Research on a rolling bearing health monitoring algorithm oriented to industrial big data

Zhu, Wenchang; Ni, Guangxian; Cao, Yupeng; Wang, Heng

doi:10.1016/j.measurement.2021.110044

Cited by 28 publications

(13 citation statements)

References 28 publications

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“…We call the dataset provided by Saxena and Goebel (2008) C-MAPSS (ver.1) and the dataset provided by Arias Chao et al C-MAPSS (ver 2). Each dataset has 14 health parameters, and we make a new health index that is used as a time series to predict RUL, as presented in Zhu et al (2021) . The other datasets are life time of Li-ion batteries measured under various room temperature provided by Goebel et al (2008) .…”

Section: Methodsmentioning

confidence: 99%

Shapelet selection based on a genetic algorithm for remaining useful life prediction with supervised learning

Ahn¹,

Jin²,

Hwang³

et al. 2022

Heliyon

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Section: Methodsmentioning

confidence: 99%

Shapelet selection based on a genetic algorithm for remaining useful life prediction with supervised learning

Ahn¹,

Jin²,

Hwang³

et al. 2022

Heliyon

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“…Rolling bearings play a crucial role in modern industry [1]. Bearing failures, predominantly attributed to wear [2] and fatigue cracking [3,4], significantly compromise equipment reliability and safety.…”

Section: Introductionmentioning

confidence: 99%

Graph Convolutional Network Based on CQT Spectrogram for Bearing Fault Diagnosis

Yan,

Liao,

Zhang

et al. 2024

Machines

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In this paper, a graph convolutional network is constructed and applied for bearing fault diagnosis. Specifically, the constant-Q transform (CQT) is first adopted for spectral analysis of vibration signals, where the frequencies are distributed in the logarithmic scale. Varied frequency resolutions can be obtained to satisfy the spectral resolution requirement and reduce signal dimension. Afterwards, the CQT spectrum is modeled by a graph, where nodes are frequency bins and edges reflect the inner relationship of different bins. There are edges between the fundamental and harmonic components. Then, a two-layer graph convolutional network (GCN) is utilized to assess the significance of vibration sources within the mixed signals. Finally, the bearing faults are determined according to the output of the GCN. To the best of our knowledge, this is the first work to model the vibration signal in this graph structure. The advantage of this approach lies in the simplification of edge definitions, facilitating shared connectivity relationships between the fundamental frequency and harmonics. Its performance was compared with another state-of-the-art fault diagnosis model. Experimental results demonstrate that the proposed model obtains higher accuracy, and it is more effective in extracting discriminative features.

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“…In [ 10 ], a PHM model for the prediction of component failures and the system lifetime is proposed by combining monitoring data, time-to-failure data, and background engineering knowledge of the systems. Likewise, in [ 11 ], the degradation index of a rolling bearing is constructed based on the monitoring data.…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Machine Learning Approach for Monitoring Data Fusion and Health Indicator Construction

Huang,

Pan,

Liu

et al. 2023

Sensors

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The prediction of system degradation is very important as it serves as an important basis for the formulation of condition-based maintenance strategies. An effective health indicator (HI) plays a key role in the prediction of system degradation as it enables vital information for critical tasks ranging from fault diagnosis to remaining useful life prediction. To address this issue, a method for monitoring data fusion and health indicator construction based on an autoencoder (AE) and a long short-term memory (LSTM) network is proposed in this study to improve the predictability and effectiveness of health indicators. Firstly, an unsupervised method and overall framework for HI construction is built based on a deep autoencoder and an LSTM neural network. The neural network is trained fully based on the normal operating monitoring data and then the construction error of the AE model is adopted as the health indicator of the system. Secondly, we propose related machine learning techniques for monitoring data processing to overcome the issue of data fusion, such as mutual information for sensor selection and t-distributed stochastic neighbor embedding (T-SNE) for operating condition identification. Thirdly, in order to verify the performance of the proposed method, experiments are conducted based on the CMAPSS dataset and results are compared with algorithms of principal component analysis (PCA) and a vanilla autoencoder model. Result shows that the LSTM-AE model outperforms the PCA and Vanilla-AE model in the metrics of monotonicity, trendability, prognosability, and fitness. Fourthly, in order to analyze the impact of the time step of the LSMT-AE model on HI construction, we construct and analyze the system HI curve under different time steps of 5, 10, 15, 20, and 25 cycles. Finally, the results demonstrate that the proposed method for HI construction can effectively characterize the health state of a system, which is helpful for the development of further failure prognostics and converting the scheduled maintenance into condition-based maintenance.

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Research on a rolling bearing health monitoring algorithm oriented to industrial big data

Cited by 28 publications

References 28 publications

Shapelet selection based on a genetic algorithm for remaining useful life prediction with supervised learning

Shapelet selection based on a genetic algorithm for remaining useful life prediction with supervised learning

Graph Convolutional Network Based on CQT Spectrogram for Bearing Fault Diagnosis

An Unsupervised Machine Learning Approach for Monitoring Data Fusion and Health Indicator Construction

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