Penggunaan Metode Image Processing Sebagai Alat Karakterisasi Hasil Pelapisan pada Lambung Kapal

Indonesia is a maritime country that has a large territorial water, one of the efforts to protect the ship's hull from corrosion is by using coating technology. In this research, the process of characterizing the results of image processing on the hull was carried out to determine the corrosion level of the hull. 4 stages of the process, including: Taking samples and images of the layers of the ship parts that need maintenance and coating. In making a prototype, the coating results are assembled using the mini PCNVIDIA Jatson Nano using a webcam, then the images obtained will be processed using Edge detection uses a cany to obtain the contours of the bilge cross section of the ship. Next, using the Neural Network as an artificial material to create images captured from the captured prototype results on the results of coating or coating on the observed parts of the ship. The results of various image captures are processed and observed for shapes, patterns, contours, corrosion and coatings that are formed. The image processing method can be used as an inspection method for coating results with readings on data and software with 2 readings on data, namely reject or accept. Gradient values ​​are related to changes in MSE, so gradient values ​​cannot be used as a reference to justify model performance. With the addition of iterations, the value of the neural network and neural targets produced is linear.Indonesia merupakan negara maritim yang memiliki wilayah perairan yang luas, salah satu upaya untuk melindungi bagian lambung kapal dari korosi adalah dengan menggunakan teknologi pelapisan. Pada penelitian ini dilakukan proses karakterisasi hasil image processing pada lambung kapal untuk mengetahui tingkat korosi pada lambung kapal. 4 proses tahapan, diantaranya: Pengambilan sampel dan citra gambar lapisan bagian-bagian kapal yang perlu di maintenence dan di-coating, Pada pembuatan prototype pengujian hasil coating dirangkaikan dengan menggunakan mini PCNVIDIA Jatson Nano dengan menggunakan webcam, kemudian citra gambar yang didapatkan akan diolah menggunakan deteksi tepi menggunakan cany untuk mendapatkan kontur dari penampang bilge dari kapal. Selanjutnya menggunakan Neuraal Network sebagai bahan tiruan untuk membuat citra yang tertangkap dari hasil capture prototype terhadap hasil coating atau pelapisan pada bagian-bagian kapal yang diamati. Hasil dari berbagai tangkapan citra diproses dan diamati bentuk, pola, kontur, korosi dan pelapisan yang terbentuk. Metode image processing dapat dilakukan sebagai salah satu metode inspeksi hasil coating dengan hasil pembacaan pada data dan software dengan 2 hasil pembacaan pada data yakni reject atau accept. Nilai gradien berkaitan dengan perubahan MSE, sehingga nilai gradien tidak dapat digunakan sebagai acuan untuk membenarkan performa model. Adanya pertambahan iterasi nilai neural network dan neural target yang dihasilkan bernilai linier

AMCD : an accurate deep learning-based metallic corrosion detector for MAV-based real-time visual inspection

Corrosion has been concerned as a serious safety issue for metallic facilities. Visual inspection carried out by an engineer is expensive, subjective and time-consuming. Micro Aerial Vehicles (MAVs) equipped with detection algorithms have the potential to perform safer and much more efficient visual inspection tasks than engineers. Towards corrosion detection algorithms, convolution neural networks (CNNs) have enabled the power for high accuracy metallic corrosion detection. However, these detectors are restricted by MAVs on-board capabilities. In this study, based on You Only Look Once v3-tiny (Yolov3-tiny), an accurate deep learning-based metallic corrosion detector (AMCD) is proposed for MAVs on-board metallic corrosion detection. Specifically, a backbone with depthwise separable convolution (DSConv) layers is designed to realise efficient corrosion detection. The convolutional block attention module (CBAM), three-scale object detection and focal loss are incorporated to improve the detection accuracy. Moreover, the spatial pyramid pooling (SPP) module is improved to fuse local features for further improvement of detection accuracy. A eld inspection image dataset labelled with four types of corrosions (the nubby corrosion, bar corrosion, exfoliation and fastener corrosion) is utilised for training and testing the AMCD. Test results show that the AMCD achieves 84.96% mean average precision (mAP), which outperforms other state-of-the-art detectors. Meanwhile, 20.18 frames per second (FPS) is achieved leveraging NVIDIA Jetson TX2, the most popular MAVs on-board computer, and the model size is only 6.1MB

Hyperspectral imaging based corrosion detection in nuclear packages

In the Sellafield nuclear site, intermediate level waste and special nuclear material is stored above ground in stainless steel packages or containers, with thousands expected to be stored for several decades before permanent disposal in a geological disposal facility. During this intermediate storage, the packages are susceptible to corrosion, which can potentially undermine their structural integrity. Therefore, long term monitoring is required. In this work, hyperspectral imaging (HSI) was evaluated as a non-destructive tool for detecting corrosion on stainless steel surfaces. Real samples from Sellafield, including stainless steel 1.4404 (known as 316L) and 2205 plates from the Sellafield atmospheric testing corrosion site, were imaged in the experiments, measuring the spectral responses for corrosion in the visible near-infrared (VNIR, 400-1000 nm) and short-wave-infrared (SWIR, 900-2500 nm) regions. Based on the spectral responses observed, a new concept denoted as Corrosion Index (Ci) was introduced and evaluated to estimate corrosion maps. With the CI, every pixel in the hyperspectral image is given a value between zero and one, aimed at representing corrosion intensity for a given location of the sample. Results suggest that HSI, combined with our proposed CI analysis techniques, could be used for effective automated detection of corrosion in nuclear packages

Friction Stir Welding Manufacturing Advancement by On-Line High Temperature Phased Array Ultrasonic Testing and Correlation of Process Parameters to Joint Quality

