Motor Condition Monitoring via Zero-Crossing Envelope Analysis
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Solution Overview
Problem
Existing motor condition monitoring methods, such as spectral analysis using Fast Fourier Transform (FFT), are inefficient for motors driven by power sources with limited processing power and require detailed motor design knowledge, making it difficult to implement condition monitoring across various motor types and operation points.
Innovation Solution
A method and device that sense motor signals, detect zero-crossing instants, estimate envelopes of time intervals, and use neural networks to determine fault presence and severity without complex frequency signal processing, allowing for plug-and-play operation and reduced computational and storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Fast Fourier Transform (FFT) spectral analysis is used for motor condition monitoring, then measurement precision is improved, but device complexity and processing power requirements increase
Solution Approach 1:
The patent extracts only the essential features needed for fault detection by analyzing zero-crossing time intervals and their envelopes, rather than performing complete spectral analysis. This extraction approach maintains fault detection capability while significantly reducing processing complexity and computational requirements.
Solution Approach 2:
The patent uses simple time interval measurements and envelope estimations that require minimal computational resources, replacing the computationally expensive FFT algorithm. This allows implementation on low-cost microcontrollers with limited processing power while achieving adequate fault detection precision.
2Measurement precision
If FFT spectral analysis is implemented, then measurement precision is improved, but loss of information increases due to limited memory and storage capability
Solution Approach 1:
The patent extracts only the necessary information for fault detection by measuring zero-crossing time intervals and estimating their envelopes. This selective extraction reduces the amount of data that needs to be stored and processed, minimizing memory and storage requirements while maintaining adequate measurement precision.
Solution Approach 2:
The patent performs partial spectral analysis by focusing only on time interval envelope characteristics rather than complete frequency spectrum analysis. This partial approach reduces data storage requirements while providing sufficient information for fault detection in resource-constrained environments.
3Adaptability or versatility
If spectral analysis is adjusted for different motor types, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal monitoring method that works across different motor types by analyzing fundamental time interval envelope characteristics. This approach provides multi-functionality without requiring type-specific configurations, as the envelope analysis methodology adapts automatically to different motor operating conditions while maintaining consistent implementation.
Solution Approach 2:
The patent achieves adaptability through parameter changes in the operating conditions rather than changes in the monitoring methodology. The time interval envelope analysis automatically adapts to different motor types and operating points by capturing the characteristic envelope patterns, eliminating the need for complex type-specific configurations.
4Measurement precision
If complete spectral analysis is performed, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent extracts only the essential time interval envelope information needed for fault detection, avoiding the computationally intensive complete spectral analysis. This extraction approach significantly reduces processing energy consumption while maintaining adequate fault detection precision for practical applications.
Solution Approach 2:
The patent performs partial analysis by focusing only on time interval envelope characteristics rather than complete frequency spectrum analysis. This partial approach reduces computational workload and energy consumption while providing sufficient fault detection capability for resource-constrained embedded systems.
Data Source
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AI summary
The present invention concerns a method and a device for monitoring the condition of a motor, the motor being driven by a power source that provides signals to the motor. The invention: - senses a motor signal, - detects predetermined operating conditions of the motor, and if the motor operates in predetermined conditions: - detects the instants of zero-crossing of the sensed signal, - determines the time intervals between two zero-crossing instants, - estimates plural envelopes of the determined time intervals in order to obtain an estimated pattern of envelopes, each envelope being determined over a number of determined time intervals, - determines from the operating conditions of the motor and the pattern of envelopes if the motor has a fault and/or a level of the fault.