Abstract
The vibration signals resulting from rolling bearings are nonlinear and nonstationary, and an approach for the fault diagnosis of rolling bearings using the quantile permutation entropy and EMD (empirical mode decomposition) is proposed. Firstly, the EMD is used to decompose the rolling bearings vibration signal, and several IMFs (intrinsic mode functions) spanning different scales are obtained. Secondly, aiming at the shortcomings of the permutation entropy algorithm, a new permutation entropy algorithm based on sample quantile is proposed, and the quantile permutation entropy of the first few IMFs, which contain the main fault information, is calculated. The quantile permutation entropies are accordingly seen as the characteristic vector and then input to the particle swarm optimization and support vector machine. Finally, the proposed method is applied to the experimental data. The analysis results show that the proposed approach can effectively achieve fault diagnosis of rolling bearings.
Highlights
Rolling bearings are the most frequently used component in a rotary machines, which play an essential role in the modern industry [1]
On the basis of permutation entropy (PE), this paper introduces the concept of sample quantile and defines sample quantile PE (SQPE)
In order to validate the capability of the empirical mode decomposition (EMD)-SQPE-PSOSVM method, experimental analyses on rolling bearing faults were conducted. e 6205-2RS JEM SKF deep groove ball bearing was used in the experimental, and single point faults were introduced to the test rolling bearings using electrodischarge machining with fault diameters of 0.3556 mm [24]. e shaft rotating speeds of the bearing is 1772 r/min, and the sampling frequency is 12000 Hz
Summary
Rolling bearings are the most frequently used component in a rotary machines, which play an essential role in the modern industry [1]. All of the classical nonlinear theory have made some accomplishments to fault diagnosis These algorithms often require a lot of time, and it is vulnerable to noise interference in the application process. E application of entropy theory provides a new idea for rolling bearing fault diagnosis. PE is a new method proposed for detecting the randomicity and dynamic changes of time series, which can be used in the field of fault diagnosis. In order to adaptively complete the embedding dimension selection problem and improve the feature extraction ability of PE, a new kind of entropy named sample quantile PE (SQPE) is defined by combining sample quantile and PE in this paper. Compared with the traditional feature extraction method, this paper solves the problem of embedding dimension selection in the PE calculation process by SQPE entropy calculation and improves the feature extraction ability of PE at the same scale.
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