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

2015 ◽  
Author(s):  
Noureddine Benouzza ◽  
Ahmed Hamida Boudinar ◽  
Azeddine Bendiabdellah ◽  
Mohammed Khodja
Keyword(s):  
Fault Diagnosis ◽  
Harmonic Frequency ◽  
Broken Rotor Bars ◽  
Stator Current ◽  
Current Spectrum ◽  
Frequency Detection

Fault Signature Analysis Based on Transient Current for Induction Motors

2010 ◽  
Vol 44-47 ◽  
pp. 1807-1811
Author(s):  
Feng Lv ◽  
Hao Sun ◽  
Wen Xia Du ◽  
Shue Li
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Scale Transformation ◽  
Stationary Condition ◽  
Analysis Method ◽  
Broken Rotor Bars ◽  
Multi Scale ◽  
Stator Current ◽  
Time Varying Frequency ◽  
Basic Characteristics

The characteristics of broken rotor bars in induction motors are reflected in the abnormal harmonic of the stator current. At present, fast Fourier transform( ) and time-varying frequency spectrum analysis method are used in such fault diagnosis, but non-stationary motors operation can bring a certain difficulties to the monitoring and diagnosis. This paper studies the basic characteristics of wavelet transform, adopting the wavelet analysis technologies of signal processing and selecting mother wavelet, the paper makes the multi-scale transformation to the motor starting current, excavates the harmonic informations on non-stationary condition, realizes fault diagnosis of motor broken rotor bars effectively, The consistent diagnostic results prove the effectiveness of the method.

scholarly journals Stator and Rotor Faults Diagnosis of Squirrel Cage Motor Based on Fundamental Component Extraction Method

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Guoqing An ◽  
Hongru Li
Keyword(s):  
Short Circuit ◽  
Broken Rotor Bars ◽  
Fundamental Component ◽  
Stator Current ◽  
Current Spectrum ◽  
Squirrel Cage ◽  
Voltage Frequency ◽  
Component Extraction ◽  
Spectrum Experiment ◽  
Stator Fault

Nowadays, stator current analysis used for detecting the incipient fault in squirrel cage motor has received much attention. However, in the case of interturn short circuit in stator, the traditional symmetrical component method has lost the precondition due to the harmonics and noise; the negative sequence component (NSC) is hard to be obtained accurately. For broken rotor bars, the new added fault feature blanked by fundamental component is also difficult to be discriminated in the current spectrum. To solve the above problems, a fundamental component extraction (FCE) method is proposed in this paper. On one hand, via the antisynchronous speed coordinate (ASC) transformation, NSC of extracted signals is transformed into the DC value. The amplitude of synthetic vector of NSC is used to evaluate the severity of stator fault. On the other hand, the extracted fundamental component can be filtered out to make the rotor fault feature emerge from the stator current spectrum. Experiment results indicate that this method is feasible and effective in both interturn short circuit and broken rotor bars fault diagnosis. Furthermore, only stator currents and voltage frequency are needed to be recorded, and this method is easy to implement.

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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