Application of a New Enhanced Deconvolution Method in Gearbox Fault Diagnosis

Junyuan Wang,Jingtai Wang,Guanjun Wang,Tao Li,Zhijian Wang,Wenhua Du,Jiping Zhang

doi:10.3390/app9245313

Abstract

When the mechanical transmission mechanism fails, such as gears and bearings in the gearbox, its vibration signal often appears as a periodic impact. Considering the influence of noise, however, the fault signal is often submerged in the noise, so it is necessary to propose a feasible and effective fault extraction method. MOMEDA (multipoint optimal minimum entropy deconvolution adjusted) overcomes the tedious iterative process of MED (minimum entropy deconvolution) and overcomes the resampling trouble in MCKD (maximum correlated kurtosis deconvolution). It is suitable for dealing with periodic impact signal. Besides, aiming at the poor ability of MOMEDA to capture the deconvolution result of target function in a strong noise environment, this paper proposes an improved MOMEDA gearbox fault feature extraction method. Considering that MOMEDA has poor anti-noise performance and can easily cause misdiagnosis in a strong noisy environment, this paper constructs an autoregressive mean sliding model to improve the noise immunity of MOMEDA. Firstly, the stability of the test signal is judged by the autocorrelation coefficient (ACF) and the partial correlation coefficient (PACF). Secondly, the ARMA (autoregressive moving average) model is constructed and a set of optimal model coefficients are obtained to filter the signal, which greatly improves MOMEDA’s ability to capture fault features. Thirdly, the fault feature is extracted by MOMEDA, and the fault information is extracted accurately under a strong noise environment. Finally, compared with AR-MED, ARMAMED, and other methods, the advantages of ARMAMOMEDA are verified. Moreover, the effectiveness and superiority of the proposed method are verified by simulation signals and experimental data from the Case Western Reserve University Bearing Data Center.

Highlights

When bearing gear, inner ring, outer ring, or rolling element faults occur, the transmission system will be affected and the vibration signal will appear periodically impacted [1,2,3,4,5]
Cannot identify the fault feature in the noisy the ARMA-Multipointoptimal optimal minimum entropy entropy deconvolution adjusted (MOMEDA) method proposed in this paper is used to extract the gearbox fault to verify environment, component of the characterizing bearing is less the rationalityand of the thisperiodic method.impact cannot identify the fault fault feature in correlated the noisy with other components in signal, the model estimated by the autocorrelation function is used environment, and the periodic impact component of the characterizing bearing fault is less correlated for filtering, the periodic impact component in the fault signal can be effectively separated from with other components in signal, the ARMA model estimated by the autocorrelation function is other used unrelated components
This paper proposes an ARMA-MOMEDA method that was successfully applied to gearbox

Summary

Introduction

Inner ring, outer ring, or rolling element faults occur, the transmission system will be affected and the vibration signal will appear periodically impacted [1,2,3,4,5]. When the bearing faults occur, the background noise becomes loud owing to the gearbox harsh working environment [6,7,8,9,10]. The weak fault signal is often overwhelmed by noise, and it is difficult to extract the characteristic information. The extraction of the fault information is to weaken the noise in the collected vibration signals through an optimal filter, which effectively preserves the integrity of the fault.

Methods

Results

Conclusion