A new adaptive cascaded stochastic resonance method for impact features extraction in gear fault diagnosis

被引：52

作者：

Li, Jimeng ^{[1
]}

Zhang, Yungang ^{[1
]}

Xie, Ping ^{[1
]}

机构：

[1] Yanshan Univ, Coll Elect Engn, Qinhuangdao 066004, Hebei, Peoples R China

来源：

MEASUREMENT | 2016年 / 91卷

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Cascaded stochastic resonance; Adaptive; Impact signal detection; Gear fault diagnosis; SIGNAL-DETECTION METHOD; PLANETARY GEAR; DECOMPOSITION; ARRAY;

D O I：

10.1016/j.measurement.2016.05.086

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Gearboxes are widely used in engineering machinery, but tough operation environments often make them subject to failure. And the emergence of periodic impact components is generally associated with gear failure in vibration analysis. However, effective extraction of weak impact features submerged in strong noise has remained a major challenge. Therefore, the paper presents a new adaptive cascaded stochastic resonance (SR) method for impact features extraction in gear fault diagnosis. Through the multi-filtered procession of cascaded SR, the weak impact features can be further enhanced to be more evident in the time domain. By analyzing the characteristics of non-dimensional index for impact signal detection, new measurement indexes are constructed, and can further promote the extraction capability of SR for impact features by combining the data segmentation algorithm via sliding window. Simulation and application have confirmed the effectiveness and superiority of the proposed method in gear fault diagnosis. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：499 / 508

页数：10

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