Operation Fault Diagnosis of High-Pressure Grinding Roll Using Adaptive Reconstruction Features with Parameter Optimization

doi:10.12068/j.issn.1005-3026.2025.20240087

Abstract

Abstract:

The operating environment of the high-pressure grinding roll is complicated， and the signal is easily polluted by noise. Traditional algorithms find it difficult to extract the fault characteristics of high-pressure grinding rolls effectively and select the parameters of the stochastic resonance system. To address these issues， an operation fault diagnosis method of high-pressure grinding roll based on adaptive reconstruction features with parameter optimization was proposed. First， the ensemble empirical mode decomposition （EEMD） method was employed to decompose the high-pressure grinding roll’s vibration signal into several intrinsic mode function （IMF） components. Secondly， the mixed criterion of correlation coefficient and mutual information was used to adaptively screen the component signals with the strongest abnormal operation characteristics and reconstruct them. Then， the salp swarm algorithm （SSA） was introduced to build the adaptive stochastic resonance （SR） parameter optimization mechanism by combining the population probabilistic mutation mechanism. Finally， an operation fault diagnosis algorithm of high-pressure grinding roll based on an adaptively selected component reconstruction signal was proposed. Simulation results verify the effectiveness of the proposed method.

Key words: fault diagnosis, ensemble empirical mode decomposition, salp swarm algorithm, stochastic resonance, adaptive strategy

CLC Number:

TD 50

Hong-shuo SUN, Dan-wei ZHANG, Quan XU, Tian-you CHAI. Operation Fault Diagnosis of High-Pressure Grinding Roll Using Adaptive Reconstruction Features with Parameter Optimization[J]. Journal of Northeastern University(Natural Science), 2025, 46(11): 1-11.

Figures/Tables 13

Fig.1 Fault diagnosis flowchart

Fig.2 Signal spectrum diagram

Fig.3 IMF component diagram obtained from EEMD of original simulation signal

Fig.4 Discriminant index R(p) of IMF component signal

Fig.5 Time-domain waveform and spectrum diagrams of reconstructed signal through bistable stochastic resonance system

Fig.6 Installation position and acquisition device of vibration signal sensor for high-pressure grinding roll

Fig.7 Spectrum diagram of vibration signal of high-pressure grinding roll

Fig.8 IMF component diagram of original vibration signal after EEMD

Fig.9 IMF component signal discrimination index R(p) of actual vibration signal

Fig.10 Time-domain waveform and spectrum diagrams of ant colony-optimized stochastic resonance system

Fig.11 Time-domain waveform and spectrum diagrams of particle swarm-optimized stochastic resonance system

Fig.12 Time-domain waveform and spectrum diagrams of optimized stochastic resonance system in this paper

Table 1 Comparison results of three methods

方法

寻优

次数

初始种

群数/个

时间/s

特征

频率/Hz

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