Wavelet transform and multiresolution signal decomposition for machinery monitoring and diagnosis

Author(s):  
He Zhengjia ◽  
Zhao Jiyuan ◽  
He Yibin ◽  
Meng Qingfeng
2015 ◽  
Vol 27 (1) ◽  
pp. 43-52 ◽  
Author(s):  
Henryk Borowczyk

Abstract The method of a multi-valued diagnostic model synthesis using discrete wavelet transform is presented. The method's algorithm consists of three stages: (1) - signal decomposition into low- and high frequency parts - approximations and details, (2) - approximations and details parameterization, (3) - multi-valued encoding parameters obtained in stage 2. The method is illustrated with vibroacoustic signal in real life experiment. The multi-valued diagnostic model is the final result.


2020 ◽  
Vol 10 (21) ◽  
pp. 7418
Author(s):  
Grégoire Corradi ◽  
Jean-Jacques Sinou ◽  
Sébastien Besset

This paper is devoted to discussion of the efficiency of reduced models based on a Double Modal Synthesis method that combines a classical modal reduction and a condensation at the frictional interfaces by computing a reduced complex mode basis, for the prediction of squeal noise of mechanical systems subjected to friction-induced vibration. More specifically, the use of the multiresolution signal decomposition of acoustic radiation and wavelet representation will be proposed to analyze details of a pattern on different observation scales ranging from the pixel to the size of the complete acoustic pattern. Based on this approach and the definition of specific resulting criteria, it is possible to quantify the differences in the representation of the acoustic fields for different reduced models and thus to perform convergence studies for different scales of representation in order to evaluate the potential of reduced models. The effectiveness of the proposed approach is tested on the finite element model of a simplified brake system that is composed of a disc and two pads. The contact is modeled by introducing contact elements at the two friction interfaces with the classical Coulomb law and a constant friction coefficient. It is demonstrated that the new proposed criteria, based on multiresolution signal decomposition, allow us to provide satisfactory results for the choice of an efficient reduced model for predicting acoustic radiation due to squeal noise.


2011 ◽  
Vol 314-316 ◽  
pp. 2370-2374
Author(s):  
Yin Hua Liu ◽  
Yang Yang

The process monitoring and diagnosis in assembly process is important. Multivariate T2 control charts are applied to detect the mean shift and interaction change in the assembly process. However, T2 charts can not identify the root cause of the change. The traditional MTY method for T2 signal decomposition is computationally expensive, especially when the dimension of the variables is high. A new approach based on Bayesian network to identify the significant cause of T2 signals is proposed in this paper. The headlamp bracket case is used to illustrate the overall procedure. And the effectiveness of the proposed approach is evaluated.


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