Magnetic Bearing Control System based on PI and PID Controllers

This paper describes the implementation of magnetic bearings and their active control system. The working principle of the special combined axial and radial magnetic bearings and their implementation is explained. The overall laboratory system consists of two magnetic bearings with their housings, the suspended shaft and control system with sensors and power electronics. Sensor electronics are used to measure the position of the shaft within a bearing and to determine the appropriate bearing current with outer position control loops and nested current control loops. The motor control board featuring the TMS320F28335 DSP and LDC1000 proximity sensors is used for control and power electronics. In order to implement the control system the X2C tool is used. This is an open source model based development and code generation tool embedded in the Scilab/Xcos environment. The sensor data acquisition time is optimized to increase the speed of the control system

Implementation of Inductive Proximity Sensors in Active Magnetic Bearings

Research on the implementation of inductive proximity sensors in active magnetic bearings is performed in this paper. LDC1000 digital sensors manufactured by Texas Instruments are used since they claim to provide precision measured in microns and simple acquisition of the output data. The Sensor chip is connected with the sensing coil which is placed inside the magnetic bearing, close to electromagnets. This newly developed system created a platform for research in signal quality and its resistance to electrical noise. The controller used in this application was Texas Instruments TMS320F28335 digital signal processor (DSP). Software for data acquisition from LDC1000 was written and implemented in DSP using Code Composer Studio development environment. Two different output data are acquired and processed: proximity data and frequency count. Graphs presented in this paper show different resistance to electrical noise. A conclusion derived from this research can be applied in an industry where electromagnetic noise exists, together with the need for precise distance measurement

Analysis of Gas Turbine Operation before and after Major Maintenance

This paper presents an analysis of the gas turbine real process (with all losses included) before and after a major maintenance. The analysis of both gas turbine operating regimes is based on data measured during its exploitation. Contrary to authors’ expectations, the major maintenance process did not result either in any decrease in losses or increase in efficiencies for the majority of the gas turbine components. However, the major maintenance influenced positively the gas turbine combustion chambers (reduction in losses and increase in the combustion chambers efficiency). After the major maintenance, the overall process efficiency decreased from 43.796% to 41.319% due to a significant decrease in the air mass flow rate and to an increase in the fuel mass flow rate in combustion chambers. A decrease in the gas turbine produced cumulative and useful power after a major maintenance also increased the specific fuel consumption

Genetic Algorithm-Based Parametrization of a PI Controller for DC Motor Control

This paper analyses the application of a genetic algorithm (GA) for the purpose of designing the control system with separately excited DC motor controlled according to the rotor angle. The presented research is based on the utilization of a mathematical model designed with separate electrical and mechanical sub-systems. Such an approach allows fine-tuning of PID controllers by using an evolutionary procedure, mainly GA. For purpose of PID tuning, the new fitness function which combines several step response parameters with the aim of forming a unique surface which is then minimized with a genetic algorithm. From the results, it can be seen that the elitism-based algorithm achieved better results compared to the eligibility-based selection. Such an algorithm achieved a fitness value of 0.999982 resulting in a steady-state error of 0.000584 rad. The obtained results indicate the possibility of applying a GA in the parameterization of the PID controller for DC motor control

Exergy analysis of marine steam turbine labyrinth (gland) seals

The paper presents an exergy analysis of marine steam turbine labyrinth (gland) seals - an inevitable component of any marine steam turbine cylinder, in three different operating regimes. Throughout labyrinth seals, steam specific enthalpy can be considered as a constant because the results obtained by this assumption do not deviate significantly from the results of complex numerical models. Changes in labyrinth seals exergy efficiency and specific exergy destruction are reverse proportional. The analyzed labyrinth seals have high exergy efficiencies in each observed operating regime at the ambient temperature of 298 K (above 92%), what indicates seals proper operation. An increase in the ambient temperature resulted with a decrease in labyrinth seals exergy efficiency, but even at the highest observed ambient temperature of 318 K, seals exergy efficiency did not fall below 90.5% in each observed operating regime

Thermodynamic Analysis of CO2 Closed-Cycle Gas Turbine for Marine Applications at Various Pressure Ratios