Welding, a manufacturing process for joining, is widely employed in aerospace, aeronautical, maritime, nuclear, and automotive industries. Optimizing these techniques are paramount to continue the development of technologically advanced structures and vehicles. In this work, the manufacturing technique of friction stir welding (FSW) with aluminum alloy (AA) 2219-T87 is investigated to improve understanding of the process and advance manufacturing efficiency. AAs are widely employed in aerospace applications due to their notable strength and ductility. The extension of good strength and ductility to cryogenic temperatures make AAs suitable for rocket oxidizer and fuel tankage. AA-2219, a descendent of the original duralumin used to make Zeppelin frames, is currently in wide use in the aerospace industry. FSW, a solid-state process, joins the surfaces of a seam by stirring the surfaces together with a pin while the metal is held in place by a shoulder. The strength and ductility of friction stir (FS) welds depends upon the weld parameters, chiefly spindle rotational speed, feedrate, and plunge force (pinch force for self-reacting welds). Between conditions that produce defects, it appears in this study as well as those studies of which we are aware that FS welds show little variation in strength; however, outside this process parameter “window” the weld strength drops markedly. Manufacturers operate within this process parameter window, and the parameter establishment phase of welding operations constitutes the establishment of this process parameter window. The work herein aims to improve the manufacturing process of FSW by creating a new process parameter window selection methodology, creation of a weld quality prediction model, developing an analytical defect suppression model, and constructing a high temperature on-line phased array ultrasonic testing system for quality inspection

ATiTHi: A Deep Learning Approach for Tourist Destination Classification using Hybrid Parametric Optimization

A picture is best way to explore the tourist destination by visual content. The content-based image classification of tourist destinations makes it possible to understand the tourism liking by providing a more satisfactory tour. It also provides an important reference for tourist destination marketing. To enhance the competitiveness of the tourism market in India, this research proposes an innovative tourist spot identification mechanism by identifying the content of significant numbers of tourist photos using convolutional neural network (CNN) approach. It overcomes the limitations of manual approaches by recognizing visual information in photos. In this study, six thousand photos from different tourist destinations of India were identified and categorized into six major categories to form a new dataset of Indian Trajectory. This research employed Transfer learning (TF) strategies which help to obtain a good performance measure with very small dataset for image classification.VGG-16, VGG-19, MobileNetV2, InceptionV3, ResNet-50 and AlexNet CNN model with pretrained weight from ImageNet dataset was used for initialization and then an adapted classifier was used to classify tourist destination images from the newly prepared dataset. Hybrid hyperparameter optimization employ to find out hyperparameter for proposed Atithi model which lead to more efficient model in classification. To analyse and compare the performance of the models, known performance indicators were selected. As compared to the AlexNet model (0.83), MobileNetV2(0.93), VGG-19(0.918), InceptionV3(0.89), ResNet-50(0.852) the VGG16 model has performed the best in terms of accuracy (0.95). These results show the effectiveness of the current model in tourist destination image classification

Denoising autoencoder in damage detection of pipeline using guided ultrasonic wave

Pipeline condition monitoring is essential in critical sectors such as the petrochemical, nuclear and energy sectors. The guided ultrasonic wave (GUW) monitoring system is an available pipeline condition monitoring system that is gaining much attention owing to its portability, long coverage and high sensitivity to damage. However, environmental and operational conditions (EOCs) effects, especially temperature and random noise may generate unwanted peaks, which are falsely identified as damage. Attempts to deal with EOC effects have not solved the problem, especially for small damage cases (damage equal to or less than 5% cross sectional area loss (CSAL)). In this study, a new damage feature extraction method based on the residual reliability criterion (RRC) is proposed. The performance of the proposed method is measured using the established receiver operating characteristics (ROCs) performance evaluation method. The findings show that this method performs well, with an AUC value greater than 0.9, based on numerical model under 40 ? variations and 10% random noise level, and that the application of RRC is intuitively simple. To ensure the practicality of the method, a 6 metre long, 8 inches diameter experimental pipe model filled with liquid is used to form a GUW database of small damage under 30 ? variations by using Torsional T(0,1) excitation mode at 26 kHz centre frequency. However, the RRC underperformed when experimental data is used because the random noise generated by healthy and damaged signals interferes and generates high amplitude noise. Therefore, this study proposed a denoising autoencoder (DAE) neural network to deal with the effects of EOCs. A DAE decodes high-dimensional data into low-dimensional features and reconstructs the original data from these low-dimensional features. By providing GUW signals at a reference temperature, this structure forces the DAE to learn the essential features hidden within complex data. The proposed DAE showed perfect detection (AUC value of 1.0) using numerical model and performs well (AUC greater than 0.9) using experimental model in terms of small damage identification. Moreover, the proposed method showed superiority among other advanced EOC compensation techniques using both numerical and experimental models

Continuous Autonomous UAV Inspection for FPSO vessels

This Master's thesis represents the preliminary design study and proposes the unmanned aerial vehicle (UAV) -based inspection framework, comprising several multirotors with automatic charging and deployment for 24/7 integrity inspection tasks. This project has three main topics. First one describes the operational environment and existing regulations that cover use of UAVs. It forms the basis for proposal of the relevant use-case scenarios. Third part comprises two chapters, where design of concept and framework is being based on the previous factors. It shows that before implementation of fully autonomous inspection system, there is a need to cover both regulatory and technical gaps. It can be explained by the fact that there does not exist any autonomous inspection system today. Thus, this project can be seen as a base for future development of the UAV-based inspection system, as it focuses on creation of a general framework

Algorithms for Fault Detection and Diagnosis

Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others