A Novel Intelligent Method for Fault Diagnosis of Steam Turbines Based on T-SNE and XGBoost

Liu, Zhiguo; Zhang, Lijun; Wang, Xizhe

doi:10.3390/a16020098

Cited by 14 publications

(7 citation statements)

References 31 publications

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“…Its most appealing features are its execution speed and model performance. Many writers [ 49 , 50 ] proved that XGBoost outperforms other ensemble-based approaches such as FFT in terms of vibration characteristics.…”

Section: Methodsmentioning

confidence: 99%

A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft

Wisal

2023

Sensors

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Rotor unbalance is the most common cause of vibration in industrial machines. The unbalance can result in efficiency losses and decreased lifetime of bearings and other components, leading to system failure and significant safety risk. Many complex analytical techniques and specific classifiers algorithms have been developed to study rotor imbalance. The classifier algorithms, though simple to use, lack the flexibility to be used efficiently for both low and high numbers of classes. Therefore, a robust multiclass prediction algorithm is needed to efficiently classify the rotor imbalance problem during runtime and avoid the problem’s escalation to failure. In this work, a new deep learning (DL) algorithm was developed for detecting the unbalance of a rotating shaft for both binary and multiclass identification. The model was developed by utilizing the depth and efficacy of ResNet and the feature extraction property of Convolutional Neural Network (CNN). The new algorithm outperforms both ResNet and CNN. Accelerometer data collected by a vibration sensor were used to train the algorithm. This time series data were preprocessed to extract important vibration signatures such as Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT). STFT, being a feature-rich characteristic, performs better on our model. Two types of analyses were carried out: (i) balanced vs. unbalanced case detection (two output classes) and (ii) the level of unbalance detection (five output classes). The developed model gave a testing accuracy of 99.23% for the two-class classification and 95.15% for the multilevel unbalance classification. The results suggest that the proposed deep learning framework is robust for both binary and multiclass classification problems. This study provides a robust framework for detecting shaft unbalance of rotating machinery and can serve as a real-time fault detection mechanism in industrial applications.

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Section: Methodsmentioning

confidence: 99%

A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft

Wisal

2023

Sensors

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“…This algorithm effectively reduces the dimensionality of the features and generates a clear image of their distribution. Figure 4 illustrates the distribution map of the invariant portion of potential features across domains obtained after dimensionality reduction of the bottleneck layer outputs of migration tasks 0-1 in the JNU dataset using the t-SNE algorithm [42]. The graph uses identical colours for identical labels.…”

Section: Visual Analysismentioning

confidence: 99%

Domain adaptation with domain specific information and feature disentanglement for bearing fault diagnosis

Xie,

Xia,

Zhang

2024

Meas. Sci. Technol.

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Collecting bearing fault signals from several rotating machines or under varied operating conditions often results in data distribution offset. Furthermore, the newly obtained data is typically unlabeled. When intricate confounding aspects of data distribution across several domains are present, achieving desired outcomes through straightforward transfer learning techniques becomes challenging. This research presents a new framework, the Domain-Specific Invariant Adversarial Network, which combines the principles of domain-invariant representation learning and feature de-entanglement to solve the challenge at hand. This framework uses domain-specific information as an auxiliary training tool and employs the data generation process to transfer labelled source domain data to the target domain. The aim of this approach is to uncover potential information components and improve the model's ability to acknowledge patterns. The study showcases the method's strong diagnostic capability by conducting experimental analysis on four fault datasets.

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“…Te distance between the new data points and the center of the hypersphere obtained through the network model was utilized to determine whether the data were anomalous. Additionally, the distance can provide insights into the degree of abnormality in data [31][32][33][34][35][36].…”

Section: Deep Support Vector Data Description Network Modelmentioning

confidence: 99%

Deep Multiscale Soft‐Threshold Support Vector Data Description for Enhanced Heavy‐Duty Gas Turbine Generator Sets’ Anomaly Detection

Kun,

Hongren,

Xin

et al. 2024

Shock and Vibration

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This paper introduces an innovative approach, Deep Multiscale Soft-Threshold Support Vector Data Description (DMS-SVDD), designed for the detection of anomalies and prediction of faults in heavy-duty gas turbine generator sets (GENSETs). The model combines a support vector data description (SVDD) with a deep autoencoder backbone network framework, integrating a multiscale convolutional neural network (M) and soft-threshold activation network (S) into the Deep-SVDD framework. In comparison with conventional methods, such as One-Class Support Vector Machine (OCSVM) and autoencoder (AE), DMS-SVDD demonstrates improvements in accuracy (by 22.94%), recall (by 32%), F1 score (by 12.02%), and smoothness (by 39.15%). The model excels particularly in feature extraction, denoising, and early fault detection, offering a proactive strategy for maintenance. Furthermore, the DMS-SVDD demonstrated enhanced training efficiency with a reduction in the convergence rounds by 66% and overall training times by 34.13%. The study concludes that DMS-SVDD presents a robust and efficient solution for gas turbine anomaly detection, with practical advantages for decision support in turbine maintenance. Future research could explore additional refinements and applications of the DMS-SVDD model across diverse industrial contexts.

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A Novel Intelligent Method for Fault Diagnosis of Steam Turbines Based on T-SNE and XGBoost

Cited by 14 publications

References 31 publications

A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft

A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft

Domain adaptation with domain specific information and feature disentanglement for bearing fault diagnosis

Deep Multiscale Soft‐Threshold Support Vector Data Description for Enhanced Heavy‐Duty Gas Turbine Generator Sets’ Anomaly Detection

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