scholarly journals Multifractal manifold for rotating machinery fault diagnosis based on detrended fluctuation analysis

2016 ◽  
Vol 18 (8) ◽  
pp. 5153-5173 ◽  
Author(s):  
Baochun Lu ◽  
Yi Feng ◽  
Dengfeng Zhang
Keyword(s):  
Fault Diagnosis ◽  
Detrended Fluctuation Analysis ◽  
Rotating Machinery ◽  
Fluctuation Analysis ◽  
Detrended Fluctuation ◽  
Machinery Fault Diagnosis

scholarly journals Alpha-Stable Distribution and Multifractal Detrended Fluctuation Analysis-Based Fault Diagnosis Method Application for Axle Box Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Qing Xiong ◽  
Weihua Zhang ◽  
Yanhai Xu ◽  
Yiqiang Peng ◽  
Pengyi Deng
Keyword(s):  
Fault Diagnosis ◽  
Detrended Fluctuation Analysis ◽  
Stable Distribution ◽  
Integration Scheme ◽  
Railway Vehicle ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Alpha Stable Distribution ◽  
Diagnosis Method ◽  
Detrended Fluctuation

A railway vehicle’s key components, such as wheelset treads and axle box bearings, often suffer from fatigue failures. If these faults are not detected and dealt with in time, the running safety of the railway vehicle will be seriously affected. To detect these components’ early failure and extend their fatigue life, a regular maintenance becomes critical. Currently, the regular maintenance of axle box bearings mainly depends on manual off-line inspection, which has low working efficiency and precision of fault diagnosis. In order to improve the maintenance efficiency and effectiveness of railway vehicles, this study proposes a method of integrating the vibration monitoring system of the axle box bearing in the underfloor wheelset lathe, where the integration scheme and work flow of the system are introduced followed by the detailed fault diagnosis method and application examples. Firstly, the band-pass filter and envelope analysis is successively performed on the original signal acquired by an accelerometer. Secondly, the alpha-stable distribution (ASD) and multifractal detrended fluctuation analysis (MFDFA) analysis of the envelope signal are performed, and five characteristic parameters with significant stability and sensitivity are extracted and then brought into the least squares support vectors machine based on particle swarm optimization to determine the state of the bearing quantitatively. Finally, the effectiveness of the method is validated by bench test data. The results demonstrated that the proposed method can accomplish effective diagnosis of axle box bearings’ fault location and fault degree and can yield better diagnosis accuracy than the single method of ASD or MFDFA.

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

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Hongmei Liu ◽  
Jiayao Jing ◽  
Jian Ma
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Detrended Fluctuation Analysis ◽  
Probabilistic Neural Network ◽  
Fluctuation Analysis ◽  
Electromechanical Actuators ◽  
Vibration Signals ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Feature Vectors ◽  
Detrended Fluctuation

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.

Applications of detrended-fluctuation analysis to gearbox fault diagnosis

2009 ◽  
Vol 23 (3) ◽  
pp. 682-689 ◽  
Author(s):  
E.P. de Moura ◽  
A.P. Vieira ◽  
M.A.S. Irmão ◽  
A.A. Silva
Keyword(s):  
Fault Diagnosis ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Detrended Fluctuation
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