Auto-compensating Sensor System for Electrical Machine Condition Monitoring
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Solution Overview
Problem
Existing monitoring systems for electrical machines are prone to sensor drift over time, leading to inaccurate health assessments due to unstable readings, which limits their usefulness and can result in stator bar winding failures and efficiency decreases.
Innovation Solution
An auto-compensating system that combines data from first and second sensor elements embedded in or on a stator core, using a control subsystem to generate signals indicative of changes in characteristics and refine data for accurate health monitoring, thereby compensating for sensor drift.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are used for monitoring stator bar winding movement, then monitoring accuracy is improved, but sensor drift over time causes measurement precision to deteriorate
Solution Approach 1:
The system uses a second sensor to monitor operating parameters (temperature, load, vibration) and feeds this information back to the control subsystem. The control subsystem automatically adjusts the first sensor's readings based on these operating conditions, compensating for drift without manual intervention and maintaining long-term measurement accuracy.
Solution Approach 2:
The system changes the monitoring approach by not just measuring the physical quantity of interest (winding movement) but also measuring operating parameters that affect sensor drift. By monitoring temperature, load, and vibration simultaneously, the system can correlate these parameter changes with sensor drift and apply appropriate compensation algorithms.
2Loss of time
If re-wedging or rewinding is performed infrequently, then maintenance cost is reduced, but sensor drift limits the useful monitoring period
Solution Approach 1:
The monitoring system performs self-calibration and self-compensation by using the second sensor's operating parameter data to automatically adjust the first sensor's readings. This self-service capability eliminates the need for frequent manual recalibration or maintenance interventions, extending the useful monitoring period between maintenance events.
Solution Approach 2:
The system performs preliminary compensation for sensor drift by continuously monitoring operating parameters and pre-adjusting measurements before drift becomes significant. This proactive approach maintains measurement accuracy throughout the extended period between maintenance events.
3Device complexity
If a single sensor is used for monitoring, then device complexity is reduced, but monitoring reliability deteriorates due to sensor drift
Solution Approach 1:
The second sensor serves multiple functions: it monitors operating parameters (temperature, load, vibration) that affect sensor drift, and its data is used by the control subsystem to compensate for drift in the first sensor. This multi-functional approach justifies the additional sensor by providing both direct monitoring and drift compensation capabilities.
Data Source
AI summary
A condition monitoring method for an electrical machine is provided. The method includes providing at least one first sensor element embedded in or disposed on at least one substrate element located in a stator core for obtaining a first set of data. The method also includes providing at least one second sensor element for obtaining a second set of data from the electrical machine. Further, the method includes generating signals indicative of changes in characteristics of the first sensor element based on the second set of data. Finally, the method includes refining the first set of data by combining the first set of data with the generated signals.