Complexity

Fault Identification, Diagnosis, and Prognostics Based on Complex Signal Analysis


Status
Published

1Kaunas University of Technology, Kaunas, Lithuania

2Beihang University, Beijing, China

3Hohai University, Nanjing, China

4University of Houston, Houston, USA

5Silesian University of Technology, Gliwice, Poland


Fault Identification, Diagnosis, and Prognostics Based on Complex Signal Analysis

Description

Prognostics and health management (PHM) has become one of the most popular research topics, especially for complex electromechanical systems such as rotary machinery, control system, and power system in the fields of aerospace, manufacturing, sustainable energy, infrastructure, and transportation. To maximize the operational availability, reduce potential risks, and save the cost of life cycle, a PHM system is expected to predict, diagnose, monitor, and manage the state or condition of engineering assets using various advanced sensors (accelerometers, piezoelectric sensors, etc.). The monitored signals can be conveniently acquired and contain abundant signature information that reflects the trend of the potential failure and performance degradation of the investigated systems.

The overarching intention of this special issue is to publish new progress dealing mainly (but not exclusively) with up-to-date solutions of signal processing, autonomic feature extraction, health assessment and diagnosis, and performance degradation prediction. Emphasis will be focused on various leading-edge theories and methodologies, such as chaos and fractal, genetic algorithms, cellular automata, big data analysis, and evolutionary game theory, which are expected to address the existing challenges for a real-world PHM system. If deemed relevant, integration techniques of diagnosis and prognostics could also be presented.

This special issue aims to aggregate the latest research efforts contributing to theoretical, methodological, and technological advances in detecting anomalies, forecasting potential degradation, and classifying faults by monitoring and analyzing signals collected from different electromechanical systems operating in complex environments.

Prospective authors are invited to submit high-quality original contributions and reviews for this special issue, including novel theories, methodologies, and algorithms with necessary case studies in the field of PHM.

Potential topics include but are not limited to the following:

  • Advanced diagnosis and health assessment techniques for electromechanical systems
  • Advanced prognostics for remaining useful life and performance degradation
  • Structural health monitoring in the field of aerospace
  • Integration techniques of diagnosis and prognostics in the fields of aerospace, shipbuilding, manufacturing, infrastructure, and transportation
  • Multidimensional clustering and management of monitoring data for PHM applications
  • PHM methods for software aging and rejuvenation

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 4020729
  • - Editorial

Fault Identification, Diagnosis, and Prognostics Based on Complex Signal Analysis

Minvydas Ragulskis | Chen Lu | ... | Rafal Burdzik
  • Special Issue
  • - Volume 2018
  • - Article ID 3049318
  • - Research Article

Fault Diagnosis of Rolling Bearing Based on a Novel Adaptive High-Order Local Projection Denoising Method

Rui Yuan | Yong Lv | Gangbing Song
  • Special Issue
  • - Volume 2018
  • - Article ID 3541676
  • - Research Article

Damage Diagnosis in 3D Structures Using a Novel Hybrid Multiobjective Optimization and FE Model Updating Framework

Nizar Faisal Alkayem | Maosen Cao | Minvydas Ragulskis
  • Special Issue
  • - Volume 2018
  • - Article ID 9154682
  • - Research Article

Fault Diagnosis of Electromechanical Actuator Based on VMD Multifractal Detrended Fluctuation Analysis and PNN

Hongmei Liu | Jiayao Jing | Jian Ma
  • Special Issue
  • - Volume 2018
  • - Article ID 3813029
  • - Research Article

Predicting the Remaining Useful Life of an Aircraft Engine Using a Stacked Sparse Autoencoder with Multilayer Self-Learning

Jian Ma | Hua Su | ... | Bin Liu
  • Special Issue
  • - Volume 2018
  • - Article ID 2830690
  • - Research Article

Study on the Magnitude of Reservoir-Triggered Earthquake Based on Support Vector Machines

Hai Wei | Mingming Wang | ... | Danlei Chen
  • Special Issue
  • - Volume 2018
  • - Article ID 2169364
  • - Research Article

A Novel Fault Diagnosis Method for Rolling Bearing Based on Improved Sparse Regularization via Convex Optimization

Dongjie Zhong | Cancan Yi | ... | Anding Wu
  • Special Issue
  • - Volume 2018
  • - Article ID 5913976
  • - Research Article

An Investigation of Stretched Exponential Function in Quantifying Long-Term Memory of Extreme Events Based on Artificial Data following Lévy Stable Distribution

HongGuang Sun | Lin Yuan | ... | Nicholas Privitera
  • Special Issue
  • - Volume 2018
  • - Article ID 8210817
  • - Research Article

A Modified Time Reversal Method for Guided Wave Detection of Bolt Loosening in Simulated Thermal Protection System Panels

Guan-nan Wu | Chao Xu | ... | Wei-dong Zhu
  • Special Issue
  • - Volume 2018
  • - Article ID 1462594
  • - Research Article

Multiplicative Fault Estimation-Based Adaptive Sliding Mode Fault-Tolerant Control Design for Nonlinear Systems

Ali Ben Brahim | Slim Dhahri | ... | Anis Sellami
Complexity
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 Journal metrics
Acceptance rate43%
Submission to final decision64 days
Acceptance to publication35 days
CiteScore3.200
Impact Factor2.462
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