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Multi-branch convolutional neural network with generalized shaft orbit for fault diagnosis of active magnetic bearing-rotor system
- Source :
- Measurement. 171:108778
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Fault diagnosis based on vibration signals in active magnetic bearing-rotor systems is an important research topic. However, it is difficult to obtain discriminative features to represent faults due to the nonlinear and non-stationary characteristics of the vibration signals and diverse sources of failures. Hence, this paper proposes a novel end-to-end learning mechanism of multi-sensor data fusion to learn fault representation based on the structural characteristics of active magnetic bearings. Taking the five displacement sensors of active magnetic bearing as signal sources, generalized shaft orbits are constructed and converted into discrete 2D images. Based these 2D images, a multi-branch convolutional neural network is designed to achieve high discriminative features and fault types. The experiments are performed on the rig supported by active magnetic bearings, and the effectiveness of the proposed algorithm is verified, proving it suitability in cases with changing rotating speeds and sample lengths.
- Subjects :
- Computer science
Applied Mathematics
020208 electrical & electronic engineering
010401 analytical chemistry
Magnetic bearing
02 engineering and technology
Condensed Matter Physics
Sensor fusion
Fault (power engineering)
01 natural sciences
Signal
Convolutional neural network
Displacement (vector)
0104 chemical sciences
law.invention
law
Control theory
0202 electrical engineering, electronic engineering, information engineering
Orbit (dynamics)
Electrical and Electronic Engineering
Helicopter rotor
Instrumentation
Subjects
Details
- ISSN :
- 02632241
- Volume :
- 171
- Database :
- OpenAIRE
- Journal :
- Measurement
- Accession number :
- edsair.doi...........e446a336594faa38e187b8789fc25085