Structural response recovery based on improved multi-scale principal component analysis considering sensor performance degradation

2017 ◽  
Vol 21 (2) ◽  
pp. 241-255 ◽  
Author(s):  
Shenglan Ma ◽  
Jun Li ◽  
Hong Hao ◽  
Shaofei Jiang
Author(s):  
Andrew Eaton ◽  
Wael Ahmed ◽  
Marwan A. Hassan

Abstract Centrifugal pumps are used in a variety of engineering applications, such as power production, heating, cooling, and water distribution systems. Although centrifugal pumps are considered to be highly reliable hydraulic machines, they are susceptible to a wide range of damage due to several degradation mechanisms, which make them operate away from their best efficiency range. Therefore, evaluating the energy efficiency and performance degradation of pumps is an important consideration to the operation of these systems. In the present study, the hydraulic performance along with the vibration response of an industrial scale centrifugal pump (7.5KW) subjected to different levels of impeller unbalance were experimentally investigated. Extensive testing of pump performance along with vibration measurements were carried. Both time and frequency domain techniques coupled with principal component analysis (PCA) were used in this evaluation. The effect of unbalance on the pump performance was found to be mainly on the shaft power, while no change in the flow rate and the pump head were observed. As the level of unbalance increased, the power required to operate the pump at the designated speed increased by as much as 12%. The PCA found to be a useful tool in comparing the pump vibrations in the field in order to determine the presence of unbalance as well as the degree of damage. The results of this work can be used to evaluate and monitor pump performance under prescribed degradation in order to enhance preventative maintenance programs.


2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Fengtao Wang ◽  
Xutao Chen ◽  
Bosen Dun ◽  
Bei Wang ◽  
Dawen Yan ◽  
...  

Reliability assessment is a critical consideration in equipment engineering project. Successful reliability assessment, which is dependent on selecting features that accurately reflect performance degradation as the inputs of the assessment model, allows for the proactive maintenance of equipment. In this paper, a novel method based on kernel principal component analysis (KPCA) and Weibull proportional hazards model (WPHM) is proposed to assess the reliability of rolling bearings. A high relative feature set is constructed by selecting the effective features through extracting the time domain, frequency domain, and time-frequency domain features over the bearing’s life cycle data. The kernel principal components which can accurately reflect the performance degradation process are obtained by KPCA and then input as the covariates of WPHM to assess the reliability. An example was conducted to validate the proposed method. The differences in manufacturing, installation, and working conditions of the same type of bearings during reliability assessment are reduced after extracting relative features, which enhances the practicability and stability of the proposed method.


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