Application of Artificial Neural Network Based Gas Path Diagnostics on Gas Pipeline Compressors

2020 ◽  
Author(s):  
Suleiman M. Suleiman ◽  
Yi-Guang Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Field Measurements ◽  
Discharge Temperature ◽  
Novel Approach ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Compressor Map ◽  
Map Data

Abstract This paper presents the development of an artificial neural network (ANN) Gas Path Diagnostics (GPD) technique applied to pipeline compression system for fault detection and quantification. The work detailed the various degradation mechanisms and the effect of such degradations on the performance of natural gas compressors. The data used in demonstrating the ANN diagnostics is so derived using an advanced thermodynamic performance simulation model of integrated pipeline and compressor systems, which has embedded empirical compressor map data and pipeline resistance model. Implantation of faults within the model is in such a way to account for faults degradations caused by fouling, erosion and corrosion, of various degrees of severities, to obtain wide range of corresponding simulated “true” measurements. In order to account for uncertainties normally encountered in field measurements, Gaussian noise distribution was combined with simulated true measurements, which depends on the instrument’s tolerances. Furthermore, since judicious measurements selection are crucial in ensuring flawless GPD predictions, a sensitivity and correlation analysis of the available measurements revealed that discharge temperature, rotational speed and torque are the most effective measurements for the diagnostics with acceptable degrees of accuracies. The measurements observability technique is a novel approach in pipeline compressor diagnostics. Analytical case studies of the developed method show that, a selected ANN architecture can detect and quantify faults related to degradation in efficiency and flow capacities in the presence of instrument error, varied operational and environmental conditions.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

A Novel Approach for Gas Turbine Condition Monitoring Combining CUSUM Technique and Artificial Neural Network

2009 ◽  
Author(s):  
Magnus Fast ◽  
Thomas Palme´ ◽  
Magnus Genrup
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Turbine ◽  
Condition Monitoring ◽  
Sequential Analysis ◽  
Ann Model ◽  
Novel Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Operational Data

Investigation of a novel condition monitoring approach, combining artificial neural network (ANN) with a sequential analysis technique, has been reported in this paper. For this purpose operational data from a Siemens SGT600 gas turbine has been employed for the training of an ANN model. This ANN model is subsequently used for the prediction of performance parameters of the gas turbine. Simulated anomalies are introduced on two different sets of operational data, acquired one year apart, whereupon this data is compared with corresponding ANN predictions. The cumulative sum (CUSUM) technique is used to improve and facilitate the detection of such anomalies in the gas turbine’s performance. The results are promising, displaying fast detection of small changes and detection of changes even for a degraded gas turbine.

scholarly journals SEIRDQ: A COVID-19 case projection modeling framework using ANN to model quarantine

2021 ◽  
Author(s):  
Harish Chandra ◽  
Xianwei Meng ◽  
Arman Margaryan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Sources ◽  
Modeling Framework ◽  
Novel Approach ◽  
Out Of Sample ◽  
Artificial Neural ◽  
Model Versus ◽  
Artificial Neural Network Ann ◽  
The Impact

We propose and implement a novel approach to model the evolution of COVID-19 pandemic and predict the daily COVID-19 cases (infected, recovered and dead). Our model builds on the classical SEIR-based framework by adding additional compartments to capture recovered, dead and quarantined cases. Quarantine impacts are modeled using an Artificial Neural Network (ANN), leveraging alternative data sources such as the Google mobility reports. Since our model captures the impact of lockdown policies through the quarantine functions we designed, it is able to model and predict future waves of COVID-19 cases. We also benchmark out-of-sample predictions from our model versus those from other popular COVID-19 case projection models.

scholarly journals A Novel Approach to Damage Assessment in Structures Based on Artificial Neural Network Working Parallel With a Hybrid Stochastic Optimization

2021 ◽  
Author(s):  
H. Tran-Ngoc ◽  
S Khatir ◽  
T. Le-Xuan ◽  
H. Tran - Viet ◽  
G. De Roeck ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Numerical Models ◽  
Global Search ◽  
Computer Algorithms ◽  
Local Minima ◽  
Starting Position ◽  
Novel Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Artificial neural network (ANN) is the study of computer algorithms that can learn from experience to improve performance. ANN employs backpropagation (BP) algorithms using gradient descent (GD)-based learning methods to reduce the discrepancies between predicted and real targets. Even though these differences are considerably decreased after each iteration, the network may still face major risks of being entrapped in local minima if complex error surfaces contain too numerous the best local solutions. To overcome those drawbacks of ANN, numerous researchers have come up with solutions to local minimum prevention by choosing a beneficial starting position that relies on the global search capability of other algorithms. This strategy possibly assists the network in avoiding the first local minima. However, a network often has many local bests widely distributed. Hence, the solution of choosing good starting points may no further be beneficial because the particles are probably entrapped in other local optimal solutions throughout the process of looking for the global best. Therefore, in this work, a novel ANN working parallel with the stochastic search capacity of evolutionary algorithms, is proposed. Additionally, to increase the efficiency of the global search capacity, a hybrid of particle swarm optimization and genetic algorithm (PSOGA) is applied during the process of seeking the best solution, which effectively guarantees to assist the network of ANN in escaping from local minima. This strategy gains both benefits of GD techniques as well as the global search capacity of PSOGA that possibly solves the local minima issues thoroughly. The effectiveness of ANNPSOGA is assessed using both numerical models consisting of various damage cases (single and multiple damages) and a free-free steel beam with different damage levels calibrated in the laboratory. The results demonstrate that ANNPSOGA provides higher accuracy than traditional ANN, PSO, and other hybrid ANNs (even a higher level of noise is employed) and also considerably decreases calculational cost compared with PSO.

