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 2829873
  • - Research Article

Rigorous Solution of Slopes’ Stability considering Hydrostatic Pressure

Chengchao Li | Pengming Jiang | Aizhao Zhou
  • Special Issue
  • - Volume 2018
  • - Article ID 9356451
  • - Review Article

Research Progress on Monitoring and Separating Suspension Particles for Lubricating Oil

Ziping Wang | Xian Xue | ... | Yefei Li
  • Special Issue
  • - Volume 2018
  • - Article ID 5081684
  • - Research Article

Contactless Modal Phenomena Based Approach to Detecting, Identifying, and Diagnosing of Electrical Connections

Pavel Orlov | Talgat Gazizov
  • Special Issue
  • - Volume 2018
  • - Article ID 4763612
  • - Research Article

A General Purpose Adaptive Fault Detection and Diagnosis Scheme for Information Systems with Superheterodyne Receivers

Dengwei Song | Hongmei Liu | ... | Bo Zhou
  • Special Issue
  • - Volume 2018
  • - Article ID 9034865
  • - Research Article

Statistical Identification of Parameters for Damaged FGM Structures with Material Uncertainties in Thermal Environment

Yalan Xu | Yu Qian | Kongming Guo
  • Special Issue
  • - Volume 2018
  • - Article ID 3904598
  • - Research Article

Gear Fault Diagnosis in Variable Speed Condition Based on Multiscale Chirplet Path Pursuit and Linear Canonical Transform

Xu Shuiqing | Zhang Ke | ... | Feng Li
  • Special Issue
  • - Volume 2018
  • - Article ID 8740989
  • - Research Article

Fault Diagnosis for Hydraulic Servo System Using Compressed Random Subspace Based ReliefF

Yu Ding | Fei Wang | ... | Wen-jin Zhang
  • Special Issue
  • - Volume 2018
  • - Article ID 7356189
  • - Research Article

Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model

Changming Liu | Di Zhou | ... | Gangbing Song
  • Special Issue
  • - Volume 2017
  • - Article ID 6342170
  • - Research Article

Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition

Yujie Cheng | Laifa Tao | Chao Yang
Complexity
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Acceptance rate11%
Submission to final decision127 days
Acceptance to publication19 days
CiteScore4.400
Journal Citation Indicator0.720
Impact Factor2.3
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