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

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

Estimation of gas turbine shaft torque and fuel flow of a CODLAG propulsion system using genetic programming algorithm

In this paper, the publicly available dataset of condition based maintenance of combined diesel-electric and gas (CODLAG) propulsion system for ships has been utilized to obtain symbolic expressions which could estimate gas turbine shaft torque and fuel flow using genetic programming (GP) algorithm. The entire dataset consists of 11934 samples that was divided into training and testing portions of dataset in an 80:20 ratio. The training dataset used to train the GP algorithm to obtain symbolic expressions for gas turbine shaft torque and fuel flow estimation consisted of 9548 samples. The best symbolic expressions obtained for gas turbine shaft torque and fuel flow estimation were obtained based on their R2 score generated as a result of the application of the testing portion of the dataset on the aforementioned symbolic expressions. The testing portion of the dataset consisted of 2386 samples. The three best symbolic expressions obtained for gas turbine shaft torque estimation generated R2 scores of 0.999201, 0.999296, and 0.999374, respectively. The three best symbolic expressions obtained for fuel flow estimation generated R2 scores of 0.995495, 0.996465, and 0.996487, respectively

Prediction of Robot Grasp Robustness using Artificial Intelligence Algorithms

Predicting the quality of the robot end-effector grasp quality during an industrial robot manipulator operation can be an extremely complex task. As is often the case with such complex tasks, Artificial Intelligence methods may be applied to attempt the creation of a model - if sufficient data exists. The presented dataset uses a publicly available dataset, consisting of 992632 measurements of position, torque, and velocity - for each of the three joints of three fingers of the simulated end-effector. The dataset is first analyzed and pre-processed to prepare it for model training. The duplicate values are removed from the dataset, as well as the statistical outliers. Then, a multilayer perceptron (MLP) machine learning algorithm is applied to 80% of the data contained in the dataset, using the Grid Search algorithm to determine the best combination of MLP hyperparameters. As the dataset consists of torque, velocity, and speed measurements for separate joints and fingers of the tested end-effector the testing is performed to see if a subset of the inputs may be used to regress the robustness of the given grip. The normalization of the dataset is also applied, and its effect on the regression quality is tested. The results, evaluated with the coefficient of determination, show that while the best model is achieved using all the possible inputs, a satisfactory result can be obtained using only velocity and torque.The results also show that the normalization of the dataset improves the regression quality in all the observed cases

Neural Network-Based Model for Classification of Faults During Operation of a Robotic Manipulator

The importance of error detection is high, especially in modern manufacturing processes where assembly lines operate without direct supervision. Stopping the faulty operation in time can prevent damage to the assembly line. Public dataset is used, containing 15 classes, 2 types of faultless operation and 13 types of faults, with 463 force and torsion datapoints. Four different methods are used: Multilayer Perceptron (MLP) selected due to high classification performance, Support Vector Machines (SVM) commonly used for a low number of datapoints, Convolutional Neural Network (CNN) known for high performance in classification with matrix inputs and Siamese Neural Network (SNN) novel method with high performance in small datasets. Two classification tasks are performed-error detection and classification. Grid search is used for hyperparameter variation and F1 score as a metric, with a 10 fold cross-validation. Authors propose a hybrid system consisting of SNN for detection and CNN for fault classification

Marine Objects Recognition Using Convolutional Neural Networks

One of the challenges of maritime affairs is automatic object recognition from aerial imagery. This can be achieved by utilizing a Convolutional Neural Network (CNN) based algorithm. For purposes of these research a dataset of 5608 marine object images is collected by using Google satellite imagery and Google Image Search. The dataset is divided in two main classes ("Vessels" and "Other objects") and each class is divided into four sub-classes ("Vessels" sub-classes are "Cargo ships", "Cruise ships", "War ships" and "Boats", while "Other objects" sub-classes are "Waves", "Marine animals", “Garbage patches" and "Oil spills"). For recognition of marine objects, an algorithm constructed with three CNNs is proposed. The first CNN for classification on the main classes achieves accuracy of 92.37 %. The CNN used for vessels recognition achieves accuracies of 94.12 % for cargo ships recognition, 98.82 % for cruise ships recognition, 97.64 % for war ships recognition and 95.29 % for boats recognition. The CNN used for recognition of other objects achieves accuracies of 88.56 % for waves and marine animals recognition, 96.92 % for garbage patches recognition and 89.21 % for oil spills recognition. This research has shown that CNN is appropriate artificial intelligence (AI) method for marine object recognition from aerial imagery

COMPARISON OF ENERGY FLOW STREAM AND ISENTROPIC METHOD FOR STEAM TURBINE ENERGY ANALYSIS

In this paper, a comparison of two different methods for a steam turbine energy analysis is presented. A high-pressure steam turbine from a supercritical thermal power plant (HPT) was analysed at three different turbine loads using the energy flow stream (EFS) method and isentropic (IS) method. The EFS method is based on steam turbine input and output energy flow streams and on the real steam turbine produced power. The method is highly dependable on the steam mass flow rate lost through the turbine gland seals. The IS method is based on a comparison of turbine steam expansion processes. Observed energy analysis methods cannot be directly compared because they are based on different sources of steam turbine energy losses, so, an overall steam turbine energy analysis is presented. Unlike most steam turbines from the literature, the analysed HPT did not have the highest overall energy efficiency at a full load due to exceeding the water/steam critical pressure at the turbine inlet during such operation

Multilayer Perceptron approach to Condition-Based Maintenance of Marine CODLAG Propulsion System Components

In this paper multilayer perceptron (MLP) approach to condition-based maintenance of combined diesel-electric and gas (CODLAG) marine propulsion system is presented. By using data available in UCI, online machine learning repository, MLPs for prediction of gas turbine (GT) and GT compressor decay state coefficients are designed. Aforementioned MLPs are trained and tested by using 11 934 samples, of which 9 548 samples are used for training and 2 386 samples are used testing. In the case of GT decay state coefficient prediction, the lowest mean relative error of 0.622 % is achieved if MLP with one hidden layer of 50 artificial neurons (AN) designed with Tanh activation function is utilized. This configuration achieves the best results if it is trained by using L-BFGS solver. In the case of GT compressor decay state coefficient, the best results are achieved if MLP is designed with four hidden layers of 100, 50, 50 and 20 ANs, respectively. This configuration is designed by using Logistic sigmoid activation function. The lowest mean relative error of 1.094 % is achieved if MLP is trained by using L-BFGS solver