Accurate life prediction of aircraft engine components is very critical because it has a direct impact on aircraft safety and on operators' profits. The engine bleed air system valves have considerably high failur...
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ISBN:
(纸本)9783037855065
Accurate life prediction of aircraft engine components is very critical because it has a direct impact on aircraft safety and on operators' profits. The engine bleed air system valves have considerably high failure rates when the engines are operated in desert conditions because of sand particles erosion and blockage. In this work, an Artificial Neural Network (ANN) model for the prediction of failure rate of the most important of these valves in Boeing 737 engines is developed and validated. A previously developed feed-forward back-propagation algorithm is implemented to train the ANN. The effects of changing the number of neurons in the input layer, the number of neurons in the hidden layer, the rate of learning, and the momentum constant are investigated. The model results are validated using comparisons with actual valves failure data from a local operator in Saudi Arabia, as well as comparisons with classical Weibull model results.
Electroencephalogram (EEG) is a tool used in the diagnosis of a common neurological disorder Epilepsy. Analysis of long recordings of EEG by visual inspection for epilepsy is quite a tedious process. In this paper, we...
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ISBN:
(纸本)9781479925834
Electroencephalogram (EEG) is a tool used in the diagnosis of a common neurological disorder Epilepsy. Analysis of long recordings of EEG by visual inspection for epilepsy is quite a tedious process. In this paper, we present an approach for automated epileptic seizure detection by employing Multi layer Perceptron Neural Network (MLPNN) classifier. Independent Component Analysis (ICA), a statistical tool is used for extraction of features. The ascertained signals are trained under supervision by making use of memory efficient and fast Scaled Conjugate Gradient (SCG) backpropagation algorithm. The data set is taken from a publicly available EEG database. The MLPNN is designed with the tan-sigmoid transfer function in the hidden layer and output layer. The network is analyzed using performance metric like Mean Square Error and confusion matrix. The best classification accuracy is about 100% for the overall dataset. This indicates the proposed method has potential in designing a new intelligent EEG-based assistance diagnosis system for early detection of the electroencephalographic changes.
This paper presents an investigation on the performance of an active suspension system. After discussing a 4-DOF car suspension model, dynamic simulation of the system with an existing nonlinear classic controller cal...
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This paper presents an investigation on the performance of an active suspension system. After discussing a 4-DOF car suspension model, dynamic simulation of the system with an existing nonlinear classic controller called back-stepping method has been presented which had been previously introduced by Huang and Lin (2004). The model-based identification of the system is then preformed by the aid of the feed forward neural networks which are finally used for the corresponding fault detection process. Simulation results show that the proposed identification and fault detection approach is highly effective in evaluating the performance of an active suspension system.
The associative property of artificial neural networks (ANNs) and their inherent ability to "learn" and "recognize" highly non-linear and complex relationships finds them ideally suited to a wide r...
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The associative property of artificial neural networks (ANNs) and their inherent ability to "learn" and "recognize" highly non-linear and complex relationships finds them ideally suited to a wide range of applications in chemical engineering. Dynamic Modeling and Control of Chemical Process Systems and Fault Diagnosis are the two significant applications of ANNs that have been explored so far with success. This paper deals with the potential applications of ANNs in thermodynamics - particularly, the prediction:estimation of vapor-liquid equilibrium (VLE) data. The prediction of VLE data by conventional thermodynamic methods is tedious and requires determination of "constants" which is arbitrary in many ways. Also, the use of conventional thermodynamics for predicting VLE data for highly polar substances introduces a large number of inaccuracies. The possibility of applying ANNs for VLE data prediction/estimation has been explored using the backpropagation algorithm. The methane-ethane and ammonia-water systems have been studied and the VLE predictions have been found to be accurate to within +/- 1%. Preliminary results confirm exciting possibilities of ANNs for applications to thermodynamics of mixtures. Advantages and limitations of this application are also discussed. An heuristic approach to reduce the trial and error process for selecting the "optimum" net architecture is discussed. (C) 1999 Elsevier Science Ltd. All rights reserved.
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