A novel approach for analog fault diagnosis based on LMD decomposition and reconstruction

2016 ◽  
Author(s):  
Donglei Chen ◽  
Youren Wang ◽  
Jiang Cui ◽  
Deming Kong ◽  
Hui Luo
Keyword(s):  
Fault Diagnosis ◽  
Analog Fault Diagnosis ◽  
Novel Approach

A novel approach for analog fault diagnosis based on stochastic signal analysis and improved GHMM

Measurement ◽  
2016 ◽  
Vol 81 ◽  
pp. 26-35 ◽  
Author(s):  
Hui Luo ◽  
Wei Lu ◽  
Youren Wang ◽  
Ling Wang ◽  
Xianlin Zhao
Keyword(s):  
Fault Diagnosis ◽  
Signal Analysis ◽  
Stochastic Signal ◽  
Analog Fault Diagnosis ◽  
Novel Approach

scholarly journals A Denoising Autoencoder-Based Bearing Fault Diagnosis System for Time-Domain Vibration Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yi Gu ◽  
Jiawei Cao ◽  
Xin Song ◽  
Jian Yao
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Bp Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Novel Approach ◽  
Fault Diagnosis System ◽  
Original Time

The condition monitoring of rotating machinery is always a focus of intelligent fault diagnosis. In view of the traditional methods’ excessive dependence on prior knowledge to manually extract features, their limited capacity to learn complex nonlinear relations in fault signals and the mixing of the collected signals with environmental noise in the course of the work of rotating machines, this article proposes a novel approach for detecting the bearing fault, which is based on deep learning. To effectively detect, locate, and identify faults in rolling bearings, a stacked noise reduction autoencoder is utilized for abstracting characteristic from the original vibration of signals, and then, the characteristic is provided as input for backpropagation (BP) network classifier. The results output by this classifier represent different fault categories. Experimental results obtained on rolling bearing datasets show that this method can be used to effectively diagnose bearing faults based on original time-domain signals.

A novel approach for analog circuit fault diagnosis based on Deep Belief Network

Measurement ◽  
2018 ◽  
Vol 121 ◽  
pp. 170-178 ◽  
Author(s):  
Guangquan Zhao ◽  
Xiaoyong Liu ◽  
Bin Zhang ◽  
Yuefeng Liu ◽  
Guangxing Niu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuit ◽  
Deep Belief Network ◽  
Belief Network ◽  
Novel Approach ◽  
Circuit Fault Diagnosis

A novel approach to transformer fault diagnosis using IDM and naive credal classifier

2019 ◽  
Vol 105 ◽  
pp. 846-855 ◽  
Author(s):  
B. Zhao ◽  
M. Yang ◽  
H.R. Diao ◽  
B. An ◽  
Y.C. Zhao ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Novel Approach ◽  
Transformer Fault

A novel approach applied to fault diagnosis for micro-defects on piston throat

Measurement ◽  
2020 ◽  
pp. 108508
Author(s):  
Zhongxin Chen ◽  
Feng Zhao ◽  
Jun Zhou ◽  
Panling Huang ◽  
Wenping Song
Keyword(s):  
Fault Diagnosis ◽  
Novel Approach
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