Broken-Rotor Bar Detection in Inverter-Fed Induction Motors
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Solution Overview
Problem
Existing methods for broken-bar fault detection in inverter-fed induction motors are ineffective due to varying speed and load conditions, making it difficult to extract fault signatures using conventional motor current signature analysis (MCSA).
Innovation Solution
A system and method utilizing graph-based signal processing techniques to analyze stator current under varying speed and load conditions, employing sparsity and smoothness constraints to detect broken-bar faults by transforming stator current into a complex space vector, performing Short-Time Fourier Transform (STFT) and graph-based optimization to identify sparse and smooth frequency components in the spectrogram matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MCSA methods are used for broken-bar fault detection, then detection is effective under stationary conditions, but detection performance deteriorates under varying speed and load conditions
Solution Approach 1:
The patent applies dynamics by transforming the stator current signal into a complex space vector and then into a time-frequency domain representation using STFT. This dynamic transformation allows the fault detection system to adapt to varying speed and load conditions by capturing the time-varying characteristics of the current signal, rather than relying on stationary frequency analysis.
Solution Approach 2:
The patent changes the analysis parameters by imposing sparsity and smoothness constraints on the fault signature extraction process. These parameter changes enable the system to distinguish fault components from normal operational variations under varying speed and load conditions, improving detection accuracy in dynamic environments.
2Ease of manufacture
If short measurement periods are used for MCSA, then implementation is simple, but detection reliability decreases under variable operating conditions
Solution Approach 1:
The patent applies preliminary action by pre-defining sparsity and smoothness constraints before the actual fault detection process. These pre-established constraints guide the fault signature extraction, enabling reliable detection under variable conditions without requiring complex real-time adjustments or extended measurement periods.
3Use of energy by moving object
If inverter-fed operation is used for efficiency, then energy efficiency improves, but fault detection becomes difficult due to varying speed and load
Solution Approach 1:
The patent substitutes traditional mechanical frequency analysis with a time-frequency domain approach using complex space vector transformation and STFT. This substitution enables effective fault detection in inverter-fed motors by capturing the non-stationary characteristics of the current signal that arise from varying speed and load operations.
Data Source
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AI summary
A computer-implemented method is provided for detecting broken bar faults of an induction motor during operations. The method includes steps of acquiring, in a time domain, a signal of a stator current powering the induction motor with a fundamental frequency via an interface, wherein the induction motor is under a varying speed operation; converting the stator current to a complex space vector; transforming the complex vector to a transformed stator current by referencing to synchronous reference frame; performing Short time Fourier Transform (STFT) on the transformed stator current to get spectrogram matrix; removing a DC component from the spectrogram matrix; determining, in a frequency domain, sparse and smooth frequency components in the spectrogram matrix, wherein the determining includes a graph-based method by imposing a smoothness constraint and a sparsity constraint on the frequency component; and detecting a fault in the induction motor if the frequency component includes a continuously changing and sparse fault frequency component in the spectrogram matrix.