A weighted multi-scale dictionary learning model and its applications on bearing fault diagnosis

Journal of Sound and Vibration ◽

10.1016/j.jsv.2019.01.042 ◽

2019 ◽

Vol 446 ◽

pp. 429-452 ◽

Author(s):

Zhibin Zhao ◽

Baijie Qiao ◽

Shibin Wang ◽

Zhixian Shen ◽

Xuefeng Chen

Keyword(s):

Fault Diagnosis ◽

Dictionary Learning ◽

Learning Model ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

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Bearing fault diagnosis based on DNN using multi-scale feature fusion

2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC) ◽

10.1109/yac51587.2020.9337689 ◽

2020 ◽

Author(s):

Funa Zhou ◽

Zhiqiang Zhang ◽

Danmin Chen

Keyword(s):

Fault Diagnosis ◽

Feature Fusion ◽

Scale Feature ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

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Bearing fault diagnosis based on combined multi-scale weighted entropy morphological filtering and bi-LSTM

Applied Intelligence ◽

10.1007/s10489-021-02229-1 ◽

2021 ◽

Author(s):

Fengqian Zou ◽

Haifeng Zhang ◽

Shengtian Sang ◽

Xiaoming Li ◽

Wanying He ◽

...

Keyword(s):

Fault Diagnosis ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Multi Scale ◽

Morphological Filtering ◽

Weighted Entropy

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Rolling bearing fault diagnosis based on multi-channel convolution neural network and multi-scale clipping fusion data augmentation

Measurement ◽

10.1016/j.measurement.2021.109885 ◽

2021 ◽

pp. 109885

Author(s):

Ruxue Bai ◽

Quansheng Xu ◽

Zong Meng ◽

Lixiao Cao ◽

Kangshuo Xing ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Data Augmentation ◽

Rolling Bearing ◽

Convolution Neural Network ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Multi Scale ◽

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An anti-noise one-dimension convolutional neural network learning model applying on bearing fault diagnosis

Measurement ◽

10.1016/j.measurement.2021.110236 ◽

2021 ◽

Vol 186 ◽

pp. 110236

Author(s):

Fengqian Zou ◽

Haifeng Zhang ◽

Shengtian Sang ◽

Xiaoming Li ◽

Wanying He ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Learning Model ◽

One Dimension ◽

Neural Network Learning ◽

Network Learning ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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A deep learning model for bearing fault diagnosis based on convolution neural network with multi-channel and residual network

10.1109/ccdc52312.2021.9601592 ◽

2021 ◽

Author(s):

Jianyong Tuo ◽

Yu Hu ◽

Xin Ma ◽

Youqing Wang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Fault Diagnosis ◽

Learning Model ◽

Convolution Neural Network ◽

Residual Network ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Deep Learning Model

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A Multi-scale Fuzzy Measure Entropy and Infinite Feature Selection Based Approach for Rolling Bearing Fault Diagnosis

Journal of Nondestructive Evaluation ◽

10.1007/s10921-019-0623-4 ◽

2019 ◽

Vol 38 (4) ◽

Author(s):

Keheng Zhu ◽

Liang Chen ◽

Xiong Hu

Keyword(s):

Feature Selection ◽

Fault Diagnosis ◽

Rolling Bearing ◽

Fuzzy Measure ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Multi Scale ◽

Measure Entropy

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A new structuring element for multi-scale morphology analysis and its application in rolling element bearing fault diagnosis

Journal of Vibration and Control ◽

10.1177/1077546313486163 ◽

2013 ◽

Vol 21 (4) ◽

pp. 765-789 ◽

Author(s):

Qiong Chen ◽

Zhaowen Chen ◽

Wei Sun ◽

Guoan Yang ◽

Ahmet Palazoglu ◽

...

Keyword(s):

Fault Diagnosis ◽

Rolling Element Bearing ◽

Scale Morphology ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Structuring Element ◽

Multi Scale ◽

Morphology Analysis ◽

Rolling Element ◽

Element Bearing

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An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis

Neurocomputing ◽

10.1016/j.neucom.2019.05.052 ◽

2019 ◽

Vol 359 ◽

pp. 77-92 ◽

Author(s):

Wenyi Huang ◽

Junsheng Cheng ◽

Yu Yang ◽

Gaoyuan Guo

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Deep Convolutional Neural Network ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

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Multi-Scale CNN based on Attention Mechanism for Rolling Bearing Fault Diagnosis

2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM) ◽

10.1109/aparm49247.2020.9209470 ◽

2020 ◽

Author(s):

Yijia Hao ◽

Huan Wang ◽

Zhiliang Liu ◽

Haoran Han

Keyword(s):

Fault Diagnosis ◽

Rolling Bearing ◽

Attention Mechanism ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

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Motor Bearing Fault Diagnosis Based on MSICA-LSSVM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.55-57.747 ◽

2011 ◽

Vol 55-57 ◽

pp. 747-752

Author(s):

Zhong Hai Li ◽

Hao Fei Mao ◽

Jian Guo Cui ◽

Yan Zhang

Keyword(s):

Fault Diagnosis ◽

Wavelet Decomposition ◽

Principal Component ◽

Support Vector ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Multi Scale ◽

Diagnosis Method ◽

The paper presents a motor bearing fault diagnosis method based on MSICA (Multi-scale Independent Principal Component Analysis) and LSSVM (Least Squares Support Vector Machine). MSICA is introduced into motor fault diagnosis. First, wavelet decomposition is used, and then ICA models are built by wavelet coefficients in each scale, which detect fault, and finally LSSVM is used to classify fault type. Conclusions are obtained from the analysis of the experimental data provided by Case Western Reserve University’s Bearing Data Website. And it indicates that the proposed method is simple and effective.

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