Asynchronized Synchronous Motor-Based More Electric Ship-Less Power Electronics for More System Reliability

© 2019 IEEE. Nowadays, the fully power decoupled shipboard power system (SPS) architecture is popular. However, the volume of fragile power electronic converters is large, and the system overload capability is low. In this paper, an asynchronized synchronous motor (ASM)-based SPS is proposed for more-electric ships to handle these issues. The models of the simplified synchronous generator (SSG), ASM, back-to-back converter, and supercapacitor bank are established. Besides, the transfer function of the SSG excitation system is obtained, with the SSG stability analyzed. Moreover, an ASM control strategy based on the emulated stator voltage orientation (ESVO) without the phase-locked loop is proposed to control the ASM. By using the proposed ESVO scheme, the impacts on electric machines are mitigated by the effective SSG stator power control and ASM torque control. High quality of the three-phase voltage and current waveforms can also be obtained. Furthermore, the simulation study is carried out in MATLAB/Simulink to verify the performance of the proposed ASM-SPS. The proposed ESVO scheme and the conventional stator-flux-oriented control strategy are implemented, and the operation of an induction-motor-based SPS with grid voltage orientation is also illustrated for comparison, with frequent propulsion load variations taken into consideration

Prognostic health management of repairable ship systems through different autonomy degree; From current condition to fully autonomous ship

Maritime characteristics make the progress of automatic operations in ships slow, especially compared to other means of transportation. This caused a great progressive deal of attention for Autonomy Degree (AD) of ships by research centers where the aims are to create a well-structured roadmap through the phased functional maturation approach to autonomous operation. Application of Maritime Autonomous Surface Ship (MASS) requires industries and authorities to think about the trustworthiness of autonomous operation regardless of crew availability on board the ship. Accordingly, this paper aims to prognose the health state of the conventional ships, assuming that it gets through higher ADs. To this end, a comprehensive and structured Hierarchal Bayesian Inference (HBI)-based reliability framework using a machine learning application is proposed. A machinery plant operated in a merchant ship is selected as a case study to indicate the advantages of the developed methodology. Correspondingly, the given main engine in this study can operate for 3, 17, and 47 weeks without human intervention if the ship approaches the autonomy degree of four, three, and two, respectively. Given the deterioration ratio defined in this study, the acceptable transitions from different ADs are specified. The aggregated framework of this study can aid the researchers in gaining online knowledge on safe operational time and Remaining Useful Lifetime (RUL) of the conventional ship while the system is being left unattended with different degrees of autonomy.</p

Deep Learning Methods for Visual Fault Diagnostics of Dental X-ray Systems

Dental X-ray systems go through rigorous quality assurance protocols following their production and assembly. The protocols include tests, which address the image quality and find certain errors or artifacts that may be present in the images. Detecting faults from the images require human effort, experience, and time. Recent advances in deep learning have proven them to be successful in image classification, object detection, machine translation. The applications of deep learning can be extended to fault detection in X-ray systems. This thesis work consists of surveying, applying, and developing state-of-art deep learning approaches for detection of visual faults or artifacts in the dental X-ray systems. In this thesis, we have shown that deep learning methods can detect geometry and collimator artifacts from X-ray images efficiently and rapidly. This thesis is a precursor for further development of deep learning methods to include detection of wide range of faults and artifacts in X-ray systems to ease quality assurance, calibration, and device maintenance