Fault Diagnosis of Combustion Engines in MTU 16VS4000-G81 Generator Sets Using Fuzzy Logic: An Approach to Normalize Specific Fuel Consumption

Fernández, J. C.; Barrios, Luis Benigno. Corrales; Benítez, Israel; Álvarez, José R.

doi:10.1007/978-3-030-98457-1_2

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…ANNs have been shown to be extremely effective in applications involving vast amounts of data, such as deep learning [94]. In contrast to the simple linear regression method, which portrays the relationship between input and output variables with a single equation, fuzzy logic-based regression models rely on local functions and provide global approximations in a nonlinear relationship [95]. Consequently, local functions or membership functions are combined into a single expression.…”

Section: Ann Structurementioning

confidence: 99%

Using Artificial Neural Networks for Predicting Ship Fuel Consumption

Nguyen

Sakthivel

Rudzki

et al. 2023

Polish Maritime Research

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In marine vessel operations, fuel costs are major operating costs which affect the overall profitability of the maritime transport industry. The effective enhancement of using ship fuel will increase ship operation efficiency. Since ship fuel consumption depends on different factors, such as weather, cruising condition, cargo load, and engine condition, it is difficult to assess the fuel consumption pattern for various types of ships. Most traditional statistical methods do not consider these factors when predicting marine vessel fuel consumption. With technological development, different statistical models have been developed for estimating fuel consumption patterns based on ship data. Artificial Neural Networks (ANN) are some of the most effective artificial methods for modelling and validating marine vessel fuel consumption. The application of ANN in maritime transport improves the accuracy of the regression models developed for analysing interactive relationships between various factors. The present review sheds light on consolidating the works carried out in predicting ship fuel consumption using ANN, with an emphasis on topics such as ANN structure, application and prediction algorithms. Future research directions are also proposed and the present review can be a benchmark for mathematical modelling of ship fuel consumption using ANN.

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Section: Ann Structurementioning

confidence: 99%

Using Artificial Neural Networks for Predicting Ship Fuel Consumption

Nguyen

Sakthivel

Rudzki

et al. 2023

Polish Maritime Research

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“…However, these methods remain of limited use because they can be costly, not flexible to accommodate unforeseen changes in systems, also provide a false sense of security by overlooking human errors that can contribute to EPG faults. Other studies proposed artificial intelligence methods for industrial systems faults analysis, such as: artificial neural networks for fault diagnosis [5], fuzzy logic [6], and support vector machine [7]. Other authors have used artificial techniques for optimizing EPG operation and improve its availability such as: particle swarm optimization [8] and genetic algorithms [9].…”

Section: Introductionmentioning

confidence: 99%

Electrical Power Generator Faults Analysis Using Fault Tree and Bayesian Network

Touil,

Lakehal

2023

Acta Universitatis Sapientiae, Electrical and Mechanical Engineering

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This paper presents a model to predict Electrical Power Generator (EPG) faults. The fault tree (FT) model is developed and used to help maintenance engineers in fault analysis procedure of this rotating machine. By identifying the main, intermediate and basic events it’s possible to construct the FT with logical reasoning. The top dreaded event is defined. By using a Bayesian network (BN) as a complementary tool, fault prediction of the EPG becomes possible and easy. By using the developed BN, the probability of occurrence of the top event (EPG failure) is calculated. Also, by this approach, we can process complex information that causes system faults in an easy and simple way. The essential elements to do this analysis are the reliable and good exploitation of the information previously stored in the system. The use of the BN in combination with the FT gives the possibility of qualitative and quantitative analysis, diagnosis, and prediction of faults from the same Bayesian model. The flexibility of the proposed BN model in this paper allows better and precise decision making. Also, priorities regarding maintenance job are defined and resources are a priori prepared.

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Mitigation of harmonics in a 6 kV and 650 kW motor

Corrales-Barrios,

Fernández-Blanco,

Nuñez-Alvarez

et al. 2023

Electr Eng

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Motors that work with high voltage and consume a lot of power, specifically those of 6 kV and 650 kW, generate harmonics of voltage and current harmful to the electro-energy system, causing problems in its correct operation. In this study, the quality of the electrical energy of a motor having those characteristics is analyzed by measuring voltage, current intensity, power factor, active power, reactive power, as well as voltage and current harmonics. Results show that the main problem lies in the fact that the total current harmonic distortion reached a value of 17.1%, which is above the maximum value permissible according to the standard for the motor object of study. However, the total voltage harmonic distortion resulted within the allowable values, reaching a maximum of 3.3%. To mitigate the harmful effects of the total current harmonic distortion, passive and active filters were designed and simulated to improve the motor's operation and solve the problems. For the design and simulation of the filters, MATLAB R2015a software and LabView 2014 software were used.

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Fault Diagnosis of Combustion Engines in MTU 16VS4000-G81 Generator Sets Using Fuzzy Logic: An Approach to Normalize Specific Fuel Consumption

Cited by 4 publications

References 18 publications

Using Artificial Neural Networks for Predicting Ship Fuel Consumption

Using Artificial Neural Networks for Predicting Ship Fuel Consumption

Electrical Power Generator Faults Analysis Using Fault Tree and Bayesian Network

Mitigation of harmonics in a 6 kV and 650 kW motor

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