BLDC Motor Winding Fault Detection Using Sequence Current Analysis
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Solution Overview
Problem
Existing methods fail to effectively detect winding faults in electric motors, particularly in brushless direct current (BLDC) motors, which can lead to catastrophic failures such as electrical short circuits and fires due to high temperatures.
Innovation Solution
A method and system that utilize a motor winding fault diagnosis system to analyze current signals using zero cross detection, fast Fourier transform (FFT) analysis, and Hilbert analysis to detect winding faults by determining negative and zero sequence components, and assess their severity through real mean squared (RMS) value standard deviation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing monitoring methods are used, then general fault detection is possible, but winding faults cannot be effectively detected leading to catastrophic failures
Solution Approach 1:
The patent segments the current signal analysis into distinct sequence components (positive, negative, and zero sequence) using Fourier-based transformation. This segmentation allows specific fault types to be isolated and identified by examining individual sequence components, thereby enabling effective winding fault detection without requiring overly complex diagnostic systems.
Solution Approach 2:
The patent establishes baseline sequence component values during normal motor operation before faults occur. These pre-established baselines are stored and later compared against real-time measurements, enabling early detection of winding faults through deviation from the baseline. This preliminary action allows the system to detect faults in their incipient stages before catastrophic failure.
2Measurement precision
If complex analysis methods are applied, then detection accuracy improves, but processing time and computational load increase
Solution Approach 1:
The patent extracts only the relevant sequence components (negative and zero sequence) from the complete three-phase current signal using Fourier transformation. By focusing only on these specific components that indicate winding faults, rather than analyzing the entire signal spectrum, the system achieves high detection accuracy while minimizing computational load and processing time.
Solution Approach 2:
The patent applies partial action by selectively analyzing only the necessary sequence components for winding fault detection rather than performing a complete spectral analysis of all signal frequencies. This selective approach provides sufficient detection accuracy for the specific application while reducing overall processing requirements and time.
3Reliability
If continuous monitoring is implemented, then early fault detection is achieved, but energy consumption and system complexity increase
Solution Approach 1:
The patent utilizes the motor's existing current signals, which are already being drawn during normal operation, for fault detection purposes. The same current that drives the motor also provides the diagnostic information needed, eliminating the need for separate sensing systems and reducing additional energy consumption. The system essentially uses the motor's own operational signals for self-diagnosis.
Solution Approach 2:
The monitoring system performs multiple functions using the same measurement infrastructure: it simultaneously monitors motor performance and detects winding faults by analyzing sequence components of the current signal. This multi-functionality allows early fault detection without requiring dedicated separate systems, thereby minimizing additional energy consumption and system complexity.
Data Source
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AI summary
The present disclosure relates to systems and methods for detecting a winding fault and the winding fault severity in a brushless direct current motor before motor failure. Methods for detecting a winding fault include time domain based sequence component analysis, fast Fourier transform analysis, or Hilbert analysis. Methods for detecting the severity of a winding fault include analysis of the standard deviation of real mean squared values determined using motor currents.