组内相关
卷积神经网络
断层(地质)
计算机科学
人工神经网络
变量(数学)
一般化
快速傅里叶变换
人工智能
可靠性(半导体)
模式识别(心理学)
算法
数学
统计
数学分析
地质学
功率(物理)
地震学
心理测量学
物理
量子力学
作者
Xiaoli Zhao,Jianyong Yao,Wenxiang Deng,Peng Ding,Yifei Ding,Minping Jia,Zheng Liu
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2023-09-01
卷期号:34 (9): 6339-6353
被引量:82
标识
DOI:10.1109/tnnls.2021.3135877
摘要
The industrial gearboxes usually work in harsh and variable conditions, which results in partial failure of gears or bearings. Accordingly, the continuous irregular fluctuations of gearbox under variable conditions maybe increase the intraclass difference and reduce the interclass difference for the monitored samples. To this end, a new intelligent fault diagnosis method of gearbox based on adaptive intraclass and interclass convolutional neural network (AIICNN) under variable working conditions is proposed. The core of the proposed algorithm is to apply the designed intraclass and interclass constraints to improve the distribution differences of samples. Meanwhile, the adaptive activation function is added into the 1-D convolutional neural network (1dCNN) to enlarge the heterogeneous distance and narrow the homogeneous distance of samples. Specifically, the training sample subset with intraclass and interclass spacing fluctuations under variable conditions is first converted into frequency domain through the fast Fourier transform (FFT), and the designed AIICNN algorithm is employed for model training. Afterward, the testing subset is provided to the trained AIICNN algorithm for fault diagnosis. The experimental data of the planetary gearbox test rig verify the feasibility of the proposed diagnosis method and algorithm. Compared with other methods, this method can eliminate the difference of sample distribution under variable conditions and improve its diagnostic generalization.
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