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 2015
  • - Article ID 425989
  • - Research Article

Feature Extraction of Faulty Rolling Element Bearing under Variable Rotational Speed and Gear Interferences Conditions

Dezun Zhao | Jianyong Li | Weidong Cheng
  • Special Issue
  • - Volume 2015
  • - Article ID 676959
  • - Research Article

Two General Architectures for Intelligent Machine Performance Degradation Assessment

Yanwei Xu | Aijun Xu | Tancheng Xie
  • Special Issue
  • - Volume 2015
  • - Article ID 989854
  • - Research Article

In Situ Measurement of Seeking Speed and Seeking Induced Head-Disk Interface Instability in Hard Disk Drives

Yu Wang | Xiongfei Wei | ... | Kwok-Leung Tsui
  • Special Issue
  • - Volume 2015
  • - Article ID 850286
  • - Research Article

A New Transmissibility Based Indicator of Local Variation in Structure and Its Application for Damage Detection

X. Z. Li | Z. K. Peng | ... | G. Meng
  • Special Issue
  • - Volume 2015
  • - Article ID 320508
  • - Research Article

Multifault Diagnosis of Rolling Element Bearings Using a Wavelet Kurtogram and Vector Median-Based Feature Analysis

Phuong H. Nguyen | Jong-Myon Kim
  • Special Issue
  • - Volume 2015
  • - Article ID 286781
  • - Research Article

Condition Monitoring and Fault Diagnosis for an Antifalling Safety Device

Guangxiang Yang | Hua Liang
  • Special Issue
  • - Volume 2015
  • - Article ID 390134
  • - Research Article

Gearbox Fault Identification and Classification with Convolutional Neural Networks

ZhiQiang Chen | Chuan Li | René-Vinicio Sanchez
  • Special Issue
  • - Volume 2015
  • - Article ID 150797
  • - Research Article

Screw Performance Degradation Assessment Based on Quantum Genetic Algorithm and Dynamic Fuzzy Neural Network

Xiaochen Zhang | Hongli Gao | Haifeng Huang
  • Special Issue
  • - Volume 2015
  • - Article ID 167902
  • - Research Article

Rolling Element Bearing Fault Diagnosis Based on Multiscale General Fractal Features

Weigang Wen | Zhaoyan Fan | ... | Weidong Cheng
  • Special Issue
  • - Volume 2015
  • - Article ID 126489
  • - Research Article

Planetary Gearbox Vibration Signal Characteristics Analysis and Fault Diagnosis

Qiang Miao | Qinghua Zhou
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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