Electric Actuator Health Monitoring via Torque Curve Analysis
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Solution Overview
Problem
Existing process control systems for electric actuators in motor-operated valves typically analyze torque curve data only after the actuator has become unhealthy, leading to delayed detection of failures and potential operational disruptions.
Innovation Solution
A method and apparatus that utilize a controller connected via a network to obtain torque curve data from electric actuators, calculate the area under torque curves, compare it to a reference curve, and generate real-time or near-real-time health status notifications based on variance thresholds, enabling early detection of impending failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If torque curve data analysis is performed only after actuator failure, then diagnostic simplicity is maintained, but detection timing is delayed
Solution Approach 1:
The system performs preliminary analysis of torque curve data to establish baseline characteristics before failure occurs. By comparing current torque curves against historical data and identifying trends, the system detects early signs of degradation, enabling proactive maintenance before actual failure happens.
Solution Approach 2:
The system continuously monitors torque curve data and provides feedback about actuator health status. By analyzing variations in torque characteristics over time and comparing them against thresholds, the system generates alerts when degradation patterns are detected, creating a closed-loop monitoring system that improves detection timing.
2Reliability
If real-time torque trend monitoring is implemented, then failure detection is advanced, but computational requirements increase
Solution Approach 1:
The system extracts only the critical features from torque curve data for analysis, such as area under the curve, peak torque values, and specific torque characteristics. By focusing computational resources on these key parameters rather than analyzing the entire torque curve in detail, the system reduces computational energy consumption while maintaining reliable failure detection.
Solution Approach 2:
The system performs partial analysis of torque data by monitoring only the most indicative parameters for failure detection. Rather than conducting exhaustive analysis of all torque curve characteristics, the system selectively monitors specific features that provide the highest predictive value, reducing computational overhead while maintaining reliability.
3Measurement precision
If comprehensive torque data collection is performed, then diagnostic accuracy is improved, but data processing time increases
Solution Approach 1:
The system pre-processes torque data during collection by organizing it into structured formats and calculating intermediate metrics such as running averages and deviations. This preliminary organization reduces the computational burden during actual diagnostic analysis, maintaining high diagnostic accuracy while reducing processing time when failures are detected.
Data Source
AI summary
An example apparatus includes a controller operatively coupled to an electric actuator via a network. The controller is configured to obtain torque curve data over the network from the electric actuator, the torque curve data being associated with actuation of an electric motor of the electric actuator, the actuation to cause a flow control member of a motor-operated valve to move, the flow control member being mechanically coupled to the electric motor and being movable between an open position and a closed position. The controller is configured to determine an area under a torque curve based on the torque curve data, determine a variance between the area under the torque curve and an area under a reference curve, and generate a first control signal indicating that the electric actuator is healthy when it is determined that the variance does not exceed the variance threshold.