Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors

Xu, Z, Mei, X, Wang, X, Yue, M, Jin, J, Yang, Y and Li, C (2022) Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors. Renewable Energy, 182. pp. 615-626. ISSN 0960-1481

Preview

Text Revison_RE.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview

Abstract

In order to solve the problems of insufficient extrapolation of intelligent models for the fault diagnosis of bearings in real wind turbines, this study has developed a multi-scale convolutional neural network with bidirectional long short term memory (MSCNN-BiLSTM) model for improving the generalization abilities under complex working and testing environments. A weighted majority voting rule has been proposed to fuse the information from multi-sensors for improving the extrapolation of multisensory diagnosis. The superiority of the MSCNN-BiLSTM model is examined through experimental data. The results indicate that the MSCNN-BiLSTM model has 97.12% mean F1 score, which is higher than existing advanced methods. Real wind turbine dataset and an experimental dataset are used to demonstrate the effectiveness of the weighted majority voting rule for multisensory diagnosis. The results present that the diagnosis result of the MSCNN-BiLSTM model with weighted majority voting rule is higher respectively 1.32% and 5.7% than the model with traditional majority voting or fusion of multisensory information in feature-level.

Item Type:	Article
Uncontrolled Keywords:	Bearing; Convolutional neural network; Energy & Fuels; Fault diagnosis; GEARBOX; Green & Sustainable Science & Technology; Information fusion; MODEL; Science & Technology; Science & Technology - Other Topics; Technology; Wind turbine; Science & Technology; Technology; Green & Sustainable Science & Technology; Energy & Fuels; Science & Technology - Other Topics; Bearing; Wind turbine; Convolutional neural network; Fault diagnosis; Information fusion; GEARBOX; MODEL; 0906 Electrical and Electronic Engineering; 0913 Mechanical Engineering; 0915 Interdisciplinary Engineering; Energy
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TD Environmental technology. Sanitary engineering T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions:	Engineering
Publisher:	Elsevier
Date of acceptance:	6 October 2021
Date of first compliant Open Access:	7 July 2023
Date Deposited:	07 Jul 2023 10:07
Last Modified:	07 Jul 2023 10:07
DOI or ID number:	10.1016/j.renene.2021.10.024
URI:	https://ljmu-9.eprints-hosting.org/id/eprint/20243

View Item