On basis of vibration images and transfer learning, this work presents a method for fault diagnosis of rotating machinery. Vibration images can be regarded as a combination of several traditional single-point sensors, which provide abundant full-field vibration information. The frequency-domain features of vibration acceleration are extracted according to the motion phase in images. Then proposed method utilizes a transfer learning method, called joint distribution adaptation, to construct a shared feature space. In this space, the marginal probability distribution and conditional distribution of vibration signals in two different conditions are minimized. Afterwards, the k-Nearest Neighbor classifier is used in this space for fault diagnosis. Experimental results manifest the proposed method can effectively diagnose faults under different working conditions and between different machineries, and the diagnosis accuracy is above 90%.
Multi-condition Fault Diagnosis for Rotating Machinery Using Vibration Images and Joint Distribution Adaptation
Lect. Notes Electrical Eng.
2021-10-30
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Fault Diagnosis of Rotating Machinery Using Back Propagation Neural Network
British Library Online Contents | 1997
|Fault features of large rotating machinery and diagnosis using sensor fusion
Online Contents | 1995
|Fault diagnosis of rotating machinery using an intelligent order tracking system
Online Contents | 2005
|Vibration analysis of rotating machinery using the spectral distribution function
Online Contents | 1998
|Selection of wavelet packet basis for rotating machinery fault diagnosis
Online Contents | 2005
|