Remaining Useful Life Prediction of Rolling Bearings Based on PCA and GSACO-SVR Model

Jiang, You-liang; You, Zhen-nan; Cao, Zi-cong; Wang, Yan

doi:10.1088/1742-6596/2405/1/012018

Cited by 1 publication

(1 citation statement)

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“…While considering the various models used for predicting Remaining Useful Life (RUL), it is important to note that each model has its limitations. For instance, the SVR model may encounter difficulties in handling large datasets and capturing non-linear relationships, as it lacks the ability to automatically learn features [9]. On the other hand, LSTM models, which are based on the architecture of Recurrent Neural Networks (RNNs), excel in handling sequential data.…”

Section: Introductionmentioning

confidence: 99%

Bearing Health and Safety Analysis to Improve the Reliability and Efficiency of Horizontal Axis Wind Turbine

Nawaz,

Huang,

Niazi

et al. 2023

Proceeding of the 33rd European Safety and Reliability Conference

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The role of renewable energy has grown considerably in various regions of the world, particularly solar and wind energy sources. Both energy sources are capable to meet energy requirements in an efficient and environmentally sustainable manner in solar and wind rich regions of the world. South and north Asian regions are one of them, where solar and wind energy resources are utilized with the help of available energy conversion machines. The wind turbine is one of them and its various types have played a vital role in the utilization of wind energy in the industrial sector for power generation. Horizontal wind axis turbine is one such type and it is capable to achieve better power output plus energy efficiency. It is used therefore in large-scale wind electric power generation. Although, there are certain technical problems related to the performance of wind turbines, for instance bearing failure rate. Due to the failure of bearings and other parts of wind turbines, considerable costs of specialized maintenance have incurred on the purchase of parts, installation, and trained workmanship which contribute towards an increase in downtime of power generation. Bearing faults are extremely complex and sometimes cannot be resolved using reconfigurable control. Therefore, early detection of bearing faults is critical to its performance and lower downtime. This paper investigates the requirements of bearing health and safety assessment. The assessment is based on the principles of estimation of the Remaining Useful Life (RUL) of bearing. A proposed prognosis model of Reliability Health and Safety Analysis (RHSA) is used for the evaluation of the performance of the bearing. The reliability model is used for the assessment of the useful life of the bearing subsystem of the Horizontal axis wind turbine and early detection of RUL leads to improve the performance and to avoid its failures.

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Section: Introductionmentioning

confidence: 99%