Shock and Vibration

Fault Diagnosis and Prognosis of Critical Components


Status
Published

Lead Editor

1City University of Hong Kong, Hong Kong

2Universidad Politécnica Salesiana, Cuenca, Ecuador

3University of Diponegoro, Semarang, Indonesia

4PDPM Indian Institute of Information Technology, Jabalpur, India

5University of Wollongong, Wollongong, Australia


Fault Diagnosis and Prognosis of Critical Components

Description

Some critical components, such as bearings, gearboxes, and impellers, are widely used in machines. Their faults may accelerate the failures of other components and finally result in machine breakdowns. To prevent any unexpected machine breakdowns and accidents, early faults of critical components should be detected as soon as possible. Once early faults of critical components are diagnosed, their performance degradation assessment and remaining useful life estimation should be conducted to maximize lifetime of critical components. This special issue focuses on vibration based methods for fault diagnosis and prognosis of critical components.

Potential topics include, but are not limited to:

  • Patten recognition methods for diagnosis and prognosis of critical components
  • Digital signal processing methods for diagnosis and prognosis of critical components
  • Statistical signal processing methods for diagnosis and prognosis of critical components
  • Reliability and robustness Bayesian methods for diagnosis and prognosis of critical components
  • Soft computing and related techniques, such as evolutionary computing, fuzzy computing, probabilistic computing, and rough sets, and their new applications to diagnosis and prognosis of critical components

Articles

  • Special Issue
  • - Volume 2016
  • - Article ID 9597656
  • - Editorial

Fault Diagnosis and Prognosis of Critical Components

Dong Wang | Chuan Li | ... | Wahyu Caesarendra
  • Special Issue
  • - Volume 2016
  • - Article ID 3838765
  • - Research Article

A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries

Wen-An Yang | Maohua Xiao | ... | Wenhe Liao
  • Special Issue
  • - Volume 2016
  • - Article ID 8631639
  • - Research Article

Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm

Wen-An Yang | Maohua Xiao | ... | Gang Shen
  • Special Issue
  • - Volume 2016
  • - Article ID 3891429
  • - Research Article

Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

Liye Zhao | Wei Yu | Ruqiang Yan
  • Special Issue
  • - Volume 2016
  • - Article ID 1835127
  • - Research Article

Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features

Wei Peng | Dong Wang | ... | Dongni Liu
  • Special Issue
  • - Volume 2016
  • - Article ID 9263298
  • - Research Article

Fault Diagnosis for a Multistage Planetary Gear Set Using Model-Based Simulation and Experimental Investigation

Guoyan Li | Fangyi Li | ... | Dehao Dong
  • Special Issue
  • - Volume 2016
  • - Article ID 4086324
  • - Research Article

Application of Artificial Neural Network for Damage Detection in Planetary Gearbox of Wind Turbine

Marcin Strączkiewicz | Tomasz Barszcz
  • Special Issue
  • - Volume 2016
  • - Article ID 6723267
  • - Research Article

Application of Reassigned Wavelet Scalogram in Wind Turbine Planetary Gearbox Fault Diagnosis under Nonstationary Conditions

Xiaowang Chen | Zhipeng Feng
  • Special Issue
  • - Volume 2015
  • - Article ID 560171
  • - Research Article

Research on the Sparse Representation for Gearbox Compound Fault Features Using Wavelet Bases

Chunyan Luo | Changqing Shen | ... | Zhongkui Zhu
  • Special Issue
  • - Volume 2015
  • - Article ID 542472
  • - Research Article

Cyclostationary Analysis for Gearbox and Bearing Fault Diagnosis

Zhipeng Feng | Fulei Chu
Shock and Vibration
 Journal metrics
Acceptance rate36%
Submission to final decision92 days
Acceptance to publication38 days
CiteScore2.400
Impact Factor1.298
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