Abstract
Electrohydrostatic actuator is a type of actuator that uses hydraulic energy as the energy transmission carrier, which has the advantages of small size and high power. Since it is commonly used in harsh conditions such as strong vibration, high pressure, and heavy loads, condition monitoring and fault diagnosis of its hydraulic system are particularly important. This paper proposed a novel fault feature extraction method and applied to fault diagnosis of electrohydrostatic actuator. Firstly, the pressure signal of the hydraulic system is decomposed at multiple scales to obtain the center frequency of its maximum energy intrinsic mode component, and the feature data set is constructed based on the statistical features of the time domain. Then, a fault identification model of hydraulic system based on support vector machine is established. Finally, the fault classification and identification results of the hydraulic system are outputted. After a variety of method comparisons, the method proposed in this paper has a fault time ratio accuracy of 96.7%, which provides a basis and a new way for the fault diagnosis of the hydraulic system.
Highlights
E hydraulic system is the core component of the electrohydrostatic actuator. e failure of the hydraulic system seriously affects the reliability of the electrohydrostatic actuator and has a great impact on the output power quality. e hydraulic system has problems such as fluid leakage, hydraulic valve failure, and insufficient cooling, which are important factors affecting system stability
Zhang et al [6] proposed a rotating machinery fault diagnosis method based on Fourier-transform multifilter decomposition. is method uses Fourier-transform multifilter decomposition to decompose the original signal and obtain multiscale frequency domain information
Hu et al [20] decomposed the children’s EEG into various brain wave components by empirical mode decomposition (EMD), and 11 different physical quantities are extracted as features in the intrinsic mode function (IMF)
Summary
It is a nonrecursive method based on the principle of the variational model. E signal is decomposed into sparse components; at the same time, the center frequency and bandwidth of each IMF are determined in the process of iteratively solving the optimal solution of the constrained variational model. Let input space χ be a subset or discrete set of Euclidean space Rn and feature space (Hilbert space) H, if there is a mapping from χ to H: φ(x): χ ⟶ H Such that for all xi, xj∈χ, the function k (xi, xj) satisfies the following condition: Kxi, xj φ xi · φxj.
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