Bearing Early Fault Diagnosis Based on an Improved Multiscale Permutation Entropy and SVM

Abstract

Bearing fault is a process of gradual development and deepening. In the early stage of the fault, if it can be found out in time and taken reasonable prevention and elimination measures, we can avoid serious losses and safety accidents. Therefore, the feature extraction and analysis of early weak fault has important practical significance. In this paper, an improved multiscale permutation entropy (IMPE) method was proposed to overcome the shortcomings in the coarse-grained process. In order to solve the problem that only considering a single coarse-grained sequence under a certain scale may lead to the loss of feature information, this paper proposed to calculate the time series with equal overlapping segments, that was to consider all coarse-grained sequences under the same scale to reflect the feature information of fault signals more comprehensively. In order to solve the problem that feature extraction is not refined enough when using the first-order moment mean value calculation in traditional MPE calculation, a calculation method based on the skewness of the third-order moment was proposed. The calculation method is more sensitive to the complexity and fluctuation of signals and can better describe the feature details and extract the fault features effectively. IMPE was applied to feature extraction of early weak fault of rolling bearing and input into Support Vector Machines (SVMs) for faults classification. Aiming at SVM parameter optimization problem, an improved chaos firefly optimization algorithm was proposed. Experimental results show that the new method of early weak fault identification based on IMPE-SVM was effective in detecting rolling bearing faults with different severity.

Industrial Fault Detection Employing Meta Ensemble Model Based on Contact Sensor Ultrasonic Signal

“…Time-domain features are employed, and a binary logistic regression (BLR) modeling technique is utilized for the fault diagnosis and detection. Similarly, Jiang et al [22] proposed a method for weak rotating machinery fault diagnosis. They introduced a multiscale permutation entropy feature extraction approach, which involves calculating time series with equal overlapping segments.…”

Study on tool wear state monitoring based on EEMD information entropy and PSO-SVM

“…Various types of optimization algorithms, such as grid search (GS) and particle swarm optimization (PSO), have been used to solve the SVM parameter optimization problem [10] . The GS algorithm is unstable enough because it relies on personal experience to set parameters in the optimization process [11] . PSO algorithm constriction speed is fast and with high accuracy, and the second is easy implementation, so it can effectively solve the global optimization problem.…”

Degradation feature extraction of rolling bearing based on equalization symbol sequence entropy

“…In view of the nonlinear, non-stationary and non-periodic character of vibration monitoring signals, in recent years, analysis methods based on information entropy, fractal, chaos, and other complexity theories have been widely applied in the extraction of degradation features for rolling bearings, gearbox, and other rotating machinery. Some degradation features are proposed including fuzzy entropy (Tang and Sun, 2021), permutation entropy (Wang et al, 2022), dispersion entropy (Wang et al, 2021), amplitude spectrum entropy (Chen et al, 2021; Peng et al, 2021), fractal dimension (Du et al, 2021; Xi et al, 2020), and so on.…”