Slot harmonic frequency detection as a technique to improve stator current spectrum approach for eccentricity fault diagnosis

2018 ◽  
Author(s):  
Noureddine Benouzza ◽  
Ahmed Hamida Boudinar ◽  
Azzeddine Bendiabdellah
Keyword(s):  
Fault Diagnosis ◽  
Harmonic Frequency ◽  
Stator Current ◽  
Current Spectrum ◽  
Frequency Detection

scholarly journals Cost-Effective Reduced Envelope of the Stator Current via Synchronous Sampling for the Diagnosis of Rotor Asymmetries in Induction Machines Working at Very Low Slip

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3471 ◽  
Author(s):  
Jordi Burriel-Valencia ◽  
Ruben Puche-Panadero ◽  
Javier Martinez-Roman ◽  
Angel Sapena-Bano ◽  
Manuel Pineda-Sanchez
Keyword(s):  
Fault Diagnosis ◽  
Low Cost ◽  
Computing Time ◽  
Low Frequency ◽  
Cost Effective ◽  
Induction Machines ◽  
Minimal Amount ◽  
Stator Current ◽  
Current Spectrum ◽  
On Line

Fault diagnosis of rotor asymmetries of IM using the stator current relies on the detection of the characteristic signatures of the fault harmonics in the current spectrum. In some scenarios, such as large induction machines running at a very low slip, or unloaded machines tested offline, this technique may fail. In these scenarios, the fault harmonics are very close to the frequency of the fundamental component, and have a low amplitude, so that they may remain undetected, buried under the fundamental’s leakage, until the damage is severe. To avoid false positives, a proven approach is to search for the fault harmonics in the current envelope, instead of the current itself, because in this case the spectrum is free from the leakage of the fundamental. Besides, the fault harmonics appear at a very low frequency. Nevertheless, building the current spectrum is costly in terms of computing complexity, as in the case of the Hilbert transform, or hardware resources, as in the need for simultaneously sampling three stator currents in the case of the EPVA. In this work, a novel method is proposed to avoid this problem. It is based on sampling a phase current just twice per current cycle, with a fixed delay with respect to its zero crossings. It is shown that the spectrum of this reduced set of current samples contains the same fault harmonics as the spectrum of the full-length current envelope, despite using a minimal amount of computing resources. The proposed approach is cost-effective, because the computational requirements for building the current envelope are reduced to less than 1 % of those required by other conventional methods, in terms of storage and computing time. In this way, it can be implemented with low-cost embedded devices for on-line fault diagnosis. The proposed approach is introduced theoretically and validated experimentally, using a commercial induction motor with a broken bar under different load and supply conditions. Besides, the proposed approach has been implemented on a low-cost embedded device, which can be accessed on-line for remote fault diagnosis.

Motor Broken-Bar Fault Diagnosis Based on Park Vector and Wavelet Neural Network

2011 ◽  
Vol 382 ◽  
pp. 163-166
Author(s):  
Qing Xin Zhang ◽  
Jin Li ◽  
Hai Bin Li ◽  
Chong Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fundamental Frequency ◽  
Wavelet Packet ◽  
Frequency Component ◽  
Wavelet Neural Network ◽  
Simulation Software ◽  
Monitoring Methods ◽  
Stator Current ◽  
Fundamental Frequency Component

In the technology of motor fault diagnosis, current monitoring methods have become a new trend in motor fault diagnosis. This paper presents a motor fault diagnosis method based on Park vector and wavelet neural network. This method uses the stator current as the object of study. Firstly, it uses Park vector to deal with the stator current and filter out fundamental frequency component, thus the characteristics component of motor broken-bar will be separated from fundamental frequency component; Secondly, it uses five layers wavelet packet decomposition to pick up fault characteristic signal; Finally, we distinguish the fault by BP neural network, and use the simulation software of MATLAB to realize it. The test results show that: This method can detect the existence of motor broken-bar fault, and has a good value in engineering.

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