Rolling bearings are one of the essential components in rotating machinery. Efficient bearing fault diagnosis is necessary to ensure the regular operation of the mechanical system. Traditional fault diagnosis methods usually rely on a complex artificial feature extraction process, which requires a lot of human expertise. Emerging deep learning methods can reduce the dependence of the feature extraction process on manual intervention effectively. However, its training requires a large number of fault signals, which is difficult to obtain in actual engineering. In this paper, a rolling bearing fault diagnosis method based on Convolutional Neural Network and Support Vector Machine is proposed to solve the above problems. Firstly, the Continuous Wavelet Transform is used to convert one-dimensional original vibration signals into two-dimensional time-frequency images. Secondly, the obtained time-frequency images are input for training the constructed model. Finally, the diagnosis of the fault location and severity is completed. The method is verified on the CWRU data set and the MFPT data set. The results demonstrate that the proposed method achieves higher diagnostic accuracy and stability than other advanced techniques.INDEX TERMS Convolutional neural network, continuous wavelet transform, fault diagnosis, rolling bearing, support vector machine.
Purpose
The purpose of this paper is to present a solution for the uncertain fault with the propulsion subsystem of satellite formation, using the Lur’e differential inclusion linear state observers (DILSOs) and fuzzy wavelet neural network (FWNN) to perform fault detection and diagnosis.
Design/methodology/approach
The uncertain fault system cannot be described based on the accurate differential equations. The set-value mapping is introduced into the state equations to solve the problem of uncertainty, but it will cause output uncertainty. The problem can be solved by linearization of Lur’e differential inclusion state observers. The Lur’e DILSOs can be used to detect uncertain fault. The fault isolation and estimation can be performed using the FWNN.
Findings
The mixed approach from fault detection and diagnosis has featured fast and correct to found the uncertain fault. The simulation results to indicate that the methods of design are not only effective but also have the advantages of good approximation effect, fast detection speed, relatively simple structure and prior knowledge and realization of adaptive learning.
Research limitations/implications
The hybrid algorithm can be extensively applied to engineering practice and find uncertain faults of the propulsion subsystem of satellite formation promptly.
Originality/value
This paper provides a fast, effective and simple mixed fault detection and diagnosis scheme for satellite formation.
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