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Abstrak :
ABSTRAK
Teknik untuk mengetahui RFL remaining fatigue life memiliki beberapa cara antara lain dengan teknik thermography. Dalam studi ini telah dilakukan completely rotating bending fatigue testing sesuai dengan standart JIS Z2274 untuk mengukur nilai Nf Fatigue Life dan untuk mengestimasi nilai RFL dengan teknik thermography. Selama pengujian dengan teknik thermography, perubahan temperature benda uji pada titik kritis diukur dengan menggunkan infrared kamera. Dari hasil pengukuran perubahan temperature tersebut telah dilakukan estimasi nilai RFL. Material yang dipakai adalah S45C. Pengujian dilakukan dalam putaran 1600 rpm dengan kondisi suhu lingkungan 25 C. Akhirnya dapat disimpulkan bahwa nilai RFL hasil estimasi dengan teknik thermograpy sangat mendekati dengan nilai RFL hasil pengujian.

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ABSTRACT
There are several techniques to estimate the RFL remaining fatigue life such as thermography techniques. In this study, rotating bending fatigue testing has been done completely with the standard JIS Z2274 to measure the value of Nf Fatigue Life and to estimate the value of the RFL using thermography techniques. During the testing with the techniques, the positive of temperature gradient of the specimen at the critical point is measured by using the infrared camera. From the result of temperature change, the RFL value can be estimated. The material used is S45C. The test is carried out in 1600 rpm with ambient temperature of 25 C. Finally, it can be concluded that the estimated RFL value with thermograpy technique is very close to the value of RFL test result.

Abstrak :
Menjaga keberlanjutan performa maksimal panel surya menjadi tantangan terbesar pembangkit listrik tenaga surya (PLTS) saat ini. Hal ini dikarenakan panel surya rentan terhadap kegagalan yang mengurangi daya keluaran akibat faktor lingkungan. Konsekuensinya, ekspektasi payback period PLTS cukup panjang berpotensi tidak tercapai. Sehingga, operasi pemeliharaan harus rutin dilakukan menggunakan termografi karena beberapa kegagalan tidak terlihat kasat mata. Namun demikian, apabila pemeliharaan dilakukan secara manual untuk PLTS berskala besar berkapasitas diatas 1 MW dengan luas 2,3-2,9 ha, akan menghabiskan banyak waktu dan sumber daya. Metode aerial infrared thermography (AIRT) memberikan operasi pemeliharaan yang cepat dan efisien dengan mengambil citra termal radiometrik secara otomatis berdasarkan pengaturan waypoint pada unmanned aerial vehicle (UAV). Kemudian pendeteksian kegagalan panel surya dilakukan menggunakan algoritma pengolahan citra yang umumnya adalah digital image processing (DIP). Akan tetapi, DIP membutuhkan penyesuaian parameter untuk setiap citra barunya. Oleh karena itu, penelitian ini menggunakan deep learning (DL) untuk mendeteksi setiap jenis kegagalan panel surya monofacial dan bifacial. Himpunan data (dataset) citra termal yang disusun sudah memenuhi standar inspeksi yaitu nilai irradiasi diantara 500-700 W/m2. Lalu, dilakukan skenario deteksi untuk PLTS dengan panel monofacial, bifacial, atau campuran. Hasil evaluasi model DL menunjukkan mean average precision (mAP) setiap skenario bernilai diatas 80% sehingga dapat diaplikasikan pada operasio pemeliharaan PLTS skala besar. ......Maintaining the maximum performance of solar panels poses the foremost challenge for solar photovoltaic power plants in this era. This is due to panel’s vulnerability to photovoltaic (PV) defect which reduces power output due to environmental factors. Consequently, the expected payback period which has been established for a considerable duration may not be achieved. Therefore, routine maintenance operations using thermography are necessary as certain failures are not visually detectable. Nevertheless, performing these operations manually on large-scale solar power plants with a capacity above 1 MW and an area of 2.3-2.9 ha would consume a significant amount of time and resources. The aerial infrared thermography (AIRT) technique enables fast and efficient maintenance operations by automatically capturing radiometric thermal images utilizing unmanned aerial vehicle (UAV) configured with predefined waypoint settings. Subsequently, the PV defect detection is typically performed using digital image processing (DIP) algorithm. However, DIP requires parameter adjustments for each new image. Hence, this study utilizes deep learning (DL) to detect different types of PV defect for both monofacial and bifacial solar panels. The constructed thermal image dataset adheres to inspection standards, which irradiance values ranging from 500-700 W/m2. Then, detection scenarios were conducted for solar power plants utilizing monofacial, bifacial, or mixed panels. The evaluations results of the DL model yielded mean average precision (mAP) values above 80% for each scenario, confirming its applicability in large-scale solar power plants maintenance activities.

Abstrak :
Residual Stress, Thermomechanics & Infrared Imaging, Hybrid Techniques and Inverse Problems, Volume 7 of the Proceedings of the 2018 SEM Annual Conference & Exposition on Experimental and Applied Mechanics, the seventh volume of eight from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on a wide range of areas, including: - Inverse Problems/Hybrid Techniques - Material Characterizations Using Thermography - Thermoelastic Stress Analysis - Fatigue & Damage Evaluation Using Infrared Thermography - Integration of Infrared Thermography & DIC - Thermographic Non-Destructive Evaluation (NDE)