A bearing fault detection and remaining useful life prediction method based on a multi-branch residual feature fusion mechanism and optimized weight allocation

残余物 计算机科学 背景(考古学) 方位(导航) 断层(地质) 特征(语言学) 数据挖掘 人工智能 机器学习 模式识别(心理学) 可靠性工程 算法 工程类 生物 地质学 哲学 古生物学 语言学 地震学
作者
Yiran Yao,Tao Liang,Jianxin Tan,Yanwei Jing
出处
期刊:Measurement Science and Technology [IOP Publishing]
卷期号:35 (2): 025906-025906 被引量:2
标识
DOI:10.1088/1361-6501/ad0b67
摘要

Abstract Within the context of rapidly progressing industrial sectors, rolling bearings have become a fundamental component across an array of mechanical systems. Their fault detection and remaining useful life (RUL) estimations are vital for ensuring industrial production safety. Yet, the understated characteristics of early-stage, minor faults in bearing degradation often escape detection. Additionally, numerous existing networks overlook the critical information embedded in multi-scale features, consequently diminishing the accuracy of predictions and classifications. The present study proposes MM-InfoGAN (multi-branch residual feature fusion and multi-objective optimization information maximization generative adversarial network), an innovative approach for intelligent fault detection and RUL prediction to address these issues. MM-InfoGAN augments the network’s ability to extract bearing fault characteristics and RUL data, employing a multi-branch residual feature fusion network structure coupled with an attention mechanism. Moreover, it refines the weight allocation strategy for geometric loss and introduces a novel loss function. This function optimizes weight distribution during the GAN’s training phase, expediting the attainment of network equilibrium. The efficacy of the comprehensive MM-InfoGAN model and its integrated modules was substantiated through comparative and ablation experiments conducted on the XJTU-SY dataset and IMS Bearing dataset.
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