Multi-Auto Associative Neural Network based sensor validation and estimation for aero-engine

Shah, Brijeshkumar; Sarvajith, M.; Sankar, Balaji; Thennavarajan, S

doi:10.1109/autest.2013.6645076

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…The proposed auto-associative neural network architecture diagram for lost inform restoration in the TV3-117 turboshaft engine sensor failure event [9][10][11]32,33]. The key point in choosing the ANN structure is determining the optimal number of neurons in the hidden layer [9][10][11]34]. For example, Figure 2 shows the error dependence in information restoration by the ANN [32][33][34] in the failure event of one of the sensors (for example, a TV3-117 TE gas generator rotor r.p.m.…”

Section: Methodsmentioning

confidence: 99%

“…The key point in choosing the ANN structure is determining the optimal number of neurons in the hidden layer [9][10][11]34]. For example, Figure 2 shows the error dependence in information restoration by the ANN [32][33][34] in the failure event of one of the sensors (for example, a TV3-117 TE gas generator rotor r.p.m. [35,36]) on the neuron number in the ANN shaft, calculated according to the expression:…”

Section: Methodsmentioning

confidence: 99%

“…where x is input data, enc(x) is hidden layer output (encoded data), dec(enc(x)) is reconstructed data, and n is the example number in the training set. The key point in choosing the ANN structure is determining the optimal number of neurons in the hidden layer [9][10][11]34]. For example, Figure 2 shows the error dependence in information restoration by the ANN [32][33][34] in the failure event of one of the sensors (for example, a TV3-117 TE gas generator rotor r.p.m.…”

Section: Methodsmentioning

confidence: 99%

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The Method of Restoring Lost Information from Sensors Based on Auto-Associative Neural Networks

Vladov,

Yakovliev,

Vysotska

et al. 2024

ASI

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The research aims to develop a neural network-based lost information restoration method when the complex nonlinear technical object (using the example of helicopter turboshaft engines) sensors fail during operation. The basis of the research is an auto-associative neural network (autoencoder), which makes it possible to restore lost information due to the sensor failure with an accuracy of more than 99%. An auto-associative neural network (autoencoder)-modified training method is proposed. It uses regularization coefficients that consist of the loss function to create a more stable and common model. It works well on the training sample of data and can produce good results on new data. Also, it reduces its overtraining risk when it adapts too much to the training data sample and loses its ability to generalize new data. This is especially important for small amounts of data or complex models. It has been determined based on the computational experiment results (the example of the TV3-117 turboshaft engine) that lost information restoration based on an auto-associative neural network provides a data restoring error of no more than 0.45% in the case of single failures and no more than 0.6% in case of double failures of the engine parameter registration sensor event.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The Method of Restoring Lost Information from Sensors Based on Auto-Associative Neural Networks

Vladov,

Yakovliev,

Vysotska

et al. 2024

ASI

View full text Add to dashboard Cite

show abstract

“…For instance, authors in [8][9][10] proposed Kalman filter-based schemes, whilst others, as in Refs. [11,12], used AANNs. In addition, a comprehensive explanation of more than ten different techniques had been presented by Ganguli [13].…”

Section: Introductionmentioning

confidence: 99%

Gas path fault diagnostics using a hybrid intelligent method for industrial gas turbine engines

Amare

Aklilu

Gilani

2018

J Braz. Soc. Mech. Sci. Eng.

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There are many challenges against an accurate gas turbine fault diagnostics, such as the nonlinearity of the engine health, the measurement uncertainty, and the occurrence of simultaneous faults. The conventional methods have limitations in effectively handling these challenges. In this paper, a hybrid intelligent technique is devised by integrating an autoassociative neural network (AANN), nested machine learning (ML) classifiers, and a multilayer perceptron (MLP). The AANN module is used as a data preprocessor to reduce measurement noise and extract the important features for visualisation and fault diagnostics. The features are extracted from the bottleneck layer output values based on the concept of the nonlinear principal component analysis (NLPCA). The nested classifier modules are then used in such a manner that fault and no-fault conditions, component and sensor faults, and different component faults are distinguished hierarchically. As part of the classification, evaluation of the fault classification performance of five widely used ML techniques aiming to identify alternative approaches is undertaken. In the end, the MLP approximator is utilised to estimate the magnitude of the isolated component faults in terms of flow capacity and isentropic efficiency indices. The developed system was implemented to diagnose up to three simultaneous faults in a two-shaft industrial gas turbine engine. Its robustness towards the measurement uncertainty was also evaluated based on Gaussian noise corrupted data. The test results show the derivable benefits of integrating two or more methods in engine diagnostics on the basis of offsetting the weakness of the one with the strength of another.

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Sensor Fault Detection for Superconducting Cable Monitoring System based on Swarm Optimization BP Neural Network

Xihua¹,

Xize²,

Zhang³

et al. 2021

2021 33rd Chinese Control and Decision Conference (CCDC)

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Multi-Auto Associative Neural Network based sensor validation and estimation for aero-engine

Cited by 5 publications

References 13 publications

The Method of Restoring Lost Information from Sensors Based on Auto-Associative Neural Networks

The Method of Restoring Lost Information from Sensors Based on Auto-Associative Neural Networks

Gas path fault diagnostics using a hybrid intelligent method for industrial gas turbine engines

Sensor Fault Detection for Superconducting Cable Monitoring System based on Swarm Optimization BP Neural Network

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