scholarly journals A Novel Method to Improve the Resolution of Envelope Spectrum for Bearing Fault Diagnosis Based on a Wireless Sensor Node

2014 ◽  
pp. 765-775 ◽  
Author(s):  
Guojin Feng ◽  
Dong Zhen ◽  
Xiange Tian ◽  
Fengshou Gu ◽  
Andrew D. Ball
Keyword(s):  
Fault Diagnosis ◽  
Sensor Node ◽  
Wireless Sensor ◽  
Wireless Sensor Node ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Novel Method ◽  
Envelope Spectrum

Rolling Element Bearing Fault Diagnosis Based on EEMD and Index of Envelope Spectrum Sparse Ratio

2021 ◽  
Author(s):  
Zhe Li ◽  
Menghao Zhang ◽  
Longlong Li ◽  
Runlin Chen ◽  
Yanchao Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Element Bearing ◽  
Envelope Spectrum

An Improved Envelope Spectrum via Candidate Fault Frequency Optimization- gram for Bearing Fault Diagnosis

2022 ◽  
pp. 116746
Author(s):  
Yao Cheng ◽  
Shengbo Wang ◽  
Bingyan Chen ◽  
Guiming Mei ◽  
Weihua Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Frequency Optimization ◽  
Envelope Spectrum

Convolutional Neural Network Considering the Effects of Noise for Bearing Fault Diagnosis

2020 ◽  
Author(s):  
Ilyoung Han ◽  
Jangbom Chai ◽  
Chanwoo Lim ◽  
Taeyun Kim
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
High Accuracy ◽  
Training Data ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
System Noise ◽  
Novel Method ◽  
Maintenance Activities

Abstract Convolutional Neural Network (CNN) is, in general, good at finding principal components of data. However, the characteristic components of the signals could often be obscured by system noise. Therefore, even though the CNN model is well-trained and predict with high accuracy, it may detect only the primary patterns of data which could be formed by system noise. They are, in fact, highly vulnerable to maintenance activities such as reassembly. In other words, CNN models could misdiagnose even with excellent performances. In this study, a novel method that combines the classification using CNN with the data preprocessing is proposed for bearing fault diagnosis. The proposed method is demonstrated by the following steps. First, training data is preprocessed so that the noise and the fault signature of the bearings are separated. Then, CNN models are developed and trained to learn significant features containing information of defects. Lastly, the CNN models are examined and validated whether they learn and extract the meaningful features or not.

scholarly journals A Novel Method of Bearing Fault Diagnosis in Time-Frequency Graphs Using InceptionResnet and Deformable Convolution Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 92743-92753
Author(s):  
Shaobo Li ◽  
Wanli Yang ◽  
Ansi Zhang ◽  
Huibin Liu ◽  
Jinyuan Huang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Novel Method

scholarly journals Autonomous Bearing Fault Diagnosis Method based on Envelope Spectrum

2017 ◽  
Vol 50 (1) ◽  
pp. 13378-13383 ◽  
Author(s):  
Andreas Klausen ◽  
Kjell G. Robbersmyr ◽  
Hamid R. Karimi
Keyword(s):  
Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Envelope Spectrum

Local Mean Decomposition Based Bearing Fault Detection

2012 ◽  
Vol 490-495 ◽  
pp. 360-364 ◽  
Author(s):  
Hui Li
Keyword(s):  
Vibration Signal ◽  
Noise Condition ◽  
Local Mean Decomposition ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Product Function ◽  
Local Mean ◽  
Novel Method ◽  
Envelope Spectrum

A novel method of bearing fault diagnosis based on local mean decomposition (LMD) is proposed. LMD method is self-adaptive to non-stationary and non-linear signal. LMD can adaptively decompose the vibration signal into a series of product functions (PFs), which is the product of an envelope signal and a frequency modulated signal. Then the envelope spectrum is applied to the selected product function that stands for the bearing faults. Therefore, the character of the bearing fault can be recognized according to the envelope spectrum of product function. The experimental results show that local mean decomposition based envelope spectrum can effectively detect and diagnose bearing inner and outer race fault under strong background noise condition.

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