In recent years, the reliability and safety requirements of ship systems have increased drastically. This has prompted a paradigm shift toward the development of prognostics and health management (PHM) approaches for these systems' critical maritime components. In light of harsh environmental conditions with varying operational loads, and a lack of fault labels in the maritime industry generally, any PHM solution for maritime components should include independent and intelligent fault detection algorithms that can report faults automatically. In this paper, we propose an unsupervised reconstruction-based fault detection algorithm for maritime components. The advantages of the proposed algorithm are verified on five different data sets of real operational run-to-failure data provided by a highly regarded industrial company. Each data set is subject to a fault at an unknown time step. In addition, different magnitudes of random white Gaussian noise are applied to each data set in order to create several real-life situations. The results suggest that the algorithm is highly suitable to be included as part of a pure data-driven diagnostics approach in future end-to-end PHM system solutions. INDEX TERMS Automatic fault detection, deep learning, maritime industry, prognostics and health management, unsupervised learning.
A vision system has been developed for automatic quality assessment of robotic cleaning of fish processing lines. The quality assessment is done by detecting residual fish blood on cleaned surfaces. The system is based on classification using convolutional neural networks (CNNs). The performance of different convolutional neural network architectures and parameters is evaluated. The datasets that simulate various conditions in fish processing plants are generated using data augmentation techniques. Tests using further augmented training data to increase the performance of the neural network are performed, which results in a substantial increase in performance both compared to the color thresholding technique and the same neural network architecture without augmented training data. The performance of the system is validated in experiments in an industrial setting. INDEX TERMS Aquaculture, robot vision system, machine learning, computer vision.
Abstract-This paper presents the development of a robotic cleaning solution for fish processing plant. The project is currently at the stage of a first prototype consisting of a serial manipulator, a vertical linear axis and a rotational axis for the vertical axis. The purpose of the prototype is to validate the cleaning quality of a robotic cleaning solution. A cleaning solution will have to spray equipment and machines in the processing plant with chemicals and water to remove fish residue and bacterias, and special design considerations have to be taken with regards to water proofing and corrosion resistance. In order to validate such a system, a cleaning test were performed on an electric stunner, a machine typically found in salmon slaughterhouses. Results from the cleaning test shows that robotic cleaning of fish processing equipment can deliver an acceptable cleaning result. However, several issues related to making a system that can clean a whole plant still exists. Further work will require the development of a custom serial manipulator and a custom linear axis for navigating the manipulatorThe main purpose of this research is to present design considerations and investigate the validity of a robotic cleaning solution aimed at fish processing plants. This research is at TRL 5 and will enable further work with robotizing cleaning in challenging areas.
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