Advancing Early Fault Diagnosis for Multi-Domain Agricultural Machinery Rolling Bearings through Data Enhancement

Fengyun Xie (), Gang Li, Hui Liu, Enguang Sun and Yang Wang
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Fengyun Xie: School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Gang Li: School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Hui Liu: School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Enguang Sun: School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Yang Wang: School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China

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

Abstract: In the context of addressing the challenge posed by limited fault samples in agricultural machinery rolling bearings, especially when early fault characteristics are subtle, this study introduces a novel approach. The proposed multi-domain fault diagnosis method, anchored in data augmentation, aims to discern early faults in agricultural machinery rolling bearings, particularly within an imbalanced sample framework. The methodology involves determining early fault signals throughout the life cycle, constructing early fault datasets with varying imbalance rates for different fault types, and subsequently employing the Synthetic Minority Oversampling Technique (SMOTE) to balance the fault data. The study then extracts relative wavelet packet energy and time-domain sensitive features (variance, peak to peak) from the original and generated fault data to form a multi-domain fault feature vector. This vector is utilized for fault state recognition using a Support Vector Machine (SVM). Evaluation metrics such as accuracy, recall, and F1 values assess the recognition effectiveness for each rolling bearing state, with the overall model recognition evaluated based on accuracy. The proposed method is rigorously analyzed and validated using the XJTU-SY rolling bearing accelerated life test dataset. Comparative analysis is conducted with non-data enhanced fault feature vectors, specifically the relative energy of the wavelet packet, both with and without time-domain features. Experimental results underscore the superior performance of multi-domain fault features in providing a comprehensive description of signal information, leading to enhanced classification performance. Furthermore, the study demonstrates improved classification accuracy and recall rates for the balanced dataset compared to the imbalanced dataset. This research significantly contributes to an effective identification method for the early fault diagnosis of small sample rolling bearings in agricultural machinery.

Keywords: agricultural machinery rolling bearings; multi-domain fault diagnosis; data augmentation; imbalanced samples; early fault detection (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
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