An Integrated Approach Based on Swarm Decomposition, Morphology Envelope Dispersion Entropy, and Random Forest for Multi-Fault Recognition of Rolling Bearing

Abstract

Aiming at the problem that the weak faults of rolling bearing are difficult to recognize accurately, an approach on the basis of swarm decomposition (SWD), morphology envelope dispersion entropy (MEDE), and random forest (RF) is proposed to realize effective detection and intelligent recognition of weak faults in rolling bearings. The proposed approach is based on the idea of signal denoising, feature extraction and pattern classification. Firstly, the raw signal is divided into a group of oscillatory components through SWD algorithm. The first component has the richest fault information and perceived as the principal oscillatory component (POC). Secondly, the MEDE value of the POC is calculated and used to describe the characteristics of signal. Ultimately, the obtained MEDE values of various states are trained and recognized by being input as the feature vectors into the RF classifier to achieve the automatic identification of rolling bearing fault under different operation states. The dataset of Case Western Reserve University is conducted, the proposed approach achieves recognition accuracy rate of 100%. In summary, the proposed approach is efficient and robust, which can be used as a supplement to the rolling bearing fault diagnosis methods.

Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine

“…A value of 𝛽=0.005 which causes the smallest reasonable, 𝑀 is preferred [48]. In order to determine appropriate values of these parameters according to different signal types, the following criteria is followed:…”

EV Fleet Charging Load Forecasting Based on Multiple Decomposition With CEEMDAN and Swarm Decomposition

“…Herein, the weighted factor, β, is preferred to be a small value, i.e., 0.005 [26]. In order to select the optimal values of vital parameters of SWD, δ and M , the following criterion is followed in (15).The main purpose of the SWF process is to search for the parameters.…”

Threshold‐Optimized Swarm Decomposition Using Grey Wolf Optimizer for the Acoustic‐Based Internal Defect Detection of Arc Magnets

“…SWD has attracted increasing attention in some studies and has been utilized to cope with different kinds of signals, such as vibrational, biological, and electrical signals. For example, Wan and Peng [14] adopted SWD to analyze the bearing signal and realized a fault diagnosis of the rotating bearing.…”