Rolling Bearing Fault Diagnosis in Agricultural Machinery Based on Multi-Source Locally Adaptive Graph Convolution

Fengyun Xie (), Enguang Sun, Linglan Wang, Gan Wang and Qian Xiao
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Fengyun Xie: School of Mechanical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Enguang Sun: School of Mechanical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Linglan Wang: School of Mechanical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Gan Wang: School of Mechanical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Qian Xiao: School of Mechanical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China

Agriculture, 2024, vol. 14, issue 8, 1-16

Abstract: Maintaining agricultural machinery is crucial for efficient mechanized farming. Specifically, diagnosing faults in rolling bearings, which are essential rotating components, is of significant importance. Domain-adaptive technology often addresses the challenge of limited labeled data from a single source domain. However, information transfer can sometimes fall short in providing adequate relevant details for supporting target diagnosis tasks, leading to poor recognition performance. This paper introduces a novel fault diagnosis model based on a multi-source locally adaptive graph convolution network to diagnose rolling bearing faults in agricultural machinery. The model initially employs an overlapping sampling method to enhance sample data. Recognizing that two-dimensional time–frequency signals possess richer spatial characteristics in neural networks, wavelet transform is used to convert time series samples into time–frequency graph samples before feeding them into the feature network. This approach constructs a sample data pair from both source and target domains. Furthermore, a feature extraction network is developed by integrating the strengths of deep residual networks and graph convolutional networks, enabling the model to better learn invariant features across domains. The locally adaptive method aids the model in more effectively aligning features from the source and target domains. The model incorporates a Softmax layer as the bearing state classifier, which is set up after the graph convolutional network layer, and outputs bearing state recognition results upon reaching a set number of iterations. The proposed method’s effectiveness was validated using a bearing dataset from Jiangnan University. For three different groups of bearing fault diagnosis tasks under varying working conditions, the proposed method achieved recognition accuracies above 99%, with an improvement of 0.30%-4.33% compared to single-source domain diagnosis models. Comparative results indicate that the proposed method can effectively identify bearing states even without target domain labels, showcasing its practical engineering application value.

Keywords: agricultural machinery; rolling bearings; subdomain adaptive; transfer learning; graph convolutional network; fault diagnosis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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