Agent discriminate model based optimization weighted method and its application in fault diagnosis of rolling bearings

Abstract

In the fault diagnosis of rolling bearing, the vibration signals, which are collected from the field test, are often more complex because they unavoidably contain various noises and measurement errors, so ‘outliers’ may occur in the features extracted from the collected vibration signals. Aiming at the above problems, the agent discriminate model based optimization weighted (ADMOW) method is proposed. By using the entropy weight method (EWM), the entropy weights of the sample features are calculated first, and the features are then weighted to weaken the influence of ‘outliers’ on the modeling. Secondly, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of the model, and a more accurate classification model is obtained. Eventually, ADMOW is applied to recognize defaults of rolling bearings. The test results indicate that by comparing several pattern recognition methods, the proposed method can effectively weaken the influence of ‘outliers’ and improve the recognition rate and the recognition accuracy.

Characteristic enhancement of RV reducer under non-stationary condition based on WDT-IF-SS-VMD

“…They are similar decomposition calculations, EEMD and CEEMDAN. As well as the traditional noise reduction method, improved maximum correlation kurtosis deconvolution (IMCKD) [30] and maximum cyclostationarity blind deconvolution (CYCBD) [31]. Additionally, an unoptimized VMD was added to verify the necessity of optimizing the number of layers.…”

Interpretability of deep convolutional neural networks on rolling bearing fault diagnosis

“…Furthermore, different kinds of deep neural network-based diagnosis methods using pattern recognition have been attracting increasing research attention due to the ability to fully capture the underlying relationships between the collected raw data and machine health conditions [25][26][27][28].…”