A Quantitative Intelligent Diagnosis Method for Early Weak Faults of Aviation High-speed Bearings

Roller-mark is a common problem in offset printing and its solution method is important for printing. A new detecting method of texture analysis was given in this paper. In this study, printing image was acquired with high-speed CCD. Compared the difference between printing image and standard image, a defective image was obtained. Then the reason of roller-marks was given by the texture recognition of defect image. Finally, experiments were taken to prove the feasibility and effectiveness of this new method for the roller-marks diagnosis in the offset printing machine.

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Intelligent diagnosis method using probability density distribution and principal component analysis — Application on gear rotating machinery

2016 International Symposium on Flexible Automation (ISFA) ◽

10.1109/isfa.2016.7790194 ◽

2016 ◽

Cited By ~ 1

Author(s):

Liuyang Song ◽

Peng Chen ◽

Huaqing Wang

Keyword(s):

Principal Component Analysis ◽

Density Distribution ◽

Probability Density ◽

Principal Component ◽

Component Analysis ◽

Rotating Machinery ◽

Probability Density Distribution ◽

Intelligent Diagnosis ◽

Diagnosis Method ◽

Analysis Application

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Research of Fault Diagnosis Method for New High-speed Railway Single Power Distribution Station Based on Bayesian Network

10.1109/iciea51954.2021.9516213 ◽

2021 ◽

Author(s):

Hao Wang ◽

Xiaoqiong He

Keyword(s):

Fault Diagnosis ◽

Bayesian Network ◽

Power Distribution ◽

High Speed ◽

High Speed Railway ◽

Diagnosis Method ◽

Distribution Station ◽

Single Power

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Faults diagnosis in stator windings of high speed solid rotor induction motors using fuzzy neural network

International Journal of Power Electronics and Drive Systems (IJPEDS) ◽

10.11591/ijpeds.v12.i1.pp597-611 ◽

2021 ◽

Vol 12 (1) ◽

pp. 597

Author(s):

Ahmed Thamer Radhi ◽

Wael Hussein Zayer

Keyword(s):

Neural Network ◽

Equivalent Circuit ◽

High Speed ◽

Fuzzy Neural Network ◽

Induction Motors ◽

Stator Winding ◽

Three Phase ◽

Fuzzy Neural ◽

Diagnosis Method ◽

Faults Diagnosis

The paper deals with faults diagnosis method proposed to detect the inter-turn and turn to earth short circuit in stator winding of three-phase high-speed solid rotor induction motors. This method based on negative sequence current of motor and fuzzy neural network algorithm. On the basis of analysis of 2-D electromagnet field in the solid rotor the rotor impedance has been derived to develop the solid rotor induction motor equivalent circuit. The motor equivalent circuit is simulated by MATLAB software to study and record the data for training and testing the proposed diagnosis method. The numerical results of proposed approach are evaluated using simulation of a three-phase high-speed solid-rotor induction motor of two-pole, 140 Hz. The results of simulation shows that the proposed diagnosis method is fast and efficient for detecting inter-turn and turn to earth faults in stator winding of high-speed solid-rotor induction motors with different faults conditions

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