scholarly journals Study on the Fault Diagnosis for Scroll Compressor Based on Information Entropy

2012 ◽  
Author(s):  
Tao Liu ◽  
Zai xin Wu ◽  
Yong wei Wang
Keyword(s):  
Fault Diagnosis ◽  
Information Entropy ◽  
Scroll Compressor

scholarly journals Research on Fault Diagnosis Model of Rotating Machinery Vibration Based on Information Entropy and Improved SVM

2019 ◽  
Vol 118 ◽  
pp. 02036 ◽  
Author(s):  
Hankun Bing ◽  
Yuzhu Zhao ◽  
Le Pang ◽  
Minmin Zhao
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Energy Spectrum ◽  
Time Domain ◽  
Information Entropy ◽  
Singular Value ◽  
Support Vector ◽  
Learning Platform ◽  
Diagnosis Model ◽  
Concept Of Information

Based on the concept of information entropy, this paper analyzes typical nonlinear vibration fault signals of steam turbine based on spectrum, wavelet and HHT theory methods, and extracts wavelet energy spectrum entropy, IMF energy spectrum entropy, time domain singular value entropy and frequency domain power spectrum entropy as faults. The feature is supported by a support vector machine (SVM) as a learning platform. The research results show that the fusion information entropy describes the vibration fault more comprehensively, and the support vector machine fault diagnosis model can achieve higher diagnostic accuracy.

scholarly journals Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD

2020 ◽  
Vol 10 (18) ◽  
pp. 6376 ◽  
Author(s):  
Yihan Wang ◽  
Zhonghui Fan ◽  
Hongmei Liu ◽  
Xin Gao
Keyword(s):  
Fault Diagnosis ◽  
Information Entropy ◽  
Entropy Method ◽  
Characteristic Matrix ◽  
Intrinsic Mode Functions ◽  
Noise Component ◽  
Time Frequency ◽  
Frequency Information ◽  
Operation Conditions ◽  
Mode Decomposition

Planetary gearboxes are more and more widely used in large and complex construction machinery such as those used in aviation, aerospace fields, and so on. However, the movement of the gear is a typical complex motion and is often under variable conditions in real environments, which may make vibration signals of planetary gearboxes nonlinear and nonstationary. It is more difficult and complex to achieve fault diagnosis than to fix the axis gearboxes effectively. A fault diagnosis method for planetary gearboxes based on improved complementary ensemble empirical mode decomposition (ICEEMD)-time-frequency information entropy and variable predictive model-based class discriminate (VPMCD) is proposed in this paper. First, the vibration signal of planetary gearboxes is decomposed into several intrinsic mode functions (IMFs) by using the ICEEMD algorithm, which is used to determine the noise component by using the magnitude of the entropy and to remove the noise components. Then, the time-frequency information entropy of intrinsic modal function under the new decomposition is calculated and regarded as the characteristic matrix. Finally, the fault mode is classified by the VPMCD method. The experimental results demonstrate that the method proposed in this paper can not only solve the fault diagnosis of planetary gearboxes under different operation conditions, but can also be used for fault diagnosis under variable operation conditions. Simultaneously, the proposed method is superior to the wavelet entropy method and variational mode decomposition (VMD)-time-frequency information entropy.

scholarly journals Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaoming Xu ◽  
Chenglin Wen
Keyword(s):  
Fault Diagnosis ◽  
Principal Components ◽  
Information Entropy ◽  
Principle Component Analysis ◽  
Information Gain ◽  
Principal Component ◽  
Component Analysis ◽  
Principle Component ◽  
Original Dataset ◽  
Diagnosis Method

In traditional principle component analysis (PCA), because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs) often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

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