Wavelet Based Induction Motor Fault Diagnosis Using Zero Sequence Current

2017 ◽  
Vol 14 (1) ◽  
pp. 411-420
Author(s):  
W Abitha Memala ◽  
V Rajini
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Induction Motor ◽  
Load Condition ◽  
Discrete Wavelet ◽  
Sequence Components ◽  
Zero Sequence ◽  
Stationary Reference Frame ◽  
Parseval’S Theorem ◽  
Stator Fault

Induction motor stator fault is diagnosed by applying Discrete Wavelet transform on zero sequence components. The single phasing stator fault is created and diagnosed in the induction motor model developed in stationary reference frame, under varying load conditions. The stator inter-turn incipient fault is created and diagnosed in the induction motor experimental setup as well under no load condition. The qdo components are calculated from Park’s equations. The faults can be diagnosed from wavelet transform of the zero sequence current components. PSD is used for diagnosing the fault and the statistical value is used for verifying the result. The energy is calculated using Parseval’s theorem. The energy and the statistical data calculated from the wavelet coefficients of zero sequence current components are used as fault indicators. The energy value is able to reveal the fault severity in the induction motor stator winding. Power spectral Density along with Discrete Wavelet Transform plays very important role in diagnosing the fault.

Pendeteksian Misalignment Menggunakan Multi Level Transformasi Wavelet Haar dan Coiflet pada Motor Induksi

2020 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
P.P.S Saputra
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Induction Motor ◽  
High Frequency ◽  
High Efficiency ◽  
Vibration Signal ◽  
Normal Operation ◽  
Discrete Wavelet ◽  
High Frequency Signal ◽  
Motor Operation

Currently induction motors are widely used in industry due to strong construction, high efficiency, and cheap maintenance. Machine maintenance is needed to prolong the life of the induction motor. As studied, bearing faults may account for 42% -50% of all motor failures. In general it is due to manufacturing faults, lack of lubrication, and installation errors. Misalignment of motor is one of the installation errors. This paper is concerned to simulation of discrete wavelet transform for identifying misalignment in induction motor. Modelling of motor operation is introduced in this paper as normal operation and two variations of misalignment. For this task, haar and coiflet discrete wavelet transform in first level until fifth level is used to extract vibration signal of motor into high frequency of signal. Then, energy signal and other signal extraction gotten from high frequency signal is evaluated to analysis condition of motor. The results show that haar discrete wavelet transform at thirth level can identify normal motor  and misalignment motor conditions well

scholarly journals Application of Extension Neural Network with Discrete Wavelet Transform and Parseval’s Theorem for Power Quality Analysis

2019 ◽  
Vol 9 (11) ◽  
pp. 2228 ◽  
Author(s):  
Shiue-Der Lu ◽  
Hong-Wei Sian ◽  
Meng-Hui Wang ◽  
Rui-Min Liao
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Power Quality ◽  
Discrete Wavelet ◽  
Correlation Degree ◽  
Power Quality Disturbance ◽  
Propagation Network ◽  
Parseval’S Theorem

The development of renewable energy and the increase of intermittent fluctuating loads have affected the power quality of power systems, and in the long run, damage the power equipment. In order to effectively analyze the quality of power signals, this paper proposes a method of signal feature capture and fault identification, as based on the extension neural network (ENN) algorithm combined with discrete wavelet transform (DWT) and Parseval’s theorem. First, the original power quality disturbance (PQD) transient signal was subjected to DWT, and its spectrum energy was calculated for each order of wavelet coefficients through Parseval’s theorem, in order to effectively intercept the eigenvalues of the original signal. Based on the features, the extension neural algorithm was used to establish a matter-element model of power quality disturbance identification. In addition, the correlation degree between the identification data and disturbance types was calculated to accurately identify the types of power failure. To verify the accuracy of the proposed method, five common power quality disturbances were analyzed, including voltage sag, voltage swell, power interruption, voltage flicker, and power harmonics. The results were then compared with those obtained from the back-propagation network (BPN), probabilistic neural network (PNN), extension method and a learning vector quantization network (LVQ). The results showed that the proposed method has shorter computation time (0.06 s), as well as higher identification accuracy at 99.62%, which is higher than the accuracy rates of the other four types.

Sign in / Sign up

Export Citation Format

Share Document