ESP Failure Analysis Using Image-Based Machine Learning
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Solution Overview
Problem
Failure analysis of electrical submersible pump equipment is limited to domain experts due to the lack of accessible diagnostic tools and knowledge, restricting the ability of non-specialists to diagnose failures effectively.
Innovation Solution
Utilizing machine learning models trained on data including images, specifications, and reliability reports of ESP equipment to categorize failures, generate captions, and determine causes, enabling engineers to diagnose issues through a failure diagnostic tool.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If failure analysis is performed only by domain experts with previous experience, then diagnostic accuracy is improved, but accessibility and ease of operation deteriorate
Solution Approach 1:
The patent introduces an automated image analysis system that acts as an intermediary between the equipment failure and the human expert. The system processes images of failed equipment components, automatically identifies damage patterns, and presents findings to users regardless of their expertise level. This intermediary tool bridges the gap between expert-level diagnostic accuracy and broad accessibility.
Solution Approach 2:
The system creates a digital copy or representation of the physical equipment failure through image capture and analysis. By working with image data rather than requiring direct physical inspection expertise, the system replicates the diagnostic process in a form that is accessible to non-experts while maintaining diagnostic accuracy through automated pattern recognition.
2Ease of operation
If automated image analysis is implemented, then ease of operation and accessibility are improved, but measurement precision and reliability may deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-processing images, enhancing features, and preparing data before final analysis. This preliminary processing ensures that the automated analysis starts with optimized input data, improving the reliability of subsequent diagnostic decisions and reducing errors that might occur with raw, unprocessed images.
Solution Approach 2:
The system incorporates feedback mechanisms where analysis results are continuously refined based on comparison with known failure patterns and expert-validated data. This feedback loop ensures that the automated system learns from and adapts to improve diagnostic accuracy, maintaining high measurement precision while preserving ease of operation.
3Reliability
If multiple stages of equipment evaluation are maintained (assembly, testing, installation, removal, dismantling), then reliability of failure analysis is improved, but loss of time and complexity increase
Solution Approach 1:
The patent segments the failure analysis process into distinct phases corresponding to different equipment lifecycle stages (assembly, testing, installation, removal, dismantling). Each phase has specific analysis protocols and image capture requirements. This segmentation allows the system to focus on relevant failure modes for each stage, improving reliability by ensuring appropriate analysis depth while reducing overall time by avoiding unnecessary analysis at each stage.
Solution Approach 2:
The system applies partial analysis at each equipment stage, performing only the necessary level of inspection appropriate to that phase. Rather than conducting full comprehensive analysis at every stage, the system performs targeted analysis focused on failure modes most likely to occur at that specific point in the equipment lifecycle, thereby maintaining reliability while significantly reducing time investment.
Data Source
AI summary
Techniques for electrical submersible pump equipment fault analysis include training, using first training data, one or more machine learning models to categorize previously unseen images of electrical submersible pump equipment into one or more categories of a plurality of categories. The techniques further include training, using second training data, the one or more machine learning models to generate captions for the previously unseen images of electrical submersible pump equipment.