Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers

Bai, Mingliang; Yang, Xusheng; Liu, Jinfu; Liu, Jiao; Yu, Daren

doi:10.1016/j.apenergy.2021.117509

Cited by 58 publications

(13 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be generally deduced that the dynamic ANN, even if recognized as a shallow ANN with a single hidden layer, is still a leading choice for the modeling and simulation of GTs, which have negligible simulation errors and a high simulation performance of the variation trends of GT power plants. For other different successful applications of CNN and NARX ANNs, rather than time-based simulations, the reader may refer to the references [24][25][26][27][28].…”

Section: Time-based Simulation Results and Discussionmentioning

confidence: 99%

“…Having the features of the data on a similar scale makes all input and output variables of a GT power plant equally important and easier to compile by the NARX and CNN model [21]. The convolutional neural network (CNN) is one of the most popular deep neural networks [24]. CNN usually comprises various layers, such as convolutional layers, pooling layers, fully connected layers, i.e., dense layers, etc.…”

Section: The Deep Learning Convolutional Neural Network (Cnn) Model Setupmentioning

confidence: 99%

“…f (c.) represents the non-linear activation function. CNNs usually utilize the rectified linear unit (ReLU), i.e., f (x) = max(0, x), as an activation function [24]. Pooling layers are paramount for CNNs.…”

Section: The Deep Learning Convolutional Neural Network (Cnn) Model Setupmentioning

confidence: 99%

See 2 more Smart Citations

Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning

2022

View full text Add to dashboard Cite

Accurate simulations of gas turbines’ dynamic performance are essential for improvements in their practical performance and advancements in sustainable energy production. This paper presents models with extremely accurate simulations for a real dual-fuel gas turbine using two state-of-the-art techniques of neural networks: the dynamic neural network and deep neural network. The dynamic neural network has been realized via a nonlinear autoregressive network with exogenous inputs (NARX) artificial neural network (ANN), and the deep neural network has been based on a convolutional neural network (CNN). The outputs selected for simulations are: the output power, the exhausted temperature and the turbine speed or system frequency, whereas the inputs are the natural gas (NG) control valve, the pilot gas control valve and the compressor variables. The data-sets have been prepared in three essential formats for the training and validation of the networks: normalized data, standardized data and SI units’ data. Rigorous effort has been carried out for wide-range trials regarding tweaking the network structures and hyper-parameters, which leads to highly satisfactory results for both models (overall, the minimum recorded MSE in the training of the MISO NARX was 6.2626 × 10−9 and the maximum MSE that was recorded for the MISO CNN was 2.9210 × 10−4, for more than 15 h of GT operation). The results have shown a comparable satisfactory performance for both dynamic NARX ANN and the CNN with a slight superiority of NARX. It can be newly argued that the dynamic ANN is better than the deep learning ANN for the time-based performance simulation of gas turbines (GTs).

show abstract

Section: Time-based Simulation Results and Discussionmentioning

confidence: 99%

Section: The Deep Learning Convolutional Neural Network (Cnn) Model Setupmentioning

confidence: 99%

See 1 more Smart Citation

Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning

2022

View full text Add to dashboard Cite

show abstract

“…Afterwards, Yang et al provided a specific and comprehensive introduction to transfer learning [35]. In 2018, Tan et al put forward the viewpoint of deep transfer learning [36], and divided deep transfer learning into (I) instance-based deep transfer learning [37,38], (II) mapping-based deep transfer learning [39,40], (III) network-based deep transfer learning [41,42], and (IV) adversarialbased deep transfer learning [43,44], which resulted in a significantly positive effect on many domains that are difficult to improve because of insufficient training data. In this research, as it was difficult to obtain massive real turbulent images with ground truth sharp references, the deep transfer learning framework was the focus of our attention.…”

Section: Transfer Learningmentioning

confidence: 99%

Blind Restoration of Images Distorted by Atmospheric Turbulence Based on Deep Transfer Learning

Guo

Qing

et al. 2022

Photonics

View full text Add to dashboard Cite

Removing space-time varying blur and geometric distortions simultaneously from an image is a challenging task. Recent methods (including physical-based methods or learning-based methods) commonly default the turbulence-degraded operator as a fixed convolution operator. Obviously, the assumption does not hold in practice. According to the situation that the real turbulence distorted operator has double uncertainty in space and time dimensions, this paper reports a novel deep transfer learning (DTL) network framework to address this problem. Concretely, the training process of the proposed approach contains two stages. In the first stage, the GoPro Dataset was used to pre-train the Network D1 and freeze the bottom weight parameters of the model; in the second stage, a small amount of the Hot-Air Dataset was employed for finetuning the last two layers of the network. Furthermore, residual fast Fourier transform with convolution block (Res FFT-Conv Block) was introduced to integrate both low-frequency and high-frequency residual information. Subsequently, extensive experiments were carried out with multiple real-world degraded datasets by implementing the proposed method and four existing state-of-the-art methods. In contrast, the proposed method demonstrates a significant improvement over the four reported methods in terms of alleviating the blur and distortions, as well as improving the visual quality.

show abstract

“…This with the aim of developing predictive models for the detection and isolation of faults affecting the gas turbine studied. Other applications of artificial neural networks techniques have been developed by Dengji Zhou et al in [13] and Mingliang Bai et al in [20] for several investigations in the detection of failures of gas turbines, in this sense Morteza Montazeri Gh et al in [23][24] worked on the concept of fuzzy logic type 2, for the learning of the characteristic maps of the defects using a neural model with automatic growth applied to gas turbines. Also, Boulanouar Saadat et al in [8] studied the estimation of uptime in gas turbines using a prognostic modeling approach guided by studied turbine operating data and Tomas Olsson et al in [41] tested turbine operating data to predict long-term degradation.…”

Section: Introductionmentioning

confidence: 99%

Gas turbine reliability estimation to reduce the risk of failure occurrence with a comparative study between the two-parameter Weibull distribution and a new modified Weibull distribution

Zohair¹,

Hafaifa²,

Iratni³

et al. 2022

Diagnostyka

View full text Add to dashboard Cite

Responding to the needs of quality and robustness of analysis and management of degradation of equipment, to increase their life cycle and to expand these facilities to become more and more sophisticated and agronomic. This work proposes a contribution to increase the survival of a gas turbine, installed in a gascompression plant, with a comparative study between the two-parameter Weibull distribution. A new modified Weibull distribution was proposed also to reduce the risk of occurrence of failure in this rotating machine. A Statistical analysis and validation on the synthesis of turbine's reliability data and failures were considered, with a particular focus on the use of this data to increase the availability of this type of machine. So, developing a maintenance plan based on their reliability indices for scheduled inspections

show abstract

Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers

Cited by 58 publications

References 49 publications

Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning

Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning

Blind Restoration of Images Distorted by Atmospheric Turbulence Based on Deep Transfer Learning

Gas turbine reliability estimation to reduce the risk of failure occurrence with a comparative study between the two-parameter Weibull distribution and a new modified Weibull distribution

Contact Info

Product

Resources

About