Study of Fault Diagnosis for Rolling Bearing Based on Clustering Algorithms

Zhou, Qianwen; Lv, Yaqiong; Tu, Liangping; Wang, Miao; Li, Shijie

doi:10.1109/iccre49379.2020.9096469

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…SSC. The algorithm proposed in this manuscript incorporates weighted coefficients into SSC, as shown in equation (15). When the disparity between the two vectors in the input of the weighted-SSC algorithm is greater, the computed value of the Gaussian function decreases.…”

Section: The Impact Of Weighted Coefficients On Weighted-mentioning

confidence: 99%

“…The K-means algorithm is an iterative clustering analysis algorithm that assigns each object to the nearest clustering center. Each time a new sample is assigned, the cluster center is recalculated based on the existing objects in the cluster [15]. References [16,17] have pointed out that the randomness of selecting the initial clustering center in traditional K-means algorithm can lead to the algorithm falling into a local optimal solution, so they enhanced K-means clustering by incorporating ant colony optimization (ACO) and Gaussian mixture model.…”

Section: Introductionmentioning

confidence: 99%

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Fault diagnosis method for bogie bearings of high-speed trains based on weighted-sparse subspace clustering

Yang,

Liu,

Chen

et al. 2023

Meas. Sci. Technol.

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The bogie bearings are crucial components of the high-speed train transmission system, and any failure of them can severely impact the normal functioning of the vehicle. Nowadays, sparse subspace clustering (SSC) is an effective technology for diagnosing mechanical system faults. However, SSC is easily affected by noise. To address this issue, we introduce the weighted-SSC algorithm, which incorporates weighting coefficients to enhance the connections between similar sample points. Our approach involves extracting the fault characteristic parameters of the vibration signal through Wavelet Packet Transform (WPT) and Singular Value Decomposition (SVD). Subsequently, these parameters are employed to construct corresponding weighted-sparse subspaces. We also examined the selection of a hyperparameter in the weighted coefficients. The resultant sparse representation vectors are then harnessed for the purpose of transductive simi-supervised learning clustering and diagnosing the specific type of fault. To validate the effectiveness of our proposed method, we designed and built a bearing experimental platform. We compare our method with existing clustering algorithms, including K-means, SSC, Dimension Reduction Graph (DRG)-based SSC, and Ant Colony Optimization (ACO)-K-means clustering algorithms. The results demonstrate that our proposed weighted-SSC method achieves higher accuracy in fault diagnosis than the other clustering algorithms.

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Section: The Impact Of Weighted Coefficients On Weighted-mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method for bogie bearings of high-speed trains based on weighted-sparse subspace clustering

Yang,

Liu,

Chen

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…When the rolling bearing fail, which are key components of rotating equipment, a series of vibration and shock signals will be generated. About 50% of faults are characterized using vibration [5], so collecting vibration signals can directly reflect the operating status of mechanical equipment. Feature extraction is essential for fault diagnosis, and the sensitivity of features determines the accuracy of the final diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…This method extracts nine time-domain performance degradation features of rolling bearing vibration signals, and uses PCA to fuse multi-dimensional features and characterize the working state of rolling bearings. Liang et al [5] first extracted the time-domain features of diagnostic signals. Based on this, they proposed a combination of kmeans clustering and k-nearest neighbor for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Weighted K-NN Classification Method of Bearings Fault Diagnosis With Multi-Dimensional Sensitive Features

Wang

Wei

et al. 2021

IEEE Access

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Research on the intelligent fault diagnosis method of rolling bearing based on laboratory data has made some achievements. However, due to the change of working conditions and the lack of historical data of the same equipment in the actual diagnosis, some methods mostly have problems such as poor generalization. Model training and verification data are insufficient, and engineering practice still lacks effective intelligent fault diagnosis methods. In this paper, we propose a weighted k-nearest neighbor (WKNN) fault diagnosis model based on multi-dimensional sensitive features, and propose a fault diagnosis method for rolling bearings that adapts to different equipment and different operating conditions. First, we extract time domain, frequency domain, and entropy features of the original signal to form the raw signal feature set. Then, the iterative ReliefF feature screening method is used to evaluate the joint feature set, calculate the weight of each feature, remove insensitive and redundant features, and obtain a highdimensional sensitive feature set. Finally, the WKNN classification model is used to identify bearing failure modes. The fault diagnosis model was trained using rolling bearing data from the Case Western Reserve University (CWRU), while laboratory data from the Intelligent Maintenance System (IMS), the Society of Mechanical Failure Prevention Technology (MFPT) and the engineering case data were used for testing. The results show that the model proposed in this paper has high fault diagnosis accuracy and can accurately determine the fault type after early warning. Compared with other comparison methods, the fault recognition accuracy rate is higher. And it is suitable for different working conditions and different equipment, and has good engineering application value.INDEX TERMS Different working conditions, fault diagnosis, multi-dimensional sensitive features, ReliefF, WKNN.

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An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm

et al. 2021

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Study of Fault Diagnosis for Rolling Bearing Based on Clustering Algorithms

Cited by 3 publications

References 15 publications

Fault diagnosis method for bogie bearings of high-speed trains based on weighted-sparse subspace clustering

Fault diagnosis method for bogie bearings of high-speed trains based on weighted-sparse subspace clustering

Weighted K-NN Classification Method of Bearings Fault Diagnosis With Multi-Dimensional Sensitive Features

An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm

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