Abstract
Rolling bearings are a vital and widely used component in modern industry, relating to the production efficiency and remaining life of a device. An effective and robust fault diagnosis method for rolling bearings can reduce the downtime caused by unexpected failures. Thus, a novel fault diagnosis method for rolling bearings by fine-sorted dispersion entropy and mutation sine cosine algorithm and particle swarm optimization (SCA-PSO) optimized support vector machine (SVM) is presented to diagnose a fault of various sizes, locations and motor loads. Vibration signals collected from different types of faults are firstly decomposed by variational mode decomposition (VMD) into sets of intrinsic mode functions (IMFs), where the decomposing mode number K is determined by the central frequency observation method, thus, to weaken the non-stationarity of original signals. Later, the improved fine-sorted dispersion entropy (FSDE) is proposed to enhance the perception for relationship information between neighboring elements and then employed to construct the feature vectors of different fault samples. Afterward, a hybrid optimization strategy combining advantages of mutation operator, sine cosine algorithm and particle swarm optimization (MSCAPSO) is proposed to optimize the SVM model. The optimal SVM model is subsequently applied to realize the pattern recognition for different fault samples. The superiority of the proposed method is assessed through multiple contrastive experiments. Result analysis indicates that the proposed method achieves better precision and stability over some relevant methods, whereupon it is promising in the field of fault diagnosis for rolling bearings.
Highlights
Rolling bearings are a crucial part in modern industrial manufacture, which can be found in linear guides, precision machine tools, and engine parts, etc., whose failure may result in serious safety accidents and economic loss
Due to the rich information carried by vibration signal, most of fault diagnosis methods for rolling bearings rely on analyzing vibration signal [5]
Support vector machine (SVM) is a machine learning model developed by Vapnik [30], which can deduce the optimal solution between model complexity and learning ability based on limited information, to obtain the best classification accuracy, showing unique advantages in solving learning problems with limited samples, non-linear, and high dimensional data
Summary
Rolling bearings are a crucial part in modern industrial manufacture, which can be found in linear guides, precision machine tools, and engine parts, etc., whose failure may result in serious safety accidents and economic loss. EMD is adept in dealing with non-stationary signals and does not need to pre-set any basis function [7], but its performance is affected by end effects and mode mixing To overcome these defects, as an improved version of EMD, EEMD was proposed by introducing noise-assisted analysis method [8], but it increases the computational cost and cannot completely neutralize the added noise. As a feature extraction method based on non-linear dynamic parameters, is widely applied in medical and mechanical fields, which can reflect the complexity of time series and is an effective tool for analysis of non-stationary time series.
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