A thermodynamic analysis of CO2 closed-cycle gas turbine is presented in this paper. Two processes are investigated - base process and the same process upgraded with a heat regenerator. Maximum specific useful work is 159.94 kJ/kg for both observed processes. Involving of heat regenerator inside base CO2 closed-cycle gas turbine process requires attention due to required temperature differences - high pressure ratios cannot be obtained with a high efficiency heat regenerator. Base CO2 closed-cycle gas turbine process did not reach cycle efficiency higher than 25%, while for the upgraded process the cycle efficiency can reach 40% at high pressure ratio and for high regenerator efficiency. Additionally, multilayer perceptron is trained in order to achieve high quality models for estimating specific useful work and efficiency for both, base and upgraded process. As a result, MLP with three hidden layers achieved high values of R2 score

Energy Loss Analysis at the Gland Seals of a Marine Turbo-Generator Steam Turbine

The paper presents an analysis of marine Turbo-Generator Steam Turbine (TGST) energy losses at turbine gland seals. The analyzed TGST is one of two identical Turbo-Generator Steam Turbines mounted in the steam propulsion plant of a commercial LNG carrier. Research is based on the TGST measurement data obtained during exploitation at three different loads. The turbine front gland seal is the most important element which defines TGST operating parameters, energy losses and energy efficiencies. The front gland seal should have as many chambers as possible in order to minimize the leaked steam mass flow rate, which will result in a turbine energy losses’ decrease and in an increase in energy efficiency. The steam mass flow rate leakage through the TGST rear gland seal has a low or negligible influence on turbine operating parameters, energy losses and energy efficiencies. The highest turbine energy efficiencies are noted at a high load – on which TGST operation is preferable

Use of Convolutional Neural Network for Fish Species Classification

Fish population monitoring systems based on underwater video recording are becoming more popular nowadays, however, manual processing and analysis of such data can be time-consuming. Therefore, by utilizing machine learning algorithms, the data can be processed more efficiently. In this research, authors investigate the possibility of convolutional neural network (CNN) implementation for fish species classification. The dataset used in this research consists of four fish species (Plectroglyphidodon dickii, Chromis chrysura, Amphiprion clarkii, and Chaetodon lunulatus), which gives a total of 12859 fish images. For the aforementioned classification algorithm, different combinations of hyperparameters were examined as well as the impact of different activation functions on the classification performance. As a result, the best CNN classification performance was achieved when Identity activation function is applied to hidden layers, RMSprop is used as a solver with a learning rate of 0.001, and a learning rate decay of 1e-5. Accordingly, the proposed CNN model is capable of performing high-quality fish species classifications

Intelligent Automation System for Vessels Recognition: Comparison of SIFT and SURF Methods

Nowadays, with the rise of drone and satellite technology, there is a possibility for its application in sea and coastal surveillance. An advantage of this type of application is the automated recognition of marine objects, among which the most important are vessels. This paper presents the principle of vessel recognition based on the extraction of satellite image features of the vessel and the application of a multilayer perceptron (MLP). Dataset used in this research contains the total of 2750 images, where 2112 images are used as training set while the remaining 638 images are used for testing purposes. The SIFT and SURF algorithms were used to extract image features, which were later used as the input vector for MLP.The best results are achieved if a model with four hidden layers is used. These layers are constructed with 32, 128, 32, 128 neurons with ReLU activation function, respectively. Regarding the application of feature extraction, it can be observed that better results are achieved if the SIFT algorithm is used. The ROC AUC value achieved with the combination of SIFT and MLP reaches 0.99

Use of Artificial Neural Network for Estimation of Propeller Torque Values in a CODLAG Propulsion System

An artificial neural network (ANN) approach is proposed to the problem of estimating the propeller torques of a frigate using combined diesel, electric and gas (CODLAG) propulsion system. The authors use a multilayer perceptron (MLP) feed-forward ANN trained with data from a dataset which describes the decay state coefficients as outputs and system parameters as inputs – with a goal of determining the propeller torques, removing the decay state coefficients and using the torque values of the starboard and port propellers as outputs. A total of 53760 ANNs are trained – 26880 for each of the propellers, with a total 8960 parameter combinations. The results are evaluated using mean absolute error (MAE) and coefficient of determination (R2). Best results for the starboard propeller are MAE of 2.68 [Nm], and MAE of 2.58 [Nm] for the port propeller with following ANN configurations respectively: 2 hidden layers with 32 neurons and identity activation and 3 hidden layers with 16, 32 and 16 neurons and identity activation function. Both configurations achieve R2 value higher than 0.99