Performance degradation prediction of mechanical equipment based on optimized multi-kernel relevant vector machine and fuzzy information granulation

Chen, Fafa; Yang, Yunpeng; Tang, Baoping; Chen, Baojia; Xiao, Wenrong; Zhong, Xianyou

doi:10.1016/j.measurement.2019.107116

Cited by 56 publications

(24 citation statements)

References 34 publications

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“…Moreover, 7% studied condition-based monitoring, whereas %4 focused on modeling the degradation process. In [18][19][20][21] references, modeling, and analysis of degradation processes are prioritized. Meanwhile, 3% of works aimed to improve RUL accuracy, also %3 prognostics and health management system creation, %2 focused on feature selection modeling.…”

Section: Rq11: What Is the Main Motivation Of The Published Article?mentioning

confidence: 99%

Kestirimci Bakım ve Kalan Yararlı Ömür Uygulama için Teknikler: Sistematik Haritalama Çalışması

Ture

Akbulut

Zaim

2021

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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With prognostic activities, it is possible to predict the remaining useful life (RUL) of industrial systems with high accuracy by following the current health status of devices. In this study, we have collected 199 articles on predictive maintenance and remaining useful life. The aim of our systematic mapping study is to determine which techniques and methods are used in the areas of predictive maintenance and remaining useful life. Another thing we aim is to give an idea about the main subject to the researchers who will work in this field. We created our article repository by searching databases such as IEEE and Science Direct with certain criteria and classified the articles we obtained. By applying the necessary inclusion and exclusion criteria in the article pool we collected, the most appropriate articles were determined and our study was carried out through these articles. When we focused on the results, it was learned that the SupportVector Machine algorithm is the most preferred predictive maintenance method. Most studies aimed at evaluating the performance and calculating the accuracy of the results used the Root Mean Square Error algorithm. In our study, every method and algorithm included in the articles are discussed. The articles were examined together with the goals and questions we determined, and results were obtained. The obtained results are explained and shown graphically in the article. According to the results, it is seen that the topics of predictive maintenance and remaining useful lifetime provide functionality and financial gain to the environment they are used in. Our study was concluded by light on many questions about the application of predictive maintenance.

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Section: Rq11: What Is the Main Motivation Of The Published Article?mentioning

confidence: 99%

Kestirimci Bakım ve Kalan Yararlı Ömür Uygulama için Teknikler: Sistematik Haritalama Çalışması

Ture

Akbulut

Zaim

2021

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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“…Liu et al [20] used a model-based particle filter method to monitor the performance degradation of rolling bearings for a series of problems caused by the operational state of the bearings. To address the low prediction accuracy of rolling bearing performance degradation, Fafa Chen et al [21] proposed an evaluation and prediction method based on wavelet packet information entropy and multicore correlation vector machine to monitor the rolling bearing performance status in real time. However, these methods have problems such as low prediction accuracy and poor generalization ability when high-dimensional data are obtained.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention‐LSTM

Wang

Yang

et al. 2021

Shock and Vibration

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It is significant for the evaluation and prediction of the performance degradation of rolling bearings. However, the degradation stage division of the rolling bearing performance is not obvious in traditional methods, and the prediction accuracy is low. Therefore, an Attention-LSTM method is proposed to improve the evaluation and prediction of the performance degradation of rolling bearings. First, to reduce the uncertainty of the manual intervention, performance degradation characteristic indexes of rolling bearings are evaluated and screened by the correlation, the monotonicity, and the robustness. Second, the original characteristic indicator curve is divided into the Health Indicator (HI) curve and the residual curve by means of fixed-window averaging to quantitatively and intuitively reflect the deterioration degree of the rolling bearing performance. Finally, the Attention mechanism is combined with the LSTM model, and a scoring function is established to enhance the prediction accuracy. The scoring function is used to adjust the intermediate output state weight of the LSTM model and improve the prediction accuracy. The appropriate network structure and the parameter configuration are determined, and the prediction model of rolling bearing degradation performance is established. Compared with other models, the method proposed by this paper makes full use of the historical data and is more sensitive to the key information in the long time series, and the eRMSE index and the eMAE index of the two sets of experimental data are minimum, and the prediction accuracy of rolling bearing degradation performance is higher. The model has the strong robustness and the generalization ability, which has the important engineering practical value for the prediction of the equipment health state.

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“…Their flaws existing in design, manufacturing, assembly, are gradually emerged, leading to bearing performance degradation or even machine breakdown. Bearing failure seriously threatens the reliability and safety of rotating machinery in practical application [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Statistical multivariate monitoring of rolling bearings working under varying operational conditions

Wang

Hong-xia

2021

IET Science Measure & Tech

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In recent years, statistical data-driven approaches have been developed for health monitoring of rolling bearings working under variable operational conditions. Due to the reliance on reference models, most of them encounter with the model mismatch problem. A novel statistical multivariate health monitoring framework is proposed in this article to determine the initial degradation of rolling bearings. On the one hand, multivariate statistical parameters are extracted from original vibration signals to indicate bearing's degradation trajectories from different perspectives, a dynamic autoencoder neural network is extended with a matrix augmentation trick for discovering both dynamic and nonlinear latent representation in the high-dimensional features. On the other hand, an eccentricity index is introduced for measuring information variations in the latent representation, such that bearing health development can be revealed timely. Then, an adaptive threshold is exploited for online monitoring of bearing damage. The effectiveness of the proposed work is substantiated using real vibration signals acquired from two different bearing degradation test rigs. The monitoring performance of the proposed work is evaluated in terms of false alarm rate (FAR). Experimental results confirm that the proposed work can identify early bearing damage under variable working conditions, by circumventing the model mismatch problem. It is found to be more superior than existing techniques.

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Performance degradation prediction of mechanical equipment based on optimized multi-kernel relevant vector machine and fuzzy information granulation

Cited by 56 publications

References 34 publications

Kestirimci Bakım ve Kalan Yararlı Ömür Uygulama için Teknikler: Sistematik Haritalama Çalışması

Kestirimci Bakım ve Kalan Yararlı Ömür Uygulama için Teknikler: Sistematik Haritalama Çalışması

Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention‐LSTM

Statistical multivariate monitoring of rolling bearings working under varying operational conditions

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