CEEMDAN‐Based Permutation Entropy: A Suitable Feature for the Fault Identification of Spiral‐Bevel Gears

Abstract

A spiral-bevel gear is a basic transmission component and is widely used in mechanical equipment; thus, it is important to monitor and diagnose its running state to ensure safe operation of the entire equipment setup. The vibration signals of spiral-bevel gears are typically quite complicated, as they present both nonlinear and nonstationary characteristics and are interfered with by strong noise. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method has been proven to be an effective method for analyzing this kind of signal. However, the fault feature information after CEEMDAN is not obvious and needs to be quantified. Permutation entropy can be used to quantify the randomness, complexity, and mutation of vibration time-series signals. This paper proposes to take the CEEMDAN-based permutation entropy as the sensitive feature for spiral-bevel gear fault identification. First, the raw vibration signal is decomposed by the CEEMDAN method to obtain a series of intrinsic modal functions (IMFs). The IMFs which included greater amounts fault information are selected as the optimal IMFs based on the correlation coefficient. Next, the permutation entropy values of the optimal IMFs are calculated. In order to obtain accurate permutation entropy values, the two key parameters, namely, embedding dimension and delay time, are optimized by using the overlapping parameter method. In order to assess the sensibility of the permutation entropy features, the support vector machine (SVM) is used as the classifier for fault mode identification, and the diagnostic accuracy can verify its sensibility. The permutation entropy of CEEMDAN-based/EEMD-based/EMD-based features, combined with SVM, is applied to identify three different fault modes of spiral-bevel gears. Their respective diagnostic accuracies are 100%, 88.33%, and 83.33%, which indicate that the CEEMDAN-based permutation entropy is the most sensitive feature for the fault identification of spiral-bevel gears.

Multiscale Entropy Feature Extraction Method of Running Power Equipment Sound

“…For comparison, the same experimental data were analyzed using the feature extraction method in [ 48 , 55 ]. The feature extraction results of MIPE [ 48 ] are shown in Figure 12 a, where the mean value of IPE and its standard deviation error is drawn.…”

“…The reason lies in the lack of noise elimination before the MIPE calculation, and the results are influenced by the noise, which becomes larger with the coarse-graining process. The analysis results of CEEMDAN-PE [ 55 ] are demonstrated in Figure 12 b, where the abscissa represents the different types of samples and the ordinate represents the PE value. The parameters of the algorithm were set as same as those in [ 55 ].…”

“…The analysis results of CEEMDAN-PE [ 55 ] are demonstrated in Figure 12 b, where the abscissa represents the different types of samples and the ordinate represents the PE value. The parameters of the algorithm were set as same as those in [ 55 ]. Because CEEMDAN-PE was based on a single scale, the relationship between entropy and time scale was discarded.…”

Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines

“…This feature extraction technique was also used for general bogie assessment in [ 4 ]. A further development of EMD, called Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), has been recently used for gear diagnosis in [ 14 ] and for feature extraction of underwater acoustic signals in [ 15 ].…”

“…Depending on the number of features and how they change with the presence of the fault, complex models can be proposed. In recent years, complex intelligent classification systems have been widely used, such as Neural Networks (NNs) for axles in [ 5 , 19 ], Support Vector Machines (SVMs) for predicting rail track degradation in [ 14 , 22 ], and to diagnose axle bearings in [ 23 ].…”