Rotor Field Winding Short Detection via Magnetic Flux Analysis

The patent replaces manual analysis methods with an automated computer-based system that uses software algorithms to process magnetic flux data. The system automatically collects data from flux probes, processes it through computational algorithms, and identifies shorted turns without human intervention, thereby increasing automation level while managing system complexity through standardized computational processes.

The system performs self-diagnosis by automatically monitoring its own performance and detecting faults in the field winding. The computer-based system continuously self-monitors magnetic flux characteristics and autonomously identifies abnormalities indicating shorted turns, eliminating the need for external manual inspection and enabling continuous self-service monitoring.

The patent segments the field winding into discrete coil turns and analyzes magnetic flux data for each segment independently. By dividing the continuous flux signal into discrete temporal and spatial segments corresponding to individual coil turns, the system can precisely identify which specific turns are shorted without requiring analysis of the entire winding at once, thereby improving detection precision while managing processing time through segmented processing.

The system performs preliminary data processing and feature extraction before final analysis. Magnetic flux data is pre-processed to identify characteristic patterns and anomalies, and turn identification algorithms are prepared in advance. This preliminary action reduces the computational burden during actual fault detection, enabling comprehensive analysis with reduced processing time.

The patent implements periodic sampling of magnetic flux data at optimized intervals rather than continuous uninterrupted monitoring. The system samples flux data at periods determined by rotor rotation speed and operational conditions, processing data in discrete time windows. This periodic action maintains detection reliability by capturing fault conditions when they occur while significantly reducing energy consumption and data processing load compared to continuous monitoring.

The system dynamically adjusts monitoring parameters based on operational conditions. The sampling rate, analysis thresholds, and processing intensity are modified according to rotor speed, load conditions, and detected anomalies. During normal operation, the system uses lower processing intensity and optimized sampling intervals, while increasing monitoring density only when necessary, thereby maintaining reliability while minimizing energy consumption and data processing load.