Power Asset Anomaly Diagnosis Using Consolidated Root Cause Models
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Solution Overview
Problem
Manual diagnostic processes for identifying the root cause of performance anomalies in power generating assets are time-consuming and inadequate for complex issues, failing to efficiently address performance anomalies in renewable and nonrenewable energy systems.
Innovation Solution
A machine-learning-based analytic system that generates a consolidation model to determine the actual root cause of performance anomalies by analyzing operational data sets from sensors, incorporating predictive models to identify potential root causes and their probabilities, and validating through engineering data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual engineering diagnostic processes are used to identify the root cause of performance anomalies, then diagnostic coverage can address complex issues through human expertise, but the process becomes time-consuming and inefficient
Solution Approach 1:
The diagnostic process is segmented into multiple independent predictive models, each specializing in analyzing specific operational data sets (vibration, temperature, power output, etc.). This segmentation allows parallel processing of different data types while maintaining comprehensive diagnostic coverage, resolving the contradiction between thorough analysis and time efficiency.
Solution Approach 2:
Manual engineering diagnostic processes are replaced with an automated machine learning system that uses predictive models and a consolidation model to identify root causes. This substitution eliminates human intervention time while maintaining diagnostic accuracy through algorithmic analysis of operational data sets.
2Measurement precision
If multiple predictive models are used to analyze different operational data sets, then diagnostic accuracy improves through comprehensive analysis, but system complexity increases
Solution Approach 1:
Multiple predictive models that analyze different operational data sets are merged through a consolidation model. This consolidation model integrates the outputs of individual predictive models (vibration analysis, temperature analysis, power output analysis, etc.) to determine the actual root cause, achieving comprehensive diagnostic accuracy while managing system complexity through unified architecture.
Solution Approach 2:
The consolidation model serves as a universal component that handles the integration and final decision-making for all predictive models. This multi-functional element processes various types of operational data through a single unified interface, reducing overall system complexity while maintaining the benefits of specialized analysis.
3Adaptability or versatility
If manual diagnostic processes are used, then engineering expertise can be applied to complex performance issues, but the process is incapable of addressing inadequately studied performance issues
Solution Approach 1:
The diagnostic system performs self-service through continuous learning from operational data sets. The machine learning models automatically adapt to new performance anomalies and patterns without requiring manual engineering intervention for each new issue, enabling the system to handle inadequately studied performance issues while maintaining high diagnostic effectiveness.
Solution Approach 2:
The system incorporates feedback mechanisms where the consolidation model receives input from multiple predictive models and continuously refines root cause determination based on operational data patterns. This feedback loop enables the system to adapt to new performance issues and improve diagnostic versatility while maintaining reliability.
Data Source
AI summary
A system and method are provided for operating a power generating asset. Accordingly, a plurality of operational data sets are received by a controller. The operational data sets include at least one indication of a performance anomaly. A plurality of predictive models are implemented by the controller to determine a plurality of potential root causes of the performance anomaly and a plurality of corresponding probabilities for each of the potential root causes. A consolidation model is generated for classifying the plurality of potential root causes and corresponding probabilities. The consolidation model is trained via a training data set to correlate the plurality of potential root causes to an actual root cause for the performance anomaly. The consolidation model is implemented by the controller to determine the actual root cause of the performance anomaly based on the plurality of potential root causes and corresponding probabilities.