Title: Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images
Author: Xunshi Yan; Chen-An Zhang; Zhengang Shi; Jingjing Zhao; Zhe Sun
Source: Sensors
Issued Date: 2019
Volume: 19, Issue:2, Pages:244
Keyword: Fault Diagnosis ; Vibration Signals ; Active Magnetic Bearing ; Rotary Machinery ; Adaboost
DOI: 10.3390/s19020244
English Abstract:

As important sources in fault diagnosis of rotary machinery, vibration signals are usually processed in the time or frequency domain as features to distinguish different classes of faults. However, these kinds of processing methods always ignore the corresponding relations among multiple signals, resulting in information loss. In this paper, a new fault description strategy named vibration image is proposed, based on which three new kinds of features are extracted, containing coupling information between different channels of vibration signals. Additionally, a new feature fusion method called two-layer AdaBoost is designed to train the fault recognition model, which avoids overfitting when the dataset is not large enough. Features based on vibration images combined with two-layer AdaBoost are adopted to diagnose faults of rotary machinery. Taking an active magnetic bearing-rotor system as the experimental platform, a dataset with four classes of faults is collected and our algorithm achieves good performance. Meanwhile, features based on vibration images and two-layer AdaBoost are both proved to be efficient separately.

Classification: 二类
URL: https://www.mdpi.com/1424-8220/19/2/244
Language: 英语
Citation statistics:
Content Type: 期刊论文
URI: [http://dspace.imech.ac.cn/handle/311007/78325]  
Appears in Collections:力学研究所_高温气体动力学国家重点实验室

description.institution: 1.Institute of Nuclear and New Energy Technology, Tsinghua University
2.State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences
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