Work

Prediction of rolling bearing performance degradation based on whale optimization algorithm and backpropagation model

Year: 2024

Type: article

Abstract: The dissertation proposes a prediction model that enhances the BP (Backpropagation) neural network using the WOA (Whale Optimization Algorithm) to address the issue of local convergence during prediction. The model optimizes the initial weights and thresholds of the network using the whale optimization algorithm, resolving the problem of BP’s local convergence. Feature data is selected using the correlation coefficient theory to obtain the model inputs. Bearing performance degradation assessment is conducted using the predicted remaining life index. The superiority of the model is validated by comparing its predictions with those of BP and LSTM prediction models.

Source: Journal of low frequency noise, vibration and active control

DOI: 10.1177/14613484241272178

Authors: Jingyue Wang, Yuntong Han, Haotian Wang, Jianming Ding, Cai Yi

Institutions: Shenyang Ligong University, State Key Laboratory of Traction Power, Southwest Jiaotong University, Shenyang Aerospace University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,500

Funder: State Key Laboratory of Traction Power