Redirigiendo al acceso original de articulo en 18 segundos...
Inicio  /  Applied Sciences  /  Vol: 12 Par: 13 (2022)  /  Artículo
ARTÍCULO
TITULO

Bearing Crack Diagnosis Using a Smooth Sliding Digital Twin to Overcome Fluctuations Arising in Unknown Conditions

Farzin Piltan    
Cheol-Hong Kim and Jong-Myon Kim    

Resumen

Bearings cause the most breakdowns in induction motors, which can result in significant economic losses. If faults in the bearings are not detected in time, they can cause the whole system to fail. System failures can lead to unexpected breakdowns, threats to worker safety, and huge economic losses. In this investigation, a new approach is proposed for fault diagnosis of bearings under variable low-speed conditions using a smooth sliding digital twin analysis of indirect acoustic emission (AE) signals. The proposed smooth sliding digital twin is designed based on the combination of the proposed autoregressive fuzzy Gauss?Laguerre bearing modeling approach and the proposed smooth sliding fuzzy observer. The proposed approach has four steps. The AE signals are resampled and the root mean square (RMS) feature is extracted from the AE signal in the first step. To estimate the resampled RMS bearing signal, a new smooth sliding digital twin is proposed in the second step. After that, the resampled RMS bearing residual signal is generated using the difference between the original and estimated signals. Next, a support vector machine (SVM) is proposed for crack detection and crack size identification. The effectiveness of this new approach is evaluated by AE signals provided by our lab?s bearing dataset, where the benchmark dataset consists of one normal and seven abnormal conditions: ball, outer, inner, outer-ball, inner-ball, inner-outer, and inner-outer-ball. The results demonstrated that the average accuracies of the anomaly diagnosis and crack size identification of AE signals for the bearings used in this new smooth sliding digital twin are 97.75% and 97.78%, respectively.

 Artículos similares

       
 
Farzin Piltan and Jong-Myon Kim    
Bearing anomaly recognition using an intelligent digital twin integrated with machine learning.
Revista: Applied Sciences

 
Zhiqiang Zhang, Binke Chen and Qingnan Lan    
A series of model tests were performed to investigate the load-bearing mechanism of a mined railway tunnel lining under water pressure. To investigate the load-bearing characteristics of different types of linings, a fully closed water pressure exerting ... ver más

 
Jiayi Peng, Hao Xu, Hailei Jia, Dragoslav Sumarac, Tongfa Deng, Xin Zhang and Maosen Cao    
Eigen-frequency, compared with mode shape and damping, is a more practical and reliable dynamic feature to portray structural damage. The frequency contour-line method relying on this feature is a representative method to identify damage in beam-type str... ver más
Revista: Applied Sciences

 
Zongyuan Zhang, Hongyuan Fang, Bin Li and Fuming Wang    
Concrete pipes are the most widely used municipal drainage pipes in China. When concrete pipes fall into years of disrepair, numerous problems appear. As one of the most common problems of concrete pipes, cracks impact on the deterioration of mechanical ... ver más
Revista: Applied Sciences

 
Svitlana Shekhorkina,Mykola Savytskyi,Tetiana Nikiforova,Kostiantyn Shliakhov,Anastasiia Myslytska     Pág. 14 - 21
A method has been proposed to calculate the composite timber-concrete bending elements taking into consideration the non-linear work of a nail joint and the stretched reinforcement in a slab. An acting building code regulates the structure estimation bas... ver más