2020
DOI: 10.1108/ijqrm-07-2019-0249
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Fault diagnosis of blowout preventer system using artificial neural networks: a comparative study

Abstract: PurposeThe increasing complexity of industrial systems is at the heart of the development of many fault diagnosis methods. The artificial neural networks (ANNs), which are part of these methods, are widely used in fault diagnosis due to their flexibility and diversification which makes them one of the most appropriate fault diagnosis methods. The purpose of this paper is to detect and locate in real time any parameter deviations that can affect the operation of the blowout preventer (BOP) system using ANNs.Des… Show more

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Cited by 6 publications
(2 citation statements)
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“…The MLP model is an artificial neural network formed by the development of the perception machine, because the model can set multiple neural layers, and each neural layer can set multiple nodes, also known as a deep neural network. The simplest MLP model consists of an input layer, a hidden layer, and an output layer, respectively, as shown in Figure 1 [9].…”
Section: Basic Approachmentioning
confidence: 99%
“…The MLP model is an artificial neural network formed by the development of the perception machine, because the model can set multiple neural layers, and each neural layer can set multiple nodes, also known as a deep neural network. The simplest MLP model consists of an input layer, a hidden layer, and an output layer, respectively, as shown in Figure 1 [9].…”
Section: Basic Approachmentioning
confidence: 99%
“…The classes stand for types of failure and non-failure. Failure prediction is different from failure detection (Polycarpou and Vemuri, 1995), failure diagnosis (Chebira et al, 2021) and prediction of remaining useful life (Gokulachandran and Mohandas, 2015). The failure classes are usually represented by error codes standing for specific abnormal conditions of the system.…”
Section: Literature Review 21 Problem Statementmentioning
confidence: 99%