Nonlinear Analog Circuit Fault Diagnosis Based on MFDFA Method

Tang, Jing; Chen, Jian Ming; Cai, Zhang

doi:10.4028/www.scientific.net/amm.263-266.108

Cited by 1 publication

(1 citation statement)

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“…However, the traditional multifractal theory can be easily disturbed by the trend of signal fluctuation and cannot reveal the multifractal characteristics hidden in nonstationary signal accurately. [16].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Electromechanical Actuator Based on VMD Multifractal Detrended Fluctuation Analysis and PNN

Liu

Jing

2018

Complexity

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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.

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Section: Introductionmentioning

confidence: 99%