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Advances in Prognostics and System Health Management

Submission Deadline: 31 March 2019

Submission Deadline: 31 March 2019

IEEE Access invites manuscript submissions in the area of Advances in Prognostics and System Health Management.

There is a growing interest from industry, government, and academia for prognostics and health management of engineering systems and critical components. Prognostics aim to predict the time when an engineering system or a critical component will no longer perform its intended functionality. Health management is to take a measure to respond to the anticipation of failures and minimize economic loss, and then prevent any unexpected accidents. Thanks to advances of sensor systems, a large amount of direct and indirect health monitoring data may be available. Direct health monitoring data mean that data can be directly used as health indicators to assess the current health condition of engineering systems and critical components. Indirect health monitoring data indicate that some transformations of data should be conducted to construct health indicators for condition assessment of engineering systems and critical components. Thus, signal processing and data mining algorithms are required to preprocess indirect health monitoring data prior to the use of any prognostic algorithms. After preprocessing is conducted, a health indicator is constructed to describe how far the current health condition of engineering systems and critical components deviate from their expected normal health conditions. Once a health indicator is available, prognostic algorithms are developed to extrapolate the current health condition to future health conditions and then predict the remaining useful life.

The goal of this Special Section in IEEE Access is to provide a forum for the latest advances in the area of prognostics and system health management.

The topics of interest include, but are not limited to:

  • Smart senor designs for emitting excitations and receiving degradation data
  • Signal processing algorithms for reprocessing degradation data
  • Extraction of health indicators from engineering systems and critical components
  • Abnormal health detection algorithms and statistical control charts
  • Data fusion for degradation data
  • Deep learning based diagnostic and prognostic algorithms
  • Statistical modelling for degradation data
  • State space modeling for degradation data
  • Uncertainty interpretation, uncertainty quantification, uncertainty propagation and uncertainty management
  • Performance evaluation
  • Decision Making for degradation data
  • Industrial applications and their success in prognostics and health management

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor:

  1. Dong Wang, Shanghai Jiao Tong University, China
  2. Chuan Li, Chongqing Technology and Business University, China


Guest Editors:

  1. Enrico Zio, Foundation Electricite’ de France (EDF) at CentraleSupélec, France
  2. José Valente de Oliveira, Universidade do Algarve, Portugal
  3. Nishchal K. Verma, Indian Institute of Technology Kanpur, India
  4. Mariela Cerrada, Universidad Politecnica Salesiana, Ecuador
  5. Michael Pecht, University of Maryland, USA
  6. Abhinav Saxena, GE Global Research, USA
  7. Chetan S. Kulkarni, NASA Ames Research Center, USA

 

Relevant IEEE Access Special Sections:

  1. Data-Driven Monitoring, Fault Diagnosis and Control of Cyber-Physical Systems
  2. New Developments on Reliable Control and Filtering of Complex Nonlinear Systems
  3. Recent Computational Methods in Knowledge Engineering and Intelligence Computation


IEEE Access Editor-in-Chief:
Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://mc.manuscriptcentral.com/ieee-access

For inquiries regarding this Special Section, please contact: dongwang4-c@my.cityu.edu.hk (Dong Wang) or chuanli@ctbu.edu.cn (Chuan Li).