Abstract
Electromechanical actuators (EMAs) are more and more widely used as actuation devices in flight control system of aircrafts and helicopters. The reliability of EMAs is vital because it will cause serious accidents if the malfunction of EMAs occurs, so it is significant to detect and diagnose the fault of EMAs timely. However, EMAs often run under variable conditions in realistic environment, and the vibration signals of EMAs are nonlinear and nonstationary, which make it difficult to effectively achieve fault diagnosis. This paper proposed a fault diagnosis method of electromechanical actuators based on variational mode decomposition (VMD) multifractal detrended fluctuation analysis (MFDFA) and probabilistic neural network (PNN). First, the vibration signals were decomposed by VMD into a number of intrinsic mode functions (IMFs). Second, the multifractal features hidden in IMFs were extracted by using MFDFA, and the generalized Hurst exponents were selected as the feature vectors. Then, the principal component analysis (PCA) was introduced to realize dimension reduction of the extracted feature vectors. Finally, the probabilistic neural network (PNN) was utilized to classify the fault modes. The experimental results show that this method can effectively achieve the fault diagnosis of EMAs even under diffident working conditions. Simultaneously, the diagnosis performance of the proposed method in this paper has an advantage over that of EMD-MFDFA method for feature extraction.
Highlights
Most aircrafts and helicopters still adopt hydraulic actuation systems, electromechanical actuators have increasingly been applied as the key actuators for flight control systems of advanced aircrafts and helicopters in recent years
This paper proposed a fault diagnosis method of electromechanical actuators based on variational mode decomposition (VMD) multifractal detrended fluctuation analysis (MFDFA) and probabilistic neural network (PNN)
A fault diagnosis method based on VMD-MFDFA-PNN for electromechanical actuator (EMA) is presented in this study
Summary
Most aircrafts and helicopters still adopt hydraulic actuation systems, electromechanical actuators have increasingly been applied as the key actuators for flight control systems of advanced aircrafts and helicopters in recent years. The commonly used methods for processing vibration signal to extract fault features include short-time Fourier transform (STFT), wavelet transform (WT), empirical mode decomposition (EMD), and local mean decomposition (LMD). LMD is an adaptive time-frequency analysis method which is proposed on the basis of EMD It can decompose the complex signal into several product functions (PFs). VMD transforms signal decomposition into nonrecursive and variational mode decomposition problem which has theoretical foundation It shows better noise robustness and can reduce the sampling effect and modal confusion. Multifractal analysis can extract fractal features of different local scales, and researchers have applied classical multifractal theory to feature extraction of fault diagnosis in recent years. A method based on MFDFA and local characteristic-scale decomposition-Teager energy operator was proposed to realize the fault diagnosis of rolling bearing [15]. PNN model is trained to classify the fault modes
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