Aeroengine Fault Diagnosis Using Optimized Elman Neural Network

Pi, Jun; Huang, Jian; Ma, Long

doi:10.1155/2017/9726529

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…As Figure 3 demonstrated, it encompasses four layers, namely the input layer, the hidden layer, the context links layer, and the output layer [21,22].…”

Section: Theory Behind Elman Neural Networkmentioning

confidence: 99%

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A Deep Learning Algorithm for Solar Radiation Time Series Forecasting: A Case Study of El Kelaa Des Sraghna City

Jallal¹,

Yassini²,

Chabaa³

et al. 2020

RIA

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Nowadays, the studies that address solar radiation (SR) forecasting tend to focus on the implementation of conventional techniques. This provides good results, but researchers should focus on the creation of new methodologies that help us in going further and boost the prediction accuracy of SR data. The prime aim of this research study is to propose an efficient deep learning (DL) algorithm that can handle nonlinearities and dynamic behaviors of the meteorological data, and generate accurate real-time forecasting of hourly global solar radiation (GSR) data of the city of El Kelaa des Sraghna (32°2’53”N 7°24’30”W), Morocco. The proposed DL algorithm integrates the dynamic model named Elman neural network with a new input configuration-based autoregressive process in order to learn from the seasonal patterns of the historical SR measurements, and the actual measurements of air temperature. The attained performance proves the reliability and the accuracy of the proposed model to forecast the hourly GSR time series in case of missing values detection or pyranometer damage. Hence, electrical power engineers can adopt this forecasting tool to improve the integration of solar power resources into the power grid system.

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“…As Figure 3 demonstrated, it encompasses four layers, namely the input layer, the hidden layer, the context links layer, and the output layer [21,22].…”

Section: Theory Behind Elman Neural Networkmentioning

confidence: 99%

“…The ENN model is supposed to have inputs' features { 1 , 2 , … , } , h neurons in the hidden layer { 1 , 2 , … , ℎ } , a context links layer { 1 , 2 , … , } with dimension, and n outputs' signals { 1 , 2 , … , } [19,20,22]. The output signal of the kth context link is defined as:…”

Section: Theory Behind Elman Neural Networkmentioning

confidence: 99%

A Deep Learning Algorithm for Solar Radiation Time Series Forecasting: A Case Study of El Kelaa Des Sraghna City

Jallal¹,

Yassini²,

Chabaa³

et al. 2020

RIA

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show abstract

“…each input layer output is an input to each hidden layer cell. Either the output of the hidden layer is an input to the output layer as well as the input layer, figure (2) shown the Elman Neural Network structure [20]. There are many color systems that can represent the color digital image, and each of these systems have many characteristics that can be used in certain applications, the color system (RGB) is the most color systems used [21][22].…”

Section: Elman Neural Networkmentioning

confidence: 99%

Improving face recognition by elman neural network using curvelet transform and HSI color space

Abdullah¹,

Abed²,

Al_Barazanchi³

2019

PEN

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In this paper, a suggested algorithm was proposed to increase the efficiency of the Elman neural algorithm in face recognition. The proposed algorithm was studied on the images of 20 students from the Department of Computer Science, Tikrit University. First step creates dataset of faces, second step convert color space to HSI and using saturation layer, image decomposition using curvelet transform, feature extraction using Principle component analysis, and final step face recognition using Elman neural network. after applying proposed algorithm, the rate of face recognition 94%.

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“…18 Pi et al designed a new Elman neural network (ENN) optimized by quantum-behaved adaptive particle swarm optimization (QAPSO) to achieve accurate results in aeroengine fault diagnosis. 19 These studies with classic and typical machine learning methods have demonstrated that the performance of data-based fault diagnosis can be high. However, the widely used feature extraction method may be prone to confusing the fault characteristics due to different fault modes.…”

Section: Introductionmentioning

confidence: 99%

Method of aeroengine fault diagnosis based on a Fingerprint map

Gou

Pan³

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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The electrification and intelligentization trends of aeroengine systems present new challenges for the diagnostics of traditionally centralized control architectures. Scientific and practical fault diagnosis is of great significance for reducing aeroengine failures, improving operation efficiency, and ensuring safety. To realize accurate fault diagnosis, an aeroengine fault diagnosis method based on an improved fingerprint map is proposed in this paper. Based on aeroengine data analysis, ISTOA-SVM diagnostic model that embeds improved fingerprint map data as a priori knowledge is adopted to identify the fault sets. Data based on the statistics of the fingerprint map are processed with a ratio coefficient, and the feature parameters are appended through Floyd-principal component analysis (Floyd-PCA) to increase the identification data dimension. On the other hand, to improve the diagnostic performance of the classifier, the sooty terns optimization algorithm algorithm combined with chaotic maps, a variable helix mechanism and a separating operator is designed, and the ISTOA-SVM is constructed. The fault diagnosis method was validated by using a real-world dataset of aeroengines. Comparison experiments show that the proposed method is superior in performance over the state-of-the-art approaches in diagnostic precision under the constraint of limited data. The ISTOA-SVM addresses the issue that similar features may be confused in traditional fingerprint maps. The results are presented through 5 practical cases. The study demonstrates that the proposed method achieves superior performance and can be applied to aeroengine fault diagnosis.

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Aeroengine Fault Diagnosis Using Optimized Elman Neural Network

Cited by 8 publications

References 13 publications

A Deep Learning Algorithm for Solar Radiation Time Series Forecasting: A Case Study of El Kelaa Des Sraghna City

A Deep Learning Algorithm for Solar Radiation Time Series Forecasting: A Case Study of El Kelaa Des Sraghna City

Improving face recognition by elman neural network using curvelet transform and HSI color space

Method of aeroengine fault diagnosis based on a Fingerprint map

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