An improved framework of region segmentation for diagnosing thermal condition of electrical installation based on infrared image analysis / Mohd Shawal Jadin
Keadaan yang tidak normal bagi peralatan elektrik akan berlaku apabila suhunya melebihi had yang dibenarkan, yang boleh mengakibatkan kegagalan peralatan tersebut. Oleh itu, pencegahan awal amat penting untuk mengelakkan perkara ini berlaku disamping meningkatkan kebolehpercayaan peralatan tersebut. Kajian ini mencadangkan satu teknik baharu bagi segmentasi kawasan imej dan kaedah untuk mendiagnosis keadaan haba bagi peralatan elektrik dengan mengambilkira analisa imej inframerah secara kualitatif dan kuantitatif. Memandangkan kebanyakan pemasangan elektrik kebiasaannya disusun secara tetap dengan struktur yang berulang-ulang, satu kaedah baharu dicadangkan bagi mengesan semua struktur peranti elektrik yang serupa dalam satu imej inframerah. Kaedah ini menggunakan gabungan dua algoritma pengesan titik utama iaitu algoritma transformasi ciri-ciri invarian skala (SIFT) dan kawasan ekstrem yang stabil (MSER) bagi meningkatkan bilangan pengesanan titik utama. Satu kaedah baharu untuk memadan dan menterjemahkan kluster telah dicadangkan dengan memperkenalkan prosedur pengundian bagi menentukan padanan kluster. Pengesanan rantau dicapai dengan menggunakan kaedah grid di mana ia membahagikan kelompok-kelompok berulang sebelum keseluruhan objek yang disasarkan itu disegmentasi dengan sempurna. Untuk menilai keadaan pemasangan peralatan elektrik, keberkesanan menggunakan tiga jenis ciri input yang berbeza telah diselidiki. Pendekatan model ‘wrapper’ digunakan untuk memilih ciri yang sesuai di mana perseptron berbilang lapisan (MLP) rangkaian neural tiruan dan mesin vektor sokongan (SVM) digunakan untuk menilai setiap set gabungan ciri. Berdasarkan hasil kajian terhadap kaedah segmentasi yang dicadangkan, kira-kira 94.27% dari rantau telah dikesan dengan betul dengan purata nilai kawasan di bawah lengkung (AUC) sebanyak 0.79 telah dicapai. Semasa menentukan keadaan terma, didapati bahawa gabungan ciri input Tdelta, Tskew, Tkurt, Tσ dan dB menghasilkan ketepatan terbaik bagi mengesan kerosakan haba yang diklasifikasikan oleh SVM menggunakan fungsi kernel asas jejarian. Kadar prestasi tertinggi dicapai pada 99.46% dan 97.78% berdasarkan ketepatan dan nilai f-score.
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The abnormality of electrical equipment will occur when its internal temperature reached beyond its limits, which can lead to subsequent failure of the equipment. Therefore, early prevention is required in order to avoid this fault while maintaining the reliability of the equipment. This research proposes a new framework of region segmentation and thermal fault detection method for diagnosing the thermal condition of electrical installation by considering both qualitative and quantitative infrared image analysis. Since most of the electrical installations are normally fixed repetitively, a new region detection method is proposed that is able to detect all identical structure of electrical devices within an infrared image. The method employs the combination of the scale invariant feature transform (SIFT) and maximally stable extremal regions (MSER) keypoint detectors for improving the number of keypoint detection. A method for matching and translating clusters is presented by introducing a voting procedure for finding a group of matched clusters. The region detection is achieved by employing a grid approach to divide the repeated cluster before properly segmenting the target region. For evaluating the condition of electrical installation, the effectiveness of thirteen types of input features is investigated. A wrapper model approach is utilized for selecting feature where the multilayer perceptron (MLP) artificial neural network and the support vector machine (SVM) are used to evaluate each of the possible combinations of the feature set. Based on experimental results on the proposed segmentation method, about 94.27 % of the regions were correctly detected with the average area under curve (AUC) value of 0.79 was achieved. Meanwhile, for assessing the thermal condition, it was found that the integration of Tdelta, Tskew, Tkurt, Tσ and dB features yield the best result when classified by SVM using radial basis kernel function. The highest classification rates are achieved at 99.46% and 97.78% of the accuracy and f-score value, respectively.
An improved framework of region segmentation for diagnosing thermal condition of electrical installation based on infrared image analysis / Mohd Shawal Jadin
An improved framework of region segmentation for diagnosing thermal condition of electrical installation based on infrared image analysis_Mohd Shawal Jadin_E3_2018_MYMY