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基于自适应噪声消除技术、异构特征提取和距离比主成分分析的变速箱故障识别模型

Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis.

作者信息

Nguyen Cong Dai, Kim Cheol Hong, Kim Jong-Myon

机构信息

Faculty of Radio-Electronic Engineering, Le Quy Don Technical University, Hanoi 10000, Vietnam.

School of Computer Science and Engineering, Soongsil University, Seoul 06978, Korea.

出版信息

Sensors (Basel). 2022 May 27;22(11):4091. doi: 10.3390/s22114091.

Abstract

Using an adaptive noise canceling technique (ANCT) and distance ratio principal component analysis (DRPCA), this paper proposes a new fault diagnostic model for multi-degree tooth-cut failures (MTCF) in a gearbox operating at inconsistent speeds. To account for background and disturbance noise in the vibration characteristics of gear failures, the proposed approach employs ANCT in the first stage to optimize vibration signals. The ANCT applies an adaptive denoising technique to each basic frequency segment in the whole frequency response of vibrations. Following that, a novel DRPCA is used to extract the discriminating low-dimensional features. The DRPCA initially determines each feature's relative proximity to fault categories by computing the average Euclidian distance ratio between similar and dissimilar classes. The most discriminatory features with the lowest dimensions are selected, as determined by principal component analysis (PCA). The new DRPCA is created by combining distance ratio-based feature inspection with PCA. The optimal feature set containing the most discriminative features is then fed to the support vector machine classifier to identify multiple failure categories. The experimental results indicate that the proposed model outperforms the state-of-art approaches and offers the highest identification accuracy.

摘要

本文采用自适应噪声消除技术(ANCT)和距离比主成分分析(DRPCA),提出了一种针对变速运行的变速箱中多程度断齿故障(MTCF)的新型故障诊断模型。为了考虑齿轮故障振动特性中的背景噪声和干扰噪声,该方法在第一阶段采用ANCT来优化振动信号。ANCT对振动全频率响应中的每个基本频率段应用自适应去噪技术。随后,使用一种新颖的DRPCA来提取具有区分性的低维特征。DRPCA首先通过计算相似类和不同类之间的平均欧几里得距离比来确定每个特征与故障类别的相对接近程度。如主成分分析(PCA)所确定的,选择维度最低且最具区分性的特征。通过将基于距离比的特征检验与PCA相结合创建新的DRPCA。然后将包含最具区分性特征的最优特征集输入到支持向量机分类器中,以识别多种故障类别。实验结果表明,所提出的模型优于现有方法,具有最高的识别准确率。

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