Pump System Card Data Monitoring for Early Fault Control
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Solution Overview
Problem
Existing systems lack efficient methods for monitoring and controlling the operational conditions of pump systems in reservoirs, leading to inefficiencies and potential damage due to issues like sand influx and equipment wear.
Innovation Solution
Implementing a machine learning model to analyze data from pump systems and generate card format data for real-time monitoring and control, allowing for dynamic adjustments and proactive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional monitoring methods are used for pump systems, then system simplicity is maintained, but operational efficiency and early issue detection capability deteriorate
Solution Approach 1:
The patent replaces traditional mechanical monitoring systems with a machine learning-based detection system. The machine learning model processes pump system data to generate card format data and detect operational conditions, substituting complex mechanical monitoring infrastructure with intelligent software-based detection that achieves higher operational efficiency without proportional increases in physical system complexity
Solution Approach 2:
The patent introduces card format data as an intermediary representation between raw pump system data and operational condition detection. This intermediate data structure enables the machine learning model to effectively process and analyze pump system information, bridging the gap between raw data collection and meaningful operational insights
2Reliability
If real-time data processing and machine learning analysis are implemented, then early issue detection capability is improved, but computational requirements and system complexity increase
Solution Approach 1:
The patent performs preliminary data processing to generate card format data before machine learning analysis. This pre-processing step organizes raw pump system data into a structured format that facilitates more efficient machine learning processing, enabling early issue detection while reducing the computational burden during real-time operation
Solution Approach 2:
The patent transforms raw pump system data into card format data, changing the parameter representation from raw sensor readings to structured operational characteristics. This parameter transformation enables the machine learning model to more effectively detect operational conditions while optimizing computational resource utilization
Data Source
AI summary
A method may include receiving data from a pump system at a field site; processing the data to generate card format data; detecting an operational condition of the pump system using a machine learning model and the card format data; and, responsive to the detecting, controlling operation of the pump system at the field site.