An adaptive prediction approach for rolling bearing remaining useful life based on multistage model with three-source variability

Liu, Shujie; Fan, Lexian

doi:10.1016/j.ress.2021.108182

Cited by 55 publications

(27 citation statements)

References 38 publications

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“…The latter two have received the most attention. Taking the popular statistical learning techniques as the example, many methods has been applied in degradation monitoring, including supervised learning [ 28 ], unsupervised learning [ 29 ], transfer learning [ 30 ], statistical model [ 31 ], integrated learning [ 32 ], etc. For existing data problems, a large number of studies have also been carried out to reduce their impacts in the tasks of classification [ 33 , 34 ] and regression [ 5 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

An Adaptive Sampling Framework for Life Cycle Degradation Monitoring

Yin

Liu

Zhang

et al. 2023

Sensors

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Data redundancy and data loss are relevant issues in condition monitoring. Sampling strategies for segment intervals can address these at the source, but do not receive the attention they deserve. Currently, the sampling methods in relevant research lack sufficient adaptability to the condition. In this paper, an adaptive sampling framework of segment intervals is proposed, based on the summary and improvement of existing problems. The framework is implemented to monitor mechanical degradation, and experiments are implemented on simulation data and real datasets. Subsequently, the distributions of the samples collected by different sampling strategies are visually presented through a color map, and five metrics are designed to assess the sampling results. The intuitive and numerical results show the superiority of the proposed method in comparison to existing methods, and the results are closely related to data status and degradation indicators. The smaller the data fluctuation and the more stable the degradation trend, the better the result. Furthermore, the results of the objective physical indicators are obviously better than those of the feature indicators. By addressing existing problems, the proposed framework opens up a new idea of predictive sampling, which significantly improves the degradation monitoring.

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Section: Methodsmentioning

confidence: 99%

An Adaptive Sampling Framework for Life Cycle Degradation Monitoring

Yin

Liu

Zhang

et al. 2023

Sensors

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“…Minimization is performed through the trust-region reflective algorithm implemented in an open-source scientific computing library. The used curves are disposed of in Table 2 and represent common degradation patterns found in mechanical components [13,26].…”

Section: Step 3: Fault Prognosismentioning

confidence: 99%

Semi-Supervised Framework with Autoencoder-Based Neural Networks for Fault Prognosis

Rosa

Melani

Pereira

et al. 2022

Sensors

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This paper presents a generic framework for fault prognosis using autoencoder-based deep learning methods. The proposed approach relies upon a semi-supervised extrapolation of autoencoder reconstruction errors, which can deal with the unbalanced proportion between faulty and non-faulty data in an industrial context to improve systems’ safety and reliability. In contrast to supervised methods, the approach requires less manual data labeling and can find previously unknown patterns in data. The technique focuses on detecting and isolating possible measurement divergences and tracking their growth to signalize a fault’s occurrence while individually evaluating each monitored variable to provide fault detection and prognosis. Additionally, the paper also provides an appropriate set of metrics to measure the accuracy of the models, which is a common disadvantage of unsupervised methods due to the lack of predefined answers during training. Computational results using the Commercial Modular Aero Propulsion System Simulation (CMAPSS) monitoring data show the effectiveness of the proposed framework.

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“…Electric motors are widely used in power systems (Shu and Le, 2022), and their smooth operation largely affects the quality of power supply of the whole system. Bearings play a role in the structure of electric motors to support the mechanical rotating body (Han et al, 2019), reduce the friction coefficient during its movement, and ensure the rotation accuracy, and the service life of the bearing has a great impact on the stable operation of the motor.…”

Section: Introductionmentioning

confidence: 99%

KPCA-WPHM-SCNs-based remaining useful life prediction method for motor rolling bearings

Han,

Song,

Shi

et al. 2023

Transactions of the Institute of Measurement and Control

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Motor rolling bearings are the important supporting components of motors. It can ensure the stable operation of motor equipment in the power grid, and bearing life prediction of it is a key issue. To solve the problem of low accuracy of remaining useful life (RUL) prediction for motor rolling bearings, a neural network model based on Weibull proportional hazards model (WPHM) and stochastic configuration networks (SCNs) is proposed. To better extract and analyze features of the bearing vibration signal in both time and frequency domains, kernel principal component analysis (KPCA) is used to reduce the dimensionality of the data. Then, a WPHM model using the top three contributing feature parameters is built, which sets the start time based on the failure rate curve and reliability function. Finally, the validity of the model is verified with the rolling bearing full life cycle dataset from the IEEE PHM 2012 Data Challenge, and a comparison with other machine learning models shows that the accuracy of the proposed model in RUL prediction is higher.

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An adaptive prediction approach for rolling bearing remaining useful life based on multistage model with three-source variability

Cited by 55 publications

References 38 publications

An Adaptive Sampling Framework for Life Cycle Degradation Monitoring

An Adaptive Sampling Framework for Life Cycle Degradation Monitoring

Semi-Supervised Framework with Autoencoder-Based Neural Networks for Fault Prognosis

KPCA-WPHM-SCNs-based remaining useful life prediction method for motor rolling bearings

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