Gearbox Fault Diagnosis Based on Gramian Angular Field and CSKD-ResNeXt

Liu, Yanlin; Dou, Shuihai; Du, Yuyue; Wang, Zhaohua

doi:10.3390/electronics12112475

Cited by 9 publications

(3 citation statements)

References 58 publications

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“…The EarlyStopping callback from the Keras library was employed in this study to implement the early stopping technique [106]. During the training process, this callback continuously monitors the validation loss and halts the training if the loss does not improve for a predefined number of epochs.…”

Section: Early Stoppingmentioning

confidence: 99%

Advancements in Household Load Forecasting: Deep Learning Model with Hyperparameter Optimization

Al-Jamimi,

BinMakhashen,

Worku

et al. 2023

Electronics

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Accurate load forecasting is of utmost importance for modern power generation facilities to effectively meet the ever-changing electricity demand. Predicting electricity consumption is a complex task due to the numerous factors that influence energy usage. Consequently, electricity utilities and government agencies are constantly in search of advanced machine learning solutions to improve load forecasting. Recently, deep learning (DL) has gained prominence as a significant area of interest in prediction efforts. This paper introduces an innovative approach to electric load forecasting, leveraging advanced DL techniques and making significant contributions to the field of energy management. The hybrid predictive model has been specifically designed to enhance the accuracy of multivariate time series forecasting for electricity consumption within the energy sector. In our comparative analysis, we evaluated the performance of our proposed model against ML-based and state-of-the-art DL models, using a dataset obtained from the Distribution Network Station located in Tetouan City, Morocco. Notably, the proposed model surpassed its counterparts, demonstrating the lowest error in terms of the Root-Mean-Square Error (RMSE). This outcome underscores its superior predictive capability and underscores its potential to advance the accuracy of electricity consumption forecasting.

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Section: Early Stoppingmentioning

confidence: 99%

Advancements in Household Load Forecasting: Deep Learning Model with Hyperparameter Optimization

Al-Jamimi,

BinMakhashen,

Worku

et al. 2023

Electronics

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show abstract

“…In order to improve the recognition accuracy of the model, the traditional convolutional neural network needs to deepen or widen the network, but with the deepening of the network layers, there will be gradient explosion or gradient disappearance, and network degradation will occur. As a variant of ResNet, the ResNeXt model adopts the idea of an inception network [16] and uses group convolution instead of traditional convolution to widen the network and reduce the number of training parameters of the model [17]. Because of its strong feature recognition ability, the ResNeXt model is widely used in the field of image recognition, such as: Yao Xiao et al [18] proposed an automatic insect recognition system based on SE-ResNeXt, which realized the visual display of insect recognition results and the digital storage of insect data.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Gearbox Fault Detection Method Based on Deep Residual Neural Network Algorithm

Wang,

Tao,

et al. 2023

Sensors

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Because of its long running time, complex working environment, and for other reasons, a gear is prone to failure, and early failure is difficult to detect by direct observation; therefore, fault diagnosis of gears is very necessary. Neural network algorithms have been widely used to realize gear fault diagnosis, but the structure of the neural network model is complicated, the training time is long and the model is not easy to converge. To solve the above problems and combine the advantages of the ResNeXt50 model in the extraction of image features, this paper proposes a gearbox fault detection method that integrates the convolutional block attention module (CBAM). Firstly, the CBAM is embedded in the ResNeXt50 network to enhance the extraction of image channels and spatial features. Secondly, the different time–frequency analysis method was compared and analyzed, and the method with the better effect was selected to convert the one-dimensional vibration signal in the open data set of the gearbox into a two-dimensional image, eliminating the influence of the redundant background noise, and took it as the input of the model for training. Finally, the accuracy and the average training time of the model were obtained by entering the test set into the model, and the results were compared with four other classical convolutional neural network models. The results show that the proposed method performs well both in fault identification accuracy and average training time under two working conditions, and it also provides some references for existing gear failure diagnosis research.

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“…Recent research has compared traditional 1D time-series data approaches with image encoding techniques, presenting findings that substantiate the superior efficacy of image encoding methods [ 17 , 18 ]. Representative image encoding techniques include recurrence plot (RP) [ 19 , 20 , 21 , 22 , 23 ], Gramian angular field (GAF) [ 14 , 24 , 25 , 26 , 27 ], Markov transition field (MTF) [ 28 , 29 , 30 ], spectrogram (SP) [ 31 , 32 ], and scalogram (SC) [ 33 , 34 ]. These image encoding techniques have recently been applied in research that converts time-series data from vibration and current signals, collected for diagnosing faults in robots and various machinery (such as bearings, gearboxes, rotating machinery, complex distribution networks, ventilation, and air conditioning systems), into images for various convolutional neural network (CNN) models.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Human Coexistence Robots Based on Convolutional Neural Networks Using Time-Series Data Generation and Image Encoding

Choi,

Park,

et al. 2023

Sensors

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This paper proposes fault diagnosis methods aimed at proactively preventing potential safety issues in robot systems, particularly human coexistence robots (HCRs) used in industrial environments. The data were collected from durability tests of the driving module for HCRs, gathering time-series vibration data until the module failed. In this study, to apply classification methods in the absence of post-failure data, the initial 50% of the collected data were designated as the normal section, and the data from the 10 h immediately preceding the failure were selected as the fault section. To generate additional data for the limited fault dataset, the Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) model was utilized and residual connections were added to the generator to maintain the basic structure while preventing the loss of key features of the data. Considering that the performance of image encoding techniques varies depending on the dataset type, this study applied and compared five image encoding methods and four CNN models to facilitate the selection of the most suitable algorithm. The time-series data were converted into image data using image encoding techniques including recurrence plot, Gramian angular field, Markov transition field, spectrogram, and scalogram. These images were then applied to CNN models, including VGGNet, GoogleNet, ResNet, and DenseNet, to calculate the accuracy of fault diagnosis and compare the performance of each model. The experimental results demonstrated significant improvements in diagnostic accuracy when employing the WGAN-GP model to generate fault data, and among the image encoding techniques and convolutional neural network models, spectrogram and DenseNet exhibited superior performance, respectively.

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Gearbox Fault Diagnosis Based on Gramian Angular Field and CSKD-ResNeXt

Cited by 9 publications

References 58 publications

Advancements in Household Load Forecasting: Deep Learning Model with Hyperparameter Optimization

Advancements in Household Load Forecasting: Deep Learning Model with Hyperparameter Optimization

Optimization of Gearbox Fault Detection Method Based on Deep Residual Neural Network Algorithm

Fault Diagnosis Method for Human Coexistence Robots Based on Convolutional Neural Networks Using Time-Series Data Generation and Image Encoding

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