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 Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

2014 ◽  
Vol 14 (2) ◽  
pp. 102-108 ◽  
Author(s):  
Yong Yang ◽  
Shuying Huang ◽  
Junfeng Gao ◽  
Zhongsheng Qian
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Discrete Wavelet ◽  
Evaluation Indexes ◽  
Fused Image ◽  
Focus Image

Abstract In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

scholarly journals A Multi-Valued Diagnostic Model Synthesis Based on Descrete Wavelet Transform

2015 ◽  
Vol 27 (1) ◽  
pp. 43-52 ◽  
Author(s):  
Henryk Borowczyk
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Real Life ◽  
Signal Decomposition ◽  
Diagnostic Model ◽  
Discrete Wavelet ◽  
Model Synthesis ◽  
Vibroacoustic Signal ◽  
Three Stages

Abstract The method of a multi-valued diagnostic model synthesis using discrete wavelet transform is presented. The method's algorithm consists of three stages: (1) - signal decomposition into low- and high frequency parts - approximations and details, (2) - approximations and details parameterization, (3) - multi-valued encoding parameters obtained in stage 2. The method is illustrated with vibroacoustic signal in real life experiment. The multi-valued diagnostic model is the final result.

scholarly journals Assessing the Contribution of the Oscillatory Potentials to the Genesis of the Photopic ERG with the Discrete Wavelet Transform

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Mathieu Gauvin ◽  
Allison L. Dorfman ◽  
Nataly Trang ◽  
Mercedes Gauthier ◽  
John M. Little ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Normal Subjects ◽  
Oscillatory Potentials ◽  
Discrete Wavelet ◽  
Wide Range ◽  
Frequency Components ◽  
Photopic Erg

The electroretinogram (ERG) is composed of slow (i.e., a-, b-waves) and fast (i.e., oscillatory potentials: OPs) components. OPs have been shown to be preferably affected in some diseases (such as diabetic retinopathy), while the a- and b-waves remain relatively intact. The purpose of this study was to determine the contribution of OPs to the building of the ERG and to examine whether a signal mostly composed of OPs could also exist. DWT analyses were performed on photopic ERGs (flash intensities: −2.23 to 2.64 log cd·s·m−2in 21 steps) obtained from normal subjects (n=40) and patients (n=21) affected with a retinopathy. In controls, the %OP value (i.e., OPs energy/ERG energy) is stimulus- and amplitude-independent (range: 56.6–61.6%; CV = 6.3%). In contrast, the %OPs measured from the ERGs of our patients varied significantly more (range: 35.4%–89.2%;p<0.05) depending on the pathology, some presenting with ERGs that are almost solely composed of OPs. In conclusion, patients may present with a wide range of %OP values. Findings herein also support the hypothesis that, in certain conditions, the photopic ERG can be mostly composed of high-frequency components.

scholarly journals Invisible Medical Image Watermarking using Edge Detection And Discrete Wavelet Transform Coefficients

2019 ◽  
Vol 9 (1) ◽  
pp. 5074-5080
Keyword(s):  
Wavelet Transform ◽  
Medical Imaging ◽  
Edge Detection ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Medical Image ◽  
Image Watermarking ◽  
Discrete Wavelet ◽  
Medical Image Watermarking ◽  
Imaging Application

Protection and authentication of medical images is essential for the patient’s disease identification and diagnosis. The watermark in medical imaging application needs to be invisible and it is also required to preserve the low and high frequency features of image data which makes watermarking a difficult assignment. Within this manuscript an unseen medical image watermarking approach is projected apply edge detection in the discrete wavelet transform domain. The wavelet transform is brought into play to decay the medical picture interested in multi-frequency secondary band coefficients. The edge detection applies to high frequency wavelet group in the direction of generating the boundary coefficients used as a key. The Gaussian noise pattern is utilized as watermark as well as embedded within the edge coefficients around the edges. To add the robustness scaled dilated edge coefficient is added with the edge coefficients to generate the watermarked image. Preserving the small frequency secondary band fulfills the information requirement of the medical imaging application. At the same time as adding together the watermark during high frequency sub-bands improve the watermark invisibility. To add additional robustness the dilation is applied on the edged coefficient before being embedded with sub band coefficients. presentation of the technique is experienced on the dissimilar set of medical imagery as well as evaluation of the proposed watermarking method founds it robust not in favor of the different attacks such at the same time as filtering, turning round plus resizing. Parametric study foundation going on Mean Square Error along with Signal to Noise Ratio shows that how good method performs for invisibility.

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.

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