Electric Motor Signal Spectrum Monitoring for Real-Time Fault Detection
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Solution Overview
Problem
Existing methods for monitoring electric motor operation status require high memory and processing power, and cannot provide real-time analysis.
Innovation Solution
A method utilizing discrete Fourier Transforms (DFT) to analyze drive application signals in real-time, reducing the need for high memory and processing power by performing spectrum analysis on different frequencies simultaneously and storing only computation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signals are stored for later post-processing and data analysis, then comprehensive data analysis can be performed, but high memory and high processing power processor are required and real-time results cannot be obtained
Solution Approach 1:
The patent extracts and processes only the essential spectral features of motor signals in real-time, rather than storing and analyzing all raw signal data. By using Fourier transforms to convert time-domain signals into frequency-domain spectra, the system identifies critical fault indicators (such as bearing defects, rotor issues) directly from spectral components, eliminating the need for high-memory storage and post-processing while maintaining monitoring accuracy
Solution Approach 2:
The system performs preliminary spectral analysis continuously in the background, maintaining ready-to-use spectral representations of motor signals. This preliminary processing allows the monitoring system to immediately detect and diagnose faults without requiring computationally intensive post-processing, thereby reducing real-time processing requirements while preserving comprehensive analysis capabilities
2Measurement precision
If comprehensive signal data is stored for later analysis, then thorough fault diagnosis can be achieved, but detection time is increased and real-time monitoring is not possible
Solution Approach 1:
The patent replaces the mechanical approach of storing and sequentially analyzing large volumes of raw signal data with a computational approach using Fourier transforms. This substitution converts time-domain signals into frequency-domain spectra, allowing immediate identification of fault characteristics through spectral peak detection. The transformation enables real-time fault diagnosis with the same diagnostic accuracy as comprehensive post-processing would provide
Solution Approach 2:
The system changes the representation parameters of motor signals from time-domain waveforms to frequency-domain spectra. By transforming the signal parameters through Fourier analysis, the system extracts fault-related frequency components that directly indicate specific motor conditions. This parameter transformation enables rapid, accurate fault detection in real-time without requiring extensive data storage or lengthy analysis periods
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time monitoring of electric motor operation status with reduced computational resources, allowing for faster detection of abnormalities and faults without the need for extensive data storage.
Implementation Method 1
The step of analyzing the observed frequency to obtain a spectrum thereof is carried out by means of Fourier Transforms, especially by discrete Fourier Transforms (DFT)
Data Source
AI summary
A method for monitoring operation status of an electric motor in real time, including: reading a drive application signal of the electric motor; selecting a frequency to be observed; analyzing the frequency to be observed to obtain a spectrum thereof; analyzing the spectrum; and detecting the operation status of the electric motor on the basis of the spectrum analysis.