Abstract
Feature extraction is one of the most important, pivotal, and difficult problems in mechanical fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of early fault prediction. Therefore, a new fault feature extraction method, called the EDOMFE method based on integrating ensemble empirical mode decomposition (EEMD), mode selection, and multi-scale fuzzy entropy is proposed to accurately diagnose fault in this paper. The EEMD method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs) with a different physical significance. The correlation coefficient analysis method is used to calculate and determine three improved IMFs, which are close to the original signal. The multi-scale fuzzy entropy with the ability of effective distinguishing the complexity of different signals is used to calculate the entropy values of the selected three IMFs in order to form a feature vector with the complexity measure, which is regarded as the inputs of the support vector machine (SVM) model for training and constructing a SVM classifier (EOMSMFD based on EDOMFE and SVM) for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by real bearing vibration signals of the motor with different loads and fault severities. The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and that the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outer race fault, and rolling element fault of the motor bearing. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery.
Highlights
Rolling bearing is one of the most important parts of rotating machinery
In order tofault verify the effectiveness of the proposed method forand different fault severities, severities, the types and fault severities of the inner race, the outer race, the rolling element the fault types and fault severities of the inner race, the outer race, and the rolling element the of the motor bearing are diagnosed
To perfectly extract the multiple scale characteristics of the vibration signal of the motor bearing and well diagnose its faults, a novel feature extraction (EDOMFE) method is proposed in this paper
Summary
Rolling bearing is one of the most important parts of rotating machinery. Its operation state directly determines whether the whole machine is safe, efficient, and reliable. Due to the influences of transferring load and the additional load by the gear meshing in motor bearing, the fault rate of rolling bearing is always high without decreasing, and even shows an upward trend sometimes. It is becoming increasingly important to improve the reliability of rolling bearings and to accurately detect bearing faults in time. When motor bearing faults occur, the periodic pulse impact force is generated, the nonlinear vibration of the mechanical system causes a collected vibration signal. The fault will rapidly develop and the collected vibration signals often contain a large amount of noise. The early fault features of the bearing in the vibration signal is relatively weak, which overwhelms the signal due to the noise. Accurately extracting fault features from the motor bearing and successfully separating the fault mode has been a serious and difficult problem
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