Shock and Vibration

Intelligent Feature Learning Methods for Machine Condition Monitoring


Publishing date
01 Mar 2021
Status
Closed
Submission deadline
23 Oct 2020

Lead Editor

1Soochow University, Suzhou, China

2Lancaster University, Lancaster, UK

3University of Nebraska-Lincoln, Lincoln, USA

4Shanghai Jiaotong University, Shanghai, China

This issue is now closed for submissions.
More articles will be published in the near future.

Intelligent Feature Learning Methods for Machine Condition Monitoring

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Massive data are being collected in various industries to monitor the health conditions of mechanical and electrical equipment. Mechanical signals including vibration signals, acoustic signals, images, etc., are sensitive to abnormal/fault conditions, which usually show the characteristics of impulsive transients. However, these repetitive transients are typically weak, especially when the equipment starts its fault at the initial stage. Moreover, environmental noises cause further interference for extracting fault information.

Traditional signal processing methods can somehow handle the above challenges with proper design of filtering, artificial feature extraction, and fault monitoring and detection. However, these steps usually require significant any human efforts and they cannot be easily extended to solve new problems. To overcome the aforementioned difficulties, artificial intelligence-based methods such as deep learning can have the potential to transform machine monitoring towards an automatic and smart direction.

The aim of this Special Issue is to promote intelligent condition monitoring, and act as a platform to present high-quality original research on the latest developments of condition monitoring methods. We welcome both original research articles and review articles discussing the current state of the art.

Potential topics include but are not limited to the following:

  • Deep learning-based fault diagnosis and prognosis
  • Degradation analysis for critical components in machines
  • Cross-domain transfer learning for robust condition monitoring
  • Model parameters optimization for satisfactory model learning
  • Advance approaches for vibration signal pre-processing
  • Improved learning approaches with massive unlabeled data and limited labeled data

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 8843124
  • - Research Article

Rolling Bearing Fault Diagnosis Based on Domain Adaptation and Preferred Feature Selection under Variable Working Conditions

Xiao Yu | Wei Chen | ... | Fei Dong
  • Special Issue
  • - Volume 2021
  • - Article ID 6649125
  • - Research Article

An Improved Dual-Kurtogram-Based Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings

Zhiyuan Jiao | Wei Fan | Zhenying Xu
  • Special Issue
  • - Volume 2021
  • - Article ID 6656635
  • - Research Article

Intelligent Diagnosis of Subway Traction Motor Bearing Fault Based on Improved Stacked Denoising Autoencoder

Yanwei Xu | Chen Li | Tancheng Xie
  • Special Issue
  • - Volume 2020
  • - Article ID 8835462
  • - Research Article

A Novel Shearer Cutting State Recognition Method Based on Improved Variational Mode Decomposition and LSSVM with Acoustic Signals

Zhongbin Wang | Bin Liang | ... | Chao Tan
  • Special Issue
  • - Volume 2020
  • - Article ID 8880960
  • - Research Article

Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer

Jingli Yang | Tianyu Gao | ... | Qing Tang
  • Special Issue
  • - Volume 2020
  • - Article ID 8863388
  • - Research Article

Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning

Jianye Zhou | Xinyu Yang | ... | Gangying Bian
  • Special Issue
  • - Volume 2020
  • - Article ID 8843314
  • - Research Article

An Improved Deep Learning Model for Online Tool Condition Monitoring Using Output Power Signals

Lang Dai | Tianyu Liu | ... | Lei Mao
  • Special Issue
  • - Volume 2020
  • - Article ID 8884179
  • - Research Article

Deep Transfer Learning-Based Fault Diagnosis for Gearbox under Complex Working Conditions

Zitong Wan | Rui Yang | Mengjie Huang
  • Special Issue
  • - Volume 2020
  • - Article ID 8823050
  • - Research Article

Railway Fastener Fault Diagnosis Based on Generative Adversarial Network and Residual Network Model

Dechen Yao | Qiang Sun | ... | Jiao Zhang
  • Special Issue
  • - Volume 2020
  • - Article ID 8871981
  • - Research Article

A Novel Multiscale Deep Health Indicator with Bidirectional LSTM Network for Bearing Performance Degradation Trend Prognosis

Han Wang | Gang Tang | ... | Yujing Huang
Shock and Vibration
 Journal metrics
Acceptance rate36%
Submission to final decision92 days
Acceptance to publication38 days
CiteScore2.200
Journal Citation Indicator0.380
Impact Factor1.543
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.