Shock and Vibration

Vibration Data-Driven Mechanical Fault Diagnosis and Prognosis in Rotating Machinery


Publishing date
01 Jul 2021
Status
Closed
Submission deadline
12 Mar 2021

Lead Editor

1Chongqing University, Chongqing, China

2Dongguan University of Technology, Dongguan, China

3SS Cyril and Methodius University, Skopje, Macedonia

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

Vibration Data-Driven Mechanical Fault Diagnosis and Prognosis in Rotating Machinery

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

Description

Rotating machinery has been widely used in the fields of machine tools, electric power, railway, ship, automobile, petrochemistry, etc. Owing to the severe environment and the complex working condition, the key rotating components including the gear, bearing, and rotor, are easily subject to failure, such as cracking, wear, pitting, spalling, fracture, scratch, etc. If early minor faults are not diagnosed timely, they will deteriorate rapidly and may lead to a halt of the whole rotating machinery, and even catastrophic economic losses and casualties. To keep the machine in safe and reliable operation, it is necessary to detect or predict faults as early as possible.

In recent years, fault detection and life prediction for rotating machineries have received a lot of attention. A number of methods, such as wavelet transform, empirical mode decomposition, variational mode decomposition, spectral kurtosis, time-frequency distribution, sparse representation, random forest, Markov model, support vector machine, manifold learning, particle filter, deep learning, transfer learning, etc., have been proposed to deal with these problems. Most studies have focused on fault feature extraction and fault classification using the acquired vibration signals, and there are relatively few studies on fault prognosis. Due to the variable working condition and the complex structure of such rotating machineries as aero-engines, high speed rail, and wind turbines, there are still great challenges in accurately detecting the incipient faults and predicting the long-term life of rotating machinery.

The aim of this Special Issue is to facilitate the development of fault prognosis and life prediction approaches based on vibration data for varied rotating machineries. This Special Issue hopes to attract studies including weak fault feature extraction based on various signal processing methods, fault diagnosis under variable working condition, intelligent fault diagnosis by machine learning, transfer fault recognition by domain adaption models, data-driven life prediction based on deep learning, hybrid life prediction based on both vibration data and physical model, etc. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

  • Sparse representation for fault feature extraction
  • Fault feature extraction based on varied mode decomposition algorithms
  • Fault feature extraction based on varied wavelet transforms
  • Fault feature extraction under variable working condition
  • Transient feature extraction by new impact indicators
  • Time-frequency distributions of vibration signals for fault diagnosis
  • Intelligent fault diagnosis by support vector machine
  • Intelligent fault diagnosis by random forest
  • Intelligent fault diagnosis by various deep neural networks
  • Transfer fault diagnosis by various domain adaption models
  • Vibration data-driven life prediction based deep learning
  • Hybrid life prediction based on both vibration data and physical models

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 8876190
  • - Research Article

Dynamic Characteristics of a Misaligned Rigid Rotor System with Flexible Supports

Meiling Wang | Baogang Wen | ... | Changxin Yu
  • Special Issue
  • - Volume 2021
  • - Article ID 6616592
  • - Research Article

Vibration Images-Driven Fault Diagnosis Based on CNN and Transfer Learning of Rolling Bearing under Strong Noise

Hongwei Fan | Ceyi Xue | ... | Sijie Shao
  • Special Issue
  • - Volume 2021
  • - Article ID 9936080
  • - Research Article

Intelligent Fault Diagnosis of Machines Based on Adaptive Transfer Density Peaks Search Clustering

Meng Li | Yanxue Wang | Chuyuan Wei
  • Special Issue
  • - Volume 2021
  • - Article ID 5584327
  • - Research Article

Performance Degradation Assessment of Rotary Machinery Based on a Multiscale Tsallis Permutation Entropy Method

Yong Chen | Mian Jiang | Kuanfang He
  • Special Issue
  • - Volume 2021
  • - Article ID 5544031
  • - Research Article

The Novel Successive Variational Mode Decomposition and Weighted Regularized Extreme Learning Machine for Fault Diagnosis of Automobile Gearbox

Yijiao Wang | Guoguang Zhou
  • Special Issue
  • - Volume 2020
  • - Article ID 8873504
  • - Research Article

Fault Diagnosis Method for Bearing of High-Speed Train Based on Multitask Deep Learning

Jia Gu | Ming Huang
  • Special Issue
  • - Volume 2020
  • - Article ID 8819313
  • - Research Article

An Antinoise Fault Diagnosis Method Based on Multiscale 1DCNN

Jie Cao | Zhidong He | ... | Ping Yu
  • Special Issue
  • - Volume 2020
  • - Article ID 8869648
  • - Research Article

A New Generative Neural Network for Bearing Fault Diagnosis with Imbalanced Data

Wei You | Changqing Shen | ... | Zhongkui Zhu
Shock and Vibration
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Acceptance rate36%
Submission to final decision92 days
Acceptance to publication38 days
CiteScore2.200
Impact Factor1.543
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