scholarly journals Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1560 ◽  
Author(s):  
Tomasz Ciszewski ◽  
Len Gelman ◽  
Andrew Ball
Keyword(s):  
Data Processing ◽  
Rolling Bearing ◽  
Higher Order ◽  
Spectral Triple ◽  
Fault Identification ◽  
Electromechanical Systems ◽  
Processing Technologies ◽  
Bearing Fault ◽  
Triple Correlation ◽  
Electrical Data

It is proposed, developed, investigated, and validated by experiments and modelling for the first time in worldwide terms new data processing technologies, higher order spectral multiple correlation technologies for fault identification for electromechanical systems via electrical data processing. Investigation of the higher order spectral triple correlation technology via modelling has shown that the proposed data processing technology effectively detects component faults. The higher order spectral triple correlation technology successfully applied for rolling bearing fault identification. Experimental investigation of the technology has shown, that the technology effectively identifies rolling bearing fault by electrical data processing at very early stage of fault development. Novel technology comparisons via modelling and experiments of the proposed higher order spectral triple correlation technology and the higher order spectra technology show the higher fault identification effectiveness of the proposed technology over the bicoherence technology.

Product function correntropy and its application in rolling bearing fault identification

Measurement ◽  
2017 ◽  
Vol 97 ◽  
pp. 88-99 ◽  
Author(s):  
Yunxiao Fu ◽  
Limin Jia ◽  
Yong Qin ◽  
Jie Yang
Keyword(s):  
Rolling Bearing ◽  
Fault Identification ◽  
Bearing Fault ◽  
Product Function

scholarly journals A Rolling Bearing Fault Classification Scheme Based on k-Optimized Adaptive Local Iterative Filtering and Improved Multiscale Permutation Entropy

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 191
Author(s):  
Yi Zhang ◽  
Yong Lv ◽  
Mao Ge
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Bp Neural Network ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Permutation Entropy ◽  
Fault Identification ◽  
Depth Information ◽  
Bearing Fault ◽  
Iterative Filtering

The health condition of the rolling bearing seriously affects the operation of the whole mechanical system. When the rolling bearing parts fail, the time series collected in the field generally shows strong nonlinearity and non-stationarity. To obtain the faulty characteristics of mechanical equipment accurately, a rolling bearing fault detection technique based on k-optimized adaptive local iterative filtering (ALIF), improved multiscale permutation entropy (improved MPE), and BP neural network was proposed. In the ALIF algorithm, a k-optimized ALIF method based on permutation entropy (PE) is presented to select the number of ALIF decomposition layers adaptively. The completely average coarse-graining method was proposed to excavate more hidden information. The performance analysis of the simulation signal shows that the improved MPE can more accurately dig out the depth information of the time series, and the entropy value obtained is more consistent and stable. In the research application, rolling bearing time series are decomposed by k-optimized ALIF to obtain a certain number of intrinsic mode functions (IMFs). Then the improved MPE value of effective IMF is calculated and input into backpropagation (BP) neural network as the feature vector for automatic fault identification. The comparative analysis of simulation signals shows that this method can extract fault information effectively. At the same time, the experimental part shows that this scheme not only effectively extracts the fault features, but also realizes the classification and identification of different fault modes and faults of different degrees, which has a certain application prospect in the research and application direction of rolling bearing fault identification.

Structuring and visualization of indicators in multidimensional data cubes

2018 ◽  
pp. 79-87
Author(s):  
E. E. Akimkina
Keyword(s):  
Decision Making ◽  
Data Processing ◽  
Data Management ◽  
Multidimensional Data ◽  
End User ◽  
Processing Technologies ◽  
Data Cubes ◽  
Multidimensional Visualization ◽  
Multidimensional Data Cubes ◽  
Practical Recommendations

The problems of structuring of indicators in multidimensional data cubes with their subsequent processing with the help of end-user tools providing multidimensional visualization and data management are analyzed; the possibilities of multidimensional data processing technologies for managing and supporting decision making at a design and technological enterprise are shown; practical recommendations on the use of domestic computer environments for the structuring and visualization of multidimensional data cubes are given.

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