Variable Dropout One-Dimensional CNN for Vibration-Based Shaft Unbalance Detection in Industrial Machinery

Yadav, Arihant; Rajagopalan, Sudhar; Purohit, Ashish; Singh, Jaskaran

doi:10.1007/s42417-022-00578-w

Cited by 5 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For rotor mass imbalance fault detection and diagnosis, Souza et al explored CNN for a predictive maintenance model with multiple faults [19]. Yadav et al studied CNN using experimental data to predict rotor for mass imbalance [20]. Xing et al used CNN to detect an aerodynamic imbalance in wind turbine blades [21].…”

Section: Introductionmentioning

confidence: 99%

“…on the target domain, the deep learning models suffer from the domain shift problem, as shown in figure 1. Many researchers have developed various methods to address the domain-shifting issue in different fields, such as natural language processing, object recognition, speech, timeserious, multi-modal, and feature learning [17][18][19][20]. Wilson and Cook [22] have summarised neural network-based unsupervised domain adaptation based on the method of adaptation such as domain mapping [DM], normalization [N], ensemble [En], target discriminative [TD], and domain-invariant feature learning [DI].…”

Section: Introductionmentioning

confidence: 99%

“…However, most researchers manually tuned the hyperparameters in rotor fault diagnosis. Rotor mass imbalance detection was explored by Yadav et al using CNN with manually tuned hyperparameters [20]. Xing et al [21] studied CNN for the aerodynamic imbalance of wind turbine blades.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Genetically optimised SMOTE-based adversarial discriminative domain adaptation for rotor fault diagnosis at variable operating conditions

Rajagopalan,

Purohit,

Singh

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

For safety, reliability, and uninterrupted output of gas turbines, aviation engines, power-generating equipment, pumps, gears, compressors etc., rotor mass imbalance must be detected and diagnosed to avoid catastrophic failure. Industry 4.0 relies on predictive digital maintenance and deep learning-based convolutional neural network (CNN), which predicts defects but fails if the operating conditions change. Research studies in various fields indicate that the domain shift issue occurs due to source and target samples being from different domains, which reduces prediction capability. Moreover, research studies are scarce in examining prediction capability under varying operating speeds for rotor mass imbalance. Hence, this research proposes the adversarial discriminative domain adaptation (ADDA) technique which predicts machine failures under various operational conditions. The efficacy of ADDA has been explored by introducing 1D-CNN as a source and a target encoder inside ADDA's architecture to take advantage of CNN's feature extraction capability. Further, this research effectively tackles CNN's inherent issues of overfitting and hyperparameters value selection. Furthermore, The real-world scenario has more healthy samples than fault condition samples, causing a multiclass imbalance in sample data, which affects the classification decision boundary and causes biased prediction. Hence, the proposed methodology first addresses the class imbalance through synthetic minority oversampling (SMOTE), then genetic algorithm optimizes 1D-CNN's hyperparameters, and the effective dropout layer positioning solves the overfitting. Finally, the deep learning-based SMOTE_ADDA_GO-1D-CNN decreases domain discrepancy with adversarial discriminative domain adaptation. The proposed methodology's efficacy has been explored through F1-Score, which is used as multiclass evaluation metrics, and it has been benchmarked against standard machine learning and deep learning algorithms. The test results of the proposed methodology surpassed all of them with maximum prediction accuracy. Thus, this study contributes to rotor mass imbalance detection and diagnosis for multiclass imbalanced data under varying operational conditions by successfully overcoming potential challenges during fault prediction.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Genetically optimised SMOTE-based adversarial discriminative domain adaptation for rotor fault diagnosis at variable operating conditions

Rajagopalan,

Purohit,

Singh

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Xing et al [20] effectively use the time-domain signal to deal with data imbalance in fault diagnosis by introducing a weighted central label optimization method in CNN. Yadav et al [21] used variable dropout rates in 1D-CNN to extract features from time-domain signals.…”

Section: Introductionmentioning

confidence: 99%

Deep transfer learning rolling bearing fault diagnosis method based on convolutional neural network feature fusion

Yu,

Fu,

Song

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Current deep-learning methods are often based on significantly large quantities of labeled fault data for supervised training. In practice, it is difficult to obtain samples of rolling bearing failures. In this paper, a transfer learning-based feature fusion convolutional neural network approach for bearing fault diagnosis is proposed. Specifically, the raw vibration signal features and the corresponding time-frequency image features of the input data are extracted by a one-dimensional convolutional neural network and a pre-trained ConvNeXt, respectively, and connected by a feature fusion strategy. Then, the fine-tuning method based on transfer learning can effectively reduce the reliance on labeled samples in the target domain. A wide convolution kernel is introduced in the time-domain signal feature extraction to increase the receptive field, which is combined with the channel attention mechanism to further optimize the feature quality. Finally, two common bearing datasets are utilized for fault diagnosis experiments. The experimental results show that the proposed model achieves an average accuracy of more than 98.63% in both cross-working conditions and cross-device diagnosis tasks. Meanwhile, anti-noise experiments and ablation experiments further validate the accuracy and robustness of the proposed method.

show abstract

Fault diagnosis based on feature enhancement and spatial adjacent region dropout strategy

Zhao,

Zhou,

et al. 2023

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

Variable Dropout One-Dimensional CNN for Vibration-Based Shaft Unbalance Detection in Industrial Machinery

Cited by 5 publications

References 37 publications

Genetically optimised SMOTE-based adversarial discriminative domain adaptation for rotor fault diagnosis at variable operating conditions

Genetically optimised SMOTE-based adversarial discriminative domain adaptation for rotor fault diagnosis at variable operating conditions

Deep transfer learning rolling bearing fault diagnosis method based on convolutional neural network feature fusion

Fault diagnosis based on feature enhancement and spatial adjacent region dropout strategy

Contact Info

Product

Resources

About