Shock and Vibration

Advances in Fault Diagnosis and Defect Detection in Mechanical and Civil Engineering


Publishing date
01 Nov 2020
Status
Published
Submission deadline
03 Jul 2020

Lead Editor

1Wenzhou University, Wenzhou, China

2Amity University, Uttar Pradesh, India

3Beijing University of Civil Engineering and Architecture, Beijing, China


Advances in Fault Diagnosis and Defect Detection in Mechanical and Civil Engineering

Description

Fault diagnosis and defect detection are now the key and crucial issues for mechanical and civil structures due to the increase of accident ratios and maintenance costs. Recently, more focus has been on fault diagnosis and defect detection for mechanical and civil structures. The history of fault diagnosis and defect detection, beginning in the 1960s, shows that new developments in this area always occur alongside new sensor technologies and measurement methods.

In order to rapidly report and spread the latest advancements in the science of fault diagnosis and detection, including new discoveries and valuable applied research, from all over the world, this Special Issue intends to publish original research and review articles into all aspects of theoretical and applied investigations about the latest development of sensor technologies and measurement methods for the detection of defects and diagnosis of faults.

The intention of this Special Issue is to open a broad scientific and technical forum for further development of fault diagnosis and defect detection technology. The main themes will be theory and background as well as applications in mechanical and civil engineering. Original research studies and review articles related to the above themes are encouraged.

Potential topics include but are not limited to the following:

  • Vibration analysis for fault diagnosis
  • Guided waves analysis for defect detection
  • Sensors and data acquisition techniques for online monitoring of faults
  • Model-based health monitoring method with applications
  • Signal processing for mechanical and civil structures
  • Artificial intelligent models for mechanical and civil structures
  • Case study in fault diagnosis of real-world running machines or civil structures

Articles

  • Special Issue
  • - Volume 2020
  • - Article ID 8816884
  • - Research Article

Study on the Measurement Method of the Crack Local Flexibility of the Beam Structure

Yumin He | Siyu Guo | Xiaolong Zhang
  • Special Issue
  • - Volume 2020
  • - Article ID 8863379
  • - Research Article

Fatigue Life Prediction of the Zirconia Fixture Based on Boundary Element Method

Y. W. Wang | J. J. Ye | ... | B. Q. Shi
  • Special Issue
  • - Volume 2020
  • - Article ID 1635621
  • - Research Article

Translation Invariance-Based Deep Learning for Rotating Machinery Diagnosis

Wenliao Du | Shuangyuan Wang | ... | Michael Pecht
  • Special Issue
  • - Volume 2020
  • - Article ID 7893925
  • - Research Article

Research on Identification Technology of Explosive Vibration Based on EEMD Energy Entropy and Multiclassification SVM

Huayuan Ma | Xinghua Li | ... | Changxiao Zhao
  • Special Issue
  • - Volume 2020
  • - Article ID 8856241
  • - Research Article

Frequency Aliasing-Based Spatial-Wavenumber Filter for Online Damage Monitoring

Bin Liu | Tingzhang Liu | ... | Dan Hang
  • Special Issue
  • - Volume 2020
  • - Article ID 8826419
  • - Research Article

Mass Laplacian Discriminant Analysis and Its Application in Gear Fault Diagnosis

Guangbin Wang | Ying Lv | ... | Huanke Cheng
  • Special Issue
  • - Volume 2020
  • - Article ID 8850976
  • - Research Article

A New Deep Convolutional Domain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions

Yongchao Zhang | Zhaohui Ren | Shihua Zhou
  • Special Issue
  • - Volume 2020
  • - Article ID 1091548
  • - Research Article

An Improved Lagrange Particle Swarm Optimization Algorithm and Its Application in Multiple Fault Diagnosis

Xiaofeng Lv | Deyun Zhou | ... | Yongchuan Tang
  • Special Issue
  • - Volume 2020
  • - Article ID 8430986
  • - Research Article

A Morphology Filter-Assisted Extreme-Point Symmetric Mode Decomposition (MF-ESMD) Denoising Method for Bridge Dynamic Deflection Based on Ground-Based Microwave Interferometry

Xianglei Liu | Mengzhuo Jiang | ... | Hui Wang
  • Special Issue
  • - Volume 2020
  • - Article ID 8015465
  • - Research Article

Development and Verification of the Diagnostic Model of the Sieving Screen

Pavlo Krot | Radoslaw Zimroz | ... | Marek Jach
Shock and Vibration
 Journal metrics
See full report
Acceptance rate25%
Submission to final decision95 days
Acceptance to publication17 days
CiteScore2.800
Journal Citation Indicator0.400
Impact Factor1.6
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