Informatics Publishing Limited

P. Ganesan ¹, V. Rajini ², B. S. Sathish ¹, V. Kalist ¹, S. K. Khamar Basha ¹

Affiliations
1 Sathyabama University, Chennai, IN
2 Department of Electrical and Electronics, SSN College of Engineering, Chennai, IN

Abstract

Segmentation is one of the most important processes in the satellite image processing to retrieve most useful information from the satellite images. This paper proposed an effective fuzzy based method of segmentation of satellite images in YCbCr color space. The YCbCr Color space represents color as intensity and exploits the characteristics of human eye. Our eye is more sensitive to intensity than hue. The intensity component can be stored with greater accuracy as the amount of information to be minimized. The JPEG file format mostly uses this color space to discard the unwanted or unimportant information. In the proposed approach, the satellite image in RGB color space is transform into YCbCr color space and then the transformed satellite image is split into three different components (channels or images) based on luminance and chrominance. Subsequently Fuzzy based segmentation is applied separately for all three components for efficient segmentation. Finally the threshold is applied to extract the foreground (object) from the background. The experimental result reveals that the proposed fuzzy based segmentation method is efficient and accurate for extracting the necessary information from the satellite images.

Keywords

Color Space, FCM, Image Segmentation, RGB, Satellite Image, Threshold, YCbCr.

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W. Abitha Memala ¹, V. Rajini ¹

Affiliations
1 EEE, SSNCE, Chennai - 603110, Tamil Nadu, IN

Abstract

The objective of our work is to identify the inter-turn incipient short fault that occurs in the induction motor at no load condition. The method used in fault identification is Information Theoretic Criteria, which uses Frequency Signal Dimension Order (FSDO) estimator and fault decision module. The FSDO estimator estimates the number of frequencies in stator current signatures using Minimum Description Length (MDL) criterion and Akaike Information Criteria (AIC). Fault decision module uses the number of frequencies as fault index in detecting the fault and identifying the fault severity. The proposed method is able to identify the fault from the data buried in noise. MDL yields consistent estimate, the fault index obtained using MDL criterion is considered for diagnosing the faults. The CDF plot of MDL and AIC helps in proving the fault severity results obtained from fault index value. With very lesser values of sampled data, the proposed method is able to distinguish between healthy and faulty conditions. This novel approach diagnoses the inter-turn incipient fault with very simple calculation and it needs only very few number of measured data for estimating the number of fault frequencies associated with the faulty conditions.

Keywords

Akaike Information Criteria (AIC), Cumulative Density Function (CDF), Information Theoretic Criteria (ITC), Inter-turn incipient short circuit fault, Minimum Description Length (MDL) Criteria and Stator Faults

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W. Abitha Memala ¹, V. Rajini ¹

Affiliations
1 EEE, SSNCE, Old Mahabalipuram Road, Kalavakkam, Chennai - 603110, Tamil Nadu, IN

Abstract

Objectives: The parametric method is proposed to identify the inter-turn incipient short circuit fault of induction motor. Methods/Statistical Analysis: The Parametric methods are the high resolution methods, which are used to detect the frequencies with low signal to noise (SNR) ratio using smaller number of data samples. The subspace parametric methods use the Multi-signal Classification (MUSIC) algorithm to identify the inter-turn incipient fault and its severity. The pseudo spectrum from the MUSIC algorithm estimates the frequencies related to the faulty condition of the induction motor. Findings: The frequencies which are related inter-turn short circuit fault are obtained from the pseudo spectrum and are compared with the estimated frequencies and thus it proves the estimated frequencies. Application/Improvements: The proposed algorithm is used to estimate the harmonic components from the pseudo spectrum which helps to differentiate the healthy and faulty conditions of the motors. Also the severity of the fault is identified from the pseudo spectrum.

Keywords

Frequency Signal Dimension Order, Inter-Turn Incipient Short Circuit Fault, Multi Signal Classification (MUSIC) Algorithm, Parametric Method, Subspace Method.

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