Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS

Abstract

Bearing vibration signals typically have nonlinear components due to their interaction and coupling effects, friction, damping, and nonlinear stiffness. Bearing faults affect the signal complexity at various scales. Hence, measuring signal complexity at different scales is helpful to diagnosis of bearing faults. Numerous studies have investigated multiscale algorithms; nevertheless, multiscale algorithms using the first moment lose important complexity data. Accordingly, generalized multiscale algorithms have been recently introduced. The present research examined the use of refined composite generalized multiscale dispersion entropy (RCGMDispEn) based on the second moment (variance) and third moment (skewness) along with refined composite multiscale dispersion entropy (RCMDispEn) in bearing fault diagnosis. Moreover, multiclass FCM-ANFIS, which is a combination of adaptive network-based fuzzy inference systems (ANFIS), was developed to improve the efficiency of rotating machinery fault classification. According to the results, it is recommended that generalized multiscale algorithms based on variance and skewness be examined for diagnosis, along with multiscale algorithms, and be used to achieve an improvement in the results. The simultaneous usage of the multiscale algorithm and generalized multiscale algorithms improved the results in all three real datasets used in this study.

Refined Composite Multiscale Fuzzy Dispersion Entropy and Its Applications to Bearing Fault Diagnosis

“…A local fault in a bearing produces a periodic impact signal that leads to the resonant excitation of the bearing; therefore, it is modulated by the significantly higher resonant frequencies of the bearing [12]. The simulated vibration signal for a bearing of a rotating machine with outer ring damage is defined as follows [8]:…”

“…Hence, their analysis is one of the conventional fault detection methods in rotating machines. Vibration signals generally represent nonlinear behavior due to effects associated with coupling, interactions, friction, damping, and nonlinear stiffness [7,8]. Therefore, the capabilities of linear feature extraction techniques have been limited in fault diagnosis [9], and researchers have focused on detecting nonlinear dynamical characteristics to improve fault diagnosis capabilities [10,11].…”

“…Step ( 4): Time series z m,c j with time delay d and embedding dimension m is constructed according to Equation (8):…”

Condition Monitoring of Rolling Bearing Based on Multi-Order FRFT and SSA-DBN

“…In 2014, a Russian helicopter failed to detect and repair the early engine bearing failure, which eventually led to a crash of the aircraft and personnel were injured or killed. Therefore, the condition monitoring of rolling bearing has extremely important practical significance and engineering application value for ensuring the safe operation of equipment [6].…”

Improved Multivariate Hierarchical Multiscale Dispersion Entropy: A New Method for Industrial Rotating Machinery Fault Diagnosis

“…Comparison of the entropy values of the four threechannel noise signalsFor the value of the embedding dimension m , the class c is set to 6, so the value can only be 2 or 3. For the parameter setting in reference[34]-[35] , this paper determines that the value of each parameter3 m = , 6 c = , 1 d = , 20  = .In this section, synthetic signals are investigated to demonstrate the stability of the improved coarse-grained computation for entropy estimation during feature extraction and the effectiveness of identifying multi-channel signals with different levels of complexity. A three-channel WGN signal, a two-channel WGN signal and a one-channel 1/f noise, a onechannel WGN and a two-channel 1/f noise, a three-channel 1/f noise.Firstly, the paper extracts the features of three methods separately, the results are shown in Fig.3.…”