The Research of Machinery Fault Feature Extraction Methods Based On Vibration Signal
Sign Up to like & getrecommendations! Published in 2018 at "IFAC-PapersOnLine"
DOI: 10.1016/j.ifacol.2018.08.202
Abstract: Abstract The vibration signal of machinery fault is complex, performing non-stationary characteristic. The classical filter can not extract low frequency impulse from the vibration signal. In this paper the machinery fault feature extraction methods will… read more here.
Keywords: vibration; feature extraction; machinery fault; vibration signal ... See more keywords
Deep learning-based adversarial multi-classifier optimization for cross-domain machinery fault diagnostics
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Manufacturing Systems"
DOI: 10.1016/j.jmsy.2020.04.017
Abstract: Abstract Despite the recent success in data-driven machinery fault diagnosis, cross-domain diagnostic tasks still remain challenging where the supervised training data and unsupervised testing data are collected under different operating conditions. In order to address… read more here.
Keywords: adversarial multi; cross domain; domain; machinery fault ... See more keywords
A systematic review of deep transfer learning for machinery fault diagnosis
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.04.045
Abstract: Abstract With the popularization of the intelligent manufacturing, much attention has been paid in such intelligent computing methods as deep learning ones for machinery fault diagnosis. Thanks to the development of deep learning models, the… read more here.
Keywords: transfer learning; machinery fault; deep transfer; fault diagnosis ... See more keywords
A novel deep autoencoder feature learning method for rotating machinery fault diagnosis
Sign Up to like & getrecommendations! Published in 2017 at "Mechanical Systems and Signal Processing"
DOI: 10.1016/j.ymssp.2017.03.034
Abstract: Abstract The operation conditions of the rotating machinery are always complex and variable, which makes it difficult to automatically and effectively capture the useful fault features from the measured vibration signals, and it is a… read more here.
Keywords: deep autoencoder; rotating machinery; machinery fault;
Adaptive Mode Decomposition Methods and Their Applications in Signal Analysis for Machinery Fault Diagnosis: A Review With Examples
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Access"
DOI: 10.1109/access.2017.2766232
Abstract: Effective signal processing methods are essential for machinery fault diagnosis. Most conventional signal processing methods lack adaptability, thus being unable to well extract the embedded meaningful information. Adaptive mode decomposition methods have excellent adaptability and… read more here.
Keywords: machinery fault; mode decomposition; adaptive mode; fault diagnosis ... See more keywords
Sparsity-Constrained Invariant Risk Minimization for Domain Generalization With Application to Machinery Fault Diagnosis Modeling.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2022.3223783
Abstract: Machine learning has been widely applied to study AI-informed machinery fault diagnosis. This work proposes a sparsity-constrained invariant risk minimization (SCIRM) framework, which develops machine-learning models with better generalization capacities for environmental disturbances in machinery… read more here.
Keywords: machinery fault; fault diagnosis; sparsity; machinery ... See more keywords
Reconstruction Domain Adaptation Transfer Network for Partial Transfer Learning of Machinery Fault Diagnostics
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2021.3129213
Abstract: In industrial applications of machinery fault diagnostics, transfer learning is often used to transfer the knowledge learned from the source domain including labeled data to the target domain containing unlabeled data. This method follows the… read more here.
Keywords: domain; machinery fault; domain adaptation; transfer ... See more keywords
Adaptive machinery fault diagnosis based on improved shift-invariant sparse coding
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Vibroengineering"
DOI: 10.21595/jve.2017.17574
Abstract: In machinery fault diagnosis, it is common that one kind of fault may correspond to several conditions, these conditions may contain different loads, different speeds and so on. When using conventional intelligent machinery fault diagnosis… read more here.
Keywords: fault; machinery fault; shift invariant; fault diagnosis ... See more keywords