A Fault Diagnosis Method based on Improved Synthetic Minority Oversampling Technique and SVM for Unbalanced Data

Han, Minghong; Wu, Yaman; Huang, Yunfeng; Wang, Yumin

doi:10.1088/1757-899x/1043/5/052034

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…In the past decades, the rapid development of global information technology has led to the emergence of powerful computers, data collection devices and storage devices. With these devices, a large amount of data information can be collected for transaction management, information retrieval and data analysis [1]. Although the amount of data collected is very large, the data useful to people is often very limited, usually only a small part of the total data.…”

Section: Introductionmentioning

confidence: 99%

Mining algorithm of accumulation sequence of unbalanced data based on probability matrix decomposition

Mou

Zhang

2023

PLoS ONE

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Due to the inherent characteristics of accumulation sequence of unbalanced data, the mining results of this kind of data are often affected by a large number of categories, resulting in the decline of mining performance. To solve the above problems, the performance of data cumulative sequence mining is optimized. The algorithm for mining cumulative sequence of unbalanced data based on probability matrix decomposition is studied. The natural nearest neighbor of a few samples in the unbalanced data cumulative sequence is determined, and the few samples in the unbalanced data cumulative sequence are clustered according to the natural nearest neighbor relationship. In the same cluster, new samples are generated from the core points of dense regions and non core points of sparse regions, and then new samples are added to the original data accumulation sequence to balance the data accumulation sequence. The probability matrix decomposition method is used to generate two random number matrices with Gaussian distribution in the cumulative sequence of balanced data, and the linear combination of low dimensional eigenvectors is used to explain the preference of specific users for the data sequence; At the same time, from a global perspective, the AdaBoost idea is used to adaptively adjust the sample weight and optimize the probability matrix decomposition algorithm. Experimental results show that the algorithm can effectively generate new samples, improve the imbalance of data accumulation sequence, and obtain more accurate mining results. Optimizing global errors as well as more efficient single-sample errors. When the decomposition dimension is 5, the minimum RMSE is obtained. The proposed algorithm has good classification performance for the cumulative sequence of balanced data, and the average ranking of index F value, G mean and AUC is the best.

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Section: Introductionmentioning

confidence: 99%

Mining algorithm of accumulation sequence of unbalanced data based on probability matrix decomposition

Mou

Zhang

2023

PLoS ONE

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A systematic review of data augmentation methods for intelligent fault diagnosis of rotating machinery under limited data conditions

Ju,

Chen,

Qiang

et al. 2024

Meas. Sci. Technol.

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In recent years, research on the intelligent fault diagnosis of rotating machinery has made remarkable progress, bringing considerable economic benefits to industrial production. However, in the industrial environment, the accuracy and stability of the diagnostic model face severe challenges due to the extremely limited fault data. Data augmentation methods have the capability to increase both the quantity and diversity of data without altering the key characteristics of the original data, which is particularly important for the development of intelligent fault diagnosis of rotating machinery under limited data conditions (IFD-RM-LDC). Despite the abundant achievements in research on data augmentation methods, there is a lack of systematic reviews and clear future development directions. Therefore, this paper systematically reviews and discusses data augmentation methods for IFD-RM-LDC. Firstly, existing data augmentation methods are categorized into three groups: synthetic minority over-sampling technique (SMOTE)-based methods, generative model-based methods, and data transformation-based methods. Then, these three methods are introduced in detail and discussed in depth: SMOTE-based methods synthesize new samples through a spatial interpolation strategy; generative model-based methods generate new samples according to the distribution characteristics of existing samples; data transformation-based methods generate new samples through a series of transformation operations. Finally, the challenges faced by current data augmentation methods, including their limitations in generalization, real-time performance, and interpretability, as well as the absence of robust evaluation metrics for generated samples, have been summarized, and potential solutions to address these issues have been explored.

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A review of bearing fault diagnosis based on weakly supervised learning

Zhao,

Cen,

et al. 2023

2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

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A Fault Diagnosis Method based on Improved Synthetic Minority Oversampling Technique and SVM for Unbalanced Data

Cited by 5 publications

References 6 publications

Mining algorithm of accumulation sequence of unbalanced data based on probability matrix decomposition

Mining algorithm of accumulation sequence of unbalanced data based on probability matrix decomposition

A systematic review of data augmentation methods for intelligent fault diagnosis of rotating machinery under limited data conditions

A review of bearing fault diagnosis based on weakly supervised learning

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