Using Artificial Neural Network (ANN) for Manipulating Energy Gain of Nansulate Coating

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Hadi Salehi ◽  
Mosayyeb Amiri ◽  
Morteza Esfandyari
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Energy Gain ◽  
Operating Conditions ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work, an extensive experimental data of Nansulate coating from NanoTechInc were applied to develop an artificial neural network (ANN) model. The Levenberg–Marquart algorithm has been used in network training to predict and calculate the energy gain and energy saving of Nansulate coating. By comparing the obtained results from ANN model with experimental data, it was observed that there is more qualitative and quantitative agreement between ANN model values and experimental data results. Furthermore, the developed ANN model shows more accurate prediction over a wide range of operating conditions. Also, maximum relative error of 3% was observed by comparison of experimental and ANN simulation results.

Identification of Modified Starches Using Infrared Spectroscopy and Artificial Neural Network Processing

1998 ◽  
Vol 52 (3) ◽  
pp. 329-338 ◽  
Author(s):  
Ludmila Dolmatova ◽  
Cyril Ruckebusch ◽  
Nathalie Dupuy ◽  
Jean-Pierre Huvenne ◽  
Pierre Legrand
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Infrared Spectra ◽  
Corn Starch ◽  
Raw Materials ◽  
Attenuated Total Reflection ◽  
Novel Approach ◽  
Modified Starches ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The authentication of food is a very important issue both for the consumers and for the food industry with respect to all levels of the food chain from raw materials to finished products. Corn starch can be used in a wide variety of food preparation as bakery cream fillings, sauce, or dry mixes. There are many modifications of the corn starch in connection with its use in the agrofood industry. This paper describes a novel approach to the classification of modified starches and the recognition of their modifications by artificial neural network (ANN) processing of attenuated total reflection Fourier transform spectroscopy (ATR/FT-IR) spectra. Using the self-organizing artificial neural network of the Kohonen type, we can obtain natural groupings of similarly modified samples on a two-dimensional plane. Such mapping provides the expert with the possibility of analyzing the distribution of samples and predicting modifications of unknown samples by using their relative position with respect to existing clusters. On the basis of the available information in the infrared spectra, a feedforward artificial neural network, trained with the intensities of the derivative infrared spectra as input and the starch modifications as output, allows the user to identify modified starches presented as prediction samples.

scholarly journals Regime identification of slurry transport in pipelines: A novel modelling approach using ANN & differential evolution

2010 ◽  
Vol 16 (4) ◽  
pp. 329-343 ◽  
Author(s):  
Sandip Lahiri ◽  
K.C. Ghanta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Differential Evolution ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Slurry Transport ◽  
Pipeline Design

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and differential evolution technique (ANN - DE) for efficient tuning of ANN Meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN - DE method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters. .

Monitoring of Self-Tapping Screw Fastenings Using Artificial Neural Networks

2005 ◽  
Vol 127 (1) ◽  
pp. 236-243 ◽  
Author(s):  
Kaspar Althoefer ◽  
Bruno Lara ◽  
Lakmal D. Seneviratne
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Simulation Data ◽  
Insertion Depth ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Novel Strategy ◽  
Assembly Operations

Screw fastenings account for a quarter of all assembly operations and automation of the process is highly desirable. This paper presents a novel strategy for monitoring this manufacturing process, focusing on the insertion of self-tapping screws. An artificial neural network (ANN), using “Torque-versus-Insertion-Depth” signature signals as input, is designed to distinguish between successful and failed insertions. The ANN is first tested using simulation data from an analytical model for screw insertions, and then validated using experimental torque signals obtained from an electric screwdriver. The results demonstrate that ANNs can effectively monitor the screw fastening process and cope with a wide range of insertion cases interpolating for unseen insertion signals.

scholarly journals ARTIFICIAL NEURAL NETWORK MODELING OF THE CONFORMABLE FRACTIONAL ISOTHERMAL GAS SPHERES

2021 ◽  
Vol 57 (1) ◽  
pp. 189-198
Author(s):  
Yosry A. Azzam ◽  
Emad A-B. Abdel-Salam ◽  
Mohamed I. Nouh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Density Profiles ◽  
Emden Equation ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Isothermal Gas

The isothermal gas sphere is a particular type of Lane-Emden equation and is used widely to model many problems in astrophysics, like the formation of stars, star clusters and galaxies. In this paper, we present a computational scheme to simulate the conformable fractional isothermal gas sphere using an artificial neural network (ANN) technique, and we compare the obtained results with the analytical solution deduced using the Taylor series. We performed our calculations, trained the ANN, and tested it using a wide range of the fractional parameter. Besides the Emden functions, we calculated the mass-radius relations and the density profiles of the fractional isothermal gas spheres. The results obtained show that the ANN could perfectly simulate the conformable fractional isothermal gas spheres.

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