Abstract
In this paper, a novel weak fault features extraction scheme is proposed to extract weak fault features in head sheave bearings of floor-type multi-rope friction mine hoists in strong noise environments. A mutual information-based sample entropy (MI-SE) is proposed to select the effective intrinsic mode function (IMF). The numerical simulation presented in this paper has demonstrated that the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) has a poor performance on weak signals processing under a strong noise background, and fault features cannot be identified clearly. The de-noised signal is decomposed into several IMFs by the ICEEMDAN method, with the help of the minimum entropy deconvolution (MED), which works as a pre-filter to increase the kurtosis value by about 3.2 times. The envelope spectrum of the effective IMF selected by the MI-SE method shows almost all fault features clearly. An analogous experiment system was built to verify the feasibility of the proposed scheme, whose results have also shown that the proposed hybrid scheme has better performance compared with ICEEMDAN or MED on the weak fault features extraction under a strong noise background. This paper provides a novel method to diagnose the weak faults of the slow speed and heavy load rolling bearings in a strong noise environment.
Highlights
The head sheave is a critical part of the floor-type multi-rope friction hoist system in coal mines, and its health status has an important influence on the entire performance of the hoisting system.Once a fault occurs, it will cause huge risks and economic losses to the coal mine
Compared with the envelope frequency spectrums obtained by aforementioned methods, the results obtained by using minimum entropy deconvolution (MED)-ICEEMDAN better conformed to the case without noise, and most of the fault features were obtained, which proves that the method proposed in the paper is effective
The assurance, coal mine a large-scale rotating equipment, there aremine stricthoist requirements for security soisitdifficult is is difficult to carry out thefield field experiment onthe the mine head sheave security assurance, so it to carry out the experiment on mine hoist head sheave bearings
Summary
The head sheave is a critical part of the floor-type multi-rope friction hoist system in coal mines, and its health status has an important influence on the entire performance of the hoisting system. EMD is a nonlinear adaptive signal processing method proposed by Huang [6], which can decompose a complex signal into finite transient intrinsic mode functions (IMF) Since it has a good adaptability, it has been widely applied in the field of fault diagnosis [7,8,9]. For the poor performance of ICEEMDAN used to extract the weak fault features of the bearing in strong noise environment, this paper uses the MED method to de-noise the original signal firstly, the pretreated signal is decomposed by ICEEMDAN into a series of IMFs. the effective IMFs are selected based on the proposed MI-SE method, and the fault features are obtained successfully
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