ESP Failure Prediction Using Multi-Window Machine Learning
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Solution Overview
Problem
Predicting failures in artificial lift equipment like ESPs is challenging due to each failure mode having a distinct signature, leading to difficulties in timely detection and prevention of premature failures that cause significant production disruptions.
Innovation Solution
A machine learning-based approach using time derivative and gradient features is employed to capture both slow and fast failure behaviors, incorporating data cleaning, feature encoding, and multi-window data augmentation to classify failure modes, enabling real-time monitoring and predictive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is captured at a single sampling rate, then the system complexity is low, but the system cannot detect both slow and fast changing failure behaviors
Solution Approach 1:
The patent segments the data capture process into multiple time windows with different sampling rates. Fast-changing parameters are captured at high sampling rates while slow-changing parameters use lower sampling rates. This segmentation allows the system to detect both slow and fast failure behaviors without uniformly high complexity across all parameters.
Solution Approach 2:
The patent implements dynamic sampling rates that adapt to the detected behavior patterns. When rapid changes are detected in operational parameters, the sampling rate increases automatically. When parameters stabilize, the sampling rate decreases. This dynamic adjustment optimizes detection capability while managing system complexity.
2Measurement precision
If multiple sampling rates are used to detect different failure behaviors, then detection accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent extracts only the essential features from multi-rate data streams for analysis. Instead of processing all raw data at all sampling rates, it identifies and extracts key failure indicators from each time window, reducing the computational burden while maintaining detection accuracy.
Solution Approach 2:
The patent merges information from multiple time windows with different sampling rates into a unified failure prediction model. By combining the insights from fast-changing and slow-changing parameters through the machine learning model, it achieves comprehensive detection accuracy without managing separate complex processing systems for each sampling rate.
3Reliability
If comprehensive feature extraction is performed to capture all failure modes, then prediction accuracy improves, but computational time increases
Solution Approach 1:
The patent performs preliminary feature extraction and data preprocessing in the training phase, preparing statistical features and patterns in advance. During real-time operation, the system only needs to evaluate pre-identified failure indicators against current data, significantly reducing computational time while maintaining high prediction accuracy.
Solution Approach 2:
The patent transforms raw operational parameters into standardized statistical features (mean, standard deviation, skewness, kurtosis) that capture failure patterns efficiently. This parameter transformation compresses complex temporal patterns into concise feature representations that are faster to process while retaining predictive power.
Data Source
AI summary
A method comprises sampling, at a first sampling rate for a first time window, data values of at least one operational parameter of equipment. The method comprises sampling, at a second sampling rate for a second time window, the data values of the at least one operational parameter, wherein the second sampling rate is different from the first sampling rate. The method comprises classifying, using a machine learning model and the data values in the first time window and the second time window, an operational mode of the equipment into different failure categories.