基于粒子群优化算法优化的新型堆叠迁移自动编码器实现不同旋转机械的智能故障诊断

Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO.

作者信息

Haidong Shao, Ziyang Ding, Junsheng Cheng, Hongkai Jiang

机构信息

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China; Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Luleå 97187, Sweden.

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.

出版信息

ISA Trans. 2020 Oct;105:308-319. doi: 10.1016/j.isatra.2020.05.041. Epub 2020 May 26.

DOI:10.1016/j.isatra.2020.05.041

PMID:32473735

Abstract

Intelligent fault diagnosis techniques cross rotating machines have great significances in theory and engineering For this purpose, this paper presents a novel method using novel stacked transfer auto-encoder (NSTAE) optimized by particle swarm optimization (PSO). First, novel stacked auto-encoder (NSAE) model is designed with scaled exponential linear unit (SELU), correntropy and nonnegative constraint. Then, NSTAE is constructed using NSAE and parameter transfer strategy to enable the pre-trained source-domain NSAE to adapt to the target-domain samples. Finally, PSO is used to flexibly decide the hyperparameters of NSTAE. The effectiveness and superiority of the presented method are investigated through analyzing the collected experimental data of bearings and gears from different rotating machines.

摘要

智能故障诊断技术在旋转机械领域具有重要的理论和工程意义。为此，本文提出了一种采用粒子群优化（PSO）优化的新型堆叠转移自动编码器（NSTAE）的新方法。首先，采用缩放指数线性单元（SELU）、核相关熵和非负约束设计了新型堆叠自动编码器（NSAE）模型。然后，利用NSAE和参数转移策略构建NSTAE，使预训练的源域NSAE能够适应目标域样本。最后，使用PSO灵活确定NSTAE的超参数。通过分析从不同旋转机械收集的轴承和齿轮实验数据，研究了所提方法的有效性和优越性。