A New Transfer Learning Ensemble Model with New Training Methods for Gear Wear Particle Recognition

Chunhua Zhao,Qian Li,Jinling Tan,Hengxing Hu,Zhangwen Lin,Yi Qin

doi:10.1155/2022/3696091

Abstract

Aiming at solving the acquisition problems of wear particle data of large-modulus gear teeth and few training datasets, an integrated model of LCNNE based on transfer learning is proposed in this paper. Firstly, the wear particles are diagnosed and classified by connecting a new joint loss function and two pretrained models VGG19 and GoogLeNet. Subsequently, the wear particles in gearbox lubricating oil are chosen as the experimental object to make a comparison. Compared with the other four models’ experimental results, the model superiority in wear particle identification and classification is verified. Taking five models as feature extractors and support vector machines as classifiers, the experimental results and comparative analysis reveal that the LCNNE model is better than the other four models because its feature expression ability is stronger than that of the other four models.

Highlights

As a key component of special equipment such as ship lift equipment and lifting platform equipment, large-modulus gear racks and pinions will cause huge losses if they fail. Because they are running at low speed and heavy load, it is difficult to diagnose them with conventional fault diagnosis methods, and the lubrication system of large-modulus rack and pinion transmission contains a lot of wear fault information [1, 2]. erefore, ferrographic analysis is a method of monitoring the health of the machine by observing the material, size, special diagnosis, and quantity of the wear particles, which can be used for the fault diagnosis of largemodulus rack and pinion
A deep learning model based on migration learning is proposed. e model trained on other large datasets is replaced with a wear particle image dataset and fine-tuned to meet the requirements of automatic wear particle images of small samples [12]. e image recognition and
Our method includes the following steps: (1) the pretrained VGG19 and GoogLeNet were fine-tuned for the gear abrasive images; (2) loss function convolutional neural network ensemble (LCNNE) model was initialized with the weight and biases from fine-tuned networks; (3) the optimization algorithm AdamW was used to fine-tune the LCNNE model for the gear abrasive images; (4) the fine-tuned LCNNE model can be used as a classifier by itself or as a feature extractor for an external classifier. e contributions of this paper are summarized below: (1) We proposed a novel and simple learning framework that processes raw wear particle images directly

Summary

Introduction

As a key component of special equipment such as ship lift equipment and lifting platform equipment, large-modulus gear racks and pinions will cause huge losses if they fail Because they are running at low speed and heavy load, it is difficult to diagnose them with conventional fault diagnosis methods, and the lubrication system of large-modulus rack and pinion transmission contains a lot of wear fault information [1, 2]. For a small sample of wear particle datasets, the performance degradation of the intelligent fault diagnosis method is very serious To this end, a deep learning model based on migration learning is proposed. A deep learning model based on migration learning is proposed. e model trained on other large datasets is replaced with a wear particle image dataset and fine-tuned to meet the requirements of automatic wear particle images of small samples [12]. e image recognition and

Objectives

Methods

Results