Intelligent Fault Diagnosis of Hydraulic System Based on Multiscale One-Dimensional Convolutional Neural Networks with Multiattention Mechanism

被引:0
作者
Sun, Jiacheng [1 ,2 ]
Ding, Hua [1 ,2 ]
Li, Ning [1 ,2 ]
Sun, Xiaochun [1 ,2 ]
Dong, Xiaoxin [1 ,2 ]
机构
[1] Taiyuan Univ Technol, Coll Mech & Vehicle Engn, Taiyuan 030024, Peoples R China
[2] Taiyuan Univ Technol, Shanxi Key Lab Fully Mechanized Coal Min Equipment, Taiyuan 030024, Peoples R China
基金
中国国家自然科学基金;
关键词
channel attention mechanism; convolutional neural network; fault diagnosis; hydraulic system; multirate data samples; FUSION;
D O I
10.3390/s24227267
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Hydraulic systems are critical components of mechanical equipment, and effective fault diagnosis is essential for minimizing maintenance costs and enhancing system reliability. In practical applications, data from hydraulic systems are collected with varying sampling frequencies, coupled with complex interdependencies within the data, which poses challenges for existing fault diagnosis algorithms. To solve the above problems, this paper proposes an intelligent fault diagnosis of a hydraulic system based on a multiscale one-dimensional convolution neural network with a multiattention mechanism (MA-MS1DCNN). The proposed method first extracts features from multirate data samples using a parallel 1DCNN with different receptive fields. Next, a Hybrid Attention Module (HAM) is proposed, consisting of two submodules: the Correlation Attention Module (CAM) and the Importance Attention Module (IAM), which aim to meticulously and comprehensively model the complex relationships between channel features. Subsequently, to effectively utilize the feature information of different frequencies, the HAM is integrated into the 1DCNN to form the MA-MS1DCNN. Finally, the proposed method is evaluated and experimentally compared using the UCI hydraulic system dataset. The results demonstrate that, compared to existing methods such as Shapelet, MCIFM, and CNNs, the proposed method shows superior diagnostic performance.
引用
收藏
页数:19
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