A New Method for Weak Fault Feature Extraction Based on Improved MED

Abstract

Because of the characteristics of weak signal and strong noise, the low-speed vibration signal fault feature extraction has been a hot spot and difficult problem in the field of equipment fault diagnosis. Moreover, the traditional minimum entropy deconvolution (MED) method has been proved to be used to detect such fault signals. The MED uses objective function method to design the filter coefficient, and the appropriate threshold value should be set in the calculation process to achieve the optimal iteration effect. It should be pointed out that the improper setting of the threshold will cause the target function to be recalculated, and the resulting error will eventually affect the distortion of the target function in the background of strong noise. This paper presents an improved MED based method of fault feature extraction from rolling bearing vibration signals that originate in high noise environments. The method uses the shuffled frog leaping algorithm (SFLA), finds the set of optimal filter coefficients, and eventually avoids the artificial error influence of selecting threshold parameter. Therefore, the fault bearing under the two rotating speeds of 60 rpm and 70 rpm is selected for verification with typical low-speed fault bearing as the research object; the results show that SFLA-MED extracts more obvious bearings and has a higher signal-to-noise ratio than the prior MED method.

Multipoint Optimal Minimum Entropy Deconvolution Adjusted for Automatic Fault Diagnosis of Hoist Bearing

“…Figure 10 shows that the characteristic frequency only corresponds to the outer ring fault. So, the fault type is determined as the outer ring fault according to equation (5). Figure 16 shows the processing result of the rolling element fault signal.…”

“…In engineering practice, the hoist bearing has several unique characteristics in comparison to common bearings, such as heavy load (tens or even hundreds of tons), large size (diameter of meters), and low rotating speed (40-60 rpm) [3]. e low-speed and heavy-load characteristics of bearing make the fault signal weak and easily disturbed by noise [4,5]. Moreover, the running state of hoist bearing is often unknown in practice.…”

An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy

“…Hence, unreasonable filter length L will weaken or enhance the energy of the original signal, and then cause misdiagnosis. To address the above-mentioned issues, scholars have introduced some optimization algorithms [ 36 , 37 ] to determine the fault period T and filter length L of MOMEDA, such as particle swarm optimization (PSO) algorithm [ 27 , 38 ], grasshopper optimization algorithm (GOA) [ 39 ] and so on. These parameter optimization algorithms mostly take kurtosis, envelope spectrum kurtosis (ESK) and other similar indexes as objective functions to improve MOMEDA.…”

A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features

“…Cheng et al [35] optimized the filter parameters of MED by particle swarm optimization (PSO) and then successfully extracted fault features by optimized MED. Instance et al [36] optimized the filter size and the number of iterations of MED by shuffled frog leaping algorithm (SFLA), and the method avoids the subjectivity of artificially selected parameters. Miao et al [37] estimated the period of MCKD by calculating the autocorrelation of envelope signals and improved the resampling process of MCKD.…”