Equipment Failure Prediction Using Time-Derivative Signatures
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Solution Overview
Problem
Existing technologies face challenges in predicting failures of equipment like electric submersible pumps (ESPs) in wellbores, as each failure mode has a distinct signature, leading to difficulties in early detection and prevention of unplanned failures.
Innovation Solution
The use of machine learning-based failure prediction systems that incorporate time derivative and gradient features of operational parameters, along with data augmentation using multiple time windows, to classify failure modes and detect anomalies in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional failure prediction methods are used, then the system is simple to implement, but it cannot effectively detect and classify different failure modes with distinct signatures
Solution Approach 1:
The patent segments the failure prediction task into multiple components: extracting operational parameters from sensor data, computing time-derivative features to capture failure signatures, using gradient calculations to identify critical changes, and classifying different failure modes separately. This segmentation enables precise detection of distinct failure signatures while managing system complexity through modular processing steps.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based failure detection systems with a machine learning-based classification system. The system uses computational algorithms to automatically learn and classify failure modes from operational data, substituting complex manual analysis and rule-based approaches with adaptive computational models that improve detection accuracy.
2Reliability
If real-time failure prediction is implemented, then unplanned failures can be prevented, but computational resources and processing time are consumed
Solution Approach 1:
The patent performs preliminary actions by pre-computing time-derivative features and gradients from operational parameters before actual failure occurs. The system continuously monitors and processes sensor data in real-time, preparing feature representations that enable rapid failure classification when anomalies are detected, thus preventing unplanned failures while managing computational load through proactive processing.
Solution Approach 2:
The patent transforms raw operational parameters into derived features by calculating time-derivatives and gradients, changing the parameter representation to highlight failure signatures. This parameter transformation enables more effective failure detection with optimized computational requirements, as the derived features capture critical failure information more efficiently than raw sensor data.
3Adaptability or versatility
If multiple operational parameters are monitored, then comprehensive failure detection is achieved, but data processing complexity increases
Solution Approach 1:
The patent implements a universal processing framework that handles multiple operational parameters (vibration, temperature, pressure, flow rate) through a single unified approach. The system applies the same time-derivative and gradient calculation methods across all parameter types, enabling comprehensive failure mode coverage while simplifying data processing through consistent, multi-functional processing routines rather than separate specialized handlers for each parameter.
Data Source
AI summary
A method comprises receiving a time series of data values for a time window of each operational parameter of a number of operational parameters of equipment; calculating a time derivative feature that comprises a change of the data values of a first operational parameter of the number of operational parameters over the time window; and classifying, using a machine learning model and based on the time derivative feature, an operational mode of the equipment into different failure categories.