Pump Failure Prediction Using Three-Model Anomaly Detection
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Solution Overview
Problem
Electric submersible pumps in oil wells are prone to mechanical failures due to sand influx and corrosion, leading to reduced production rates and economic losses from inspection and repair efforts.
Innovation Solution
A pump failure prediction apparatus utilizing supervised and unsupervised learning models to detect abnormalities in real-time data, including a first model for pre-failure anomalies, a second model for deviations from normal operation, and a third model for initial operation range deviations, using linear discriminant analysis, principal component analysis, and Mahalanobis distance to predict failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mechanical pump systems are used in oil wells, then pumping function is provided, but mechanical failures occur due to sand influx and corrosion
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing operational data to detect abnormalities before they lead to pump failure. Multiple detection models (first detection model for pre-failure anomalies, second detection model for normal operation range deviations, third detection model for initial operation range deviations) analyze data in advance to predict potential failures, allowing maintenance to be scheduled before actual failure occurs.
Solution Approach 2:
The system implements continuous feedback by monitoring pump operational data in real-time and using this information to adjust maintenance schedules and predict failures. The feedback loop collects data from the pump, analyzes it through multiple detection models, and provides actionable insights that improve future operational reliability and prevent recurrence of similar issues.
2Reliability
If pump inspection and repair are performed after failure, then pumping function is restored, but production time is lost and economic losses occur
Solution Approach 1:
The system performs preliminary failure prediction by analyzing operational data through multiple detection models before actual failure occurs. The first detection model identifies pre-failure anomalies, while the second and third models detect deviations from normal and initial operation ranges, respectively. This allows maintenance to be scheduled in advance, avoiding unplanned downtime and production losses.
3Measurement precision
If multiple detection models are used for failure prediction, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the detection process into three specialized models, each handling a specific aspect of abnormality detection: the first detection model for pre-failure anomalies, the second detection model for normal operation range deviations, and the third detection model for initial operation range deviations. This segmentation allows each model to be optimized for its specific function, improving overall detection accuracy while maintaining manageable complexity through clear division of responsibilities.
Data Source
AI summary
Proposed is a pump failure prediction apparatus which includes at least one processor, a storage, which is communicably connected with the processor and stores a program code which operates in the processor, and a communicator, which is communicably connected with the processor, wherein the program code includes a data collection module, and an abnormality detection module which detects three abnormalities. The first abnormality appears before failure occurrence by inputting the real-time data into a first model which has performed supervised learning of history data. The second abnormality appears outside of a normal operation range of the pump by inputting the real-time data to a second, which has performed unsupervised learning of normal operation data. The third abnormality appears outside an initial normal operation range of the pump by inputting the real-time data to a third model, which has performed unsupervised learning of initial operation data.