics into the benchmarking process by implementing condition-aware reference curves that continuously adapt to changing operating conditions. Instead of static design condition comparisons, the system dynamically adjusts reference curves based on actual operating parameters such as load variations, ambient conditions, and asset-specific characteristics, ensuring reliable and fair asset rankings that reflect real-world performance.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring actual asset performance and comparing it against condition-aware reference curves. The reference curves themselves are generated through feedback from historical performance data and operating conditions, creating a self-adjusting benchmarking system that improves accuracy over time while maintaining operational simplicity through automated comparisons.
3Ease of manufacture
If assets are selected off-the-shelf rather than custom-designed, then acquisition is faster and cheaper, but benchmarking against design conditions produces unfair comparisons since assets are not optimized for specific operating conditions
Solution Approach 1:
The patent creates a universal benchmarking solution that works across diverse asset types and operating conditions through condition-aware reference curves. Instead of requiring custom-designed assets for each specific condition, the system provides a multi-functional benchmarking approach that adapts to various asset configurations and operating environments, making the benchmarking process universally applicable while fairly accounting for different adaptability levels.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach identifies poor performers and facilitates targeted analysis by providing a comprehensive two-dimensional benchmarking system that accounts for actual operating efficiencies, reducing downtime and operating costs by accurately assessing asset performance relative to its maximum achievable efficiency.
Implementation Method 1
computing, by the one or more hardware processors, an inter-asset metric for each of the plurality of assets using the monitored one or more parameters at each instance of time and a corresponding condition-aware reference curve, wherein the inter-asset metric is an efficiency metric based on the first law of thermodynamics
Implementation Method 2
computing, by the one or more hardware processors, an intra-asset metric for each of the plurality of assets at each instance of time, using the computed inter-asset metric and the maximum operating efficiency of a corresponding asset from the plurality of assets, wherein the intra-asset metric is an efficiency metric based on the second law of thermodynamics
Data Source
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AI summary
Traditionally, benchmarking of asset performance involves comparing actual performance with ideal values that correspond to test conditions which may not be realized in practice leading to inappropriate ranking of the assets. Systems and methods of the present disclosure use condition-aware reference curves for estimating the maximum possible operating efficiencies (under specific operating conditions) instead of the theoretical maximum efficiencies. The reference curves are received from the manufacturer or obtained from on-site test results. Benchmarking is then performed based on two dimensions, viz., an inter-asset metric and an intra-asset metric that are analogous to the first law and second law of thermodynamics respectively. The two-dimensional benchmarking then helps in identifying inefficient assets that may be analyzed further for finding the root cause. Tracking the performance of assets over time greatly helps in operations and maintenance, and thus reducing downtime of systems and accordingly the operating costs.