Shock and Vibration

Advances in Prognostics and Health Management for Intelligent Manufacturing


Publishing date
01 Aug 2021
Status
Closed
Submission deadline
19 Mar 2021

Lead Editor

1Shanghai Jiao Tong University, Shanghai, China

2Northeastern University, Boston, USA

3Xi’an Jiaotong University, Xi'an, China

4Rutgers University, New Brunswick, USA

5North Carolina State University, Raleigh, USA

This issue is now closed for submissions.
More articles will be published in the near future.

Advances in Prognostics and Health Management for Intelligent Manufacturing

This issue is now closed for submissions.
More articles will be published in the near future.

Description

As an emerging field in the mechanical sciences, prognostics and health management (PHM) are gaining interest from the industry and academia. An effective PHM framework normally includes health prognostics and maintenance management. For health prognostics, more and more advanced instruments, such as smart sensors, meters, controllers, and computational devices, are being applied to collect and analyse the signals from individual machines. Prognostic techniques, such as vibration monitoring, oil analysis, temperature detection, acoustic emission, and ultrasonic inspection, have also been widely employed to measure the status of a machine. Many valuable prognostics approaches have thus been proposed to generate a rational estimation of the remaining useful life (RUL) or the potential degradation process. For maintenance management, the maintenance policies of complex systems are facing challenges from structural, stochastic, and economic dependencies, and advanced manufacturing paradigms.

Due to recent developments in manufacturing paradigms, PHM methodologies for traditional manufacturing systems need to be extended. There has been increasing interest in integrating PHM with intelligent manufacturing. With innovations in technique, enterprises can apply mass customisation, reconfigurable manufacturing, sustainable manufacturing, and service-oriented manufacturing to maintain competitiveness and meet customer needs. More targeted PHM methodologies enable the industry to lower the possibility of unexpected breakdowns and to lower the cost of maintenance. Thus, novel PHM methodologies for intelligent manufacturing are vital for enterprises with foresight.

The aim of this Special Issue is to promote prognostics and health management, and act as a platform to present high-quality original research on the latest developments of PHM methods for intelligent manufacturing. We welcome both original research articles and review articles discussing the current state of the art.

Potential topics include but are not limited to the following:

  • Model-driven approaches in PHM
  • Data-driven approaches in PHM
  • Machine learning based system health monitoring
  • Failure detection, classification, or localisation
  • Advanced signal conditioning techniques
  • Optimisation of intelligent sensing layout
  • Safety, reliability, risk, and life-cycle performance
  • Maintenance policies for intelligent manufacturing
  • PHM applications of advanced manufacturing paradigms
  • Operational and experimental modal analysis
  • Risk, reliability, and uncertainty in PHM

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 9937846
  • - Research Article

Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables

Sergio Cofre-Martel | Enrique Lopez Droguett | Mohammad Modarres
  • Special Issue
  • - Volume 2021
  • - Article ID 5595535
  • - Research Article

Research on Optimizing Selection and Optimizing Matching Technologies of Aeroengine Fan Rotor Blades

Lili Li | Kun Chen | ... | Junkong Liu
  • Special Issue
  • - Volume 2021
  • - Article ID 6694896
  • - Research Article

A New Maintenance Optimization Model Based on Three-Stage Time Delay for Series Intelligent System with Intermediate Buffer

Xiaolei Lv | Qinming Liu | ... | Xiang Chen
  • Special Issue
  • - Volume 2021
  • - Article ID 6675078
  • - Research Article

A New Support Vector Regression Model for Equipment Health Diagnosis with Small Sample Data Missing and Its Application

Qinming Liu | Wenyi Liu | ... | Jiarui Quan
  • Special Issue
  • - Volume 2021
  • - Article ID 6699611
  • - Research Article

Health State Prediction and Performance Evaluation of Belt Conveyor Based on Dynamic Bayesian Network in Underground Mining

Xiangong Li | Yuzhi Zhang | ... | Lin Yang
  • Special Issue
  • - Volume 2021
  • - Article ID 6627305
  • - Research Article

Multidomain Feature Fusion for Varying Speed Bearing Diagnosis Using Broad Learning System

Tingting Wu | Yufen Zhuang | ... | Kangkang Xu
  • Special Issue
  • - Volume 2021
  • - Article ID 6687525
  • - Research Article

Parallel-Machine Scheduling with DeJong’s Learning Effect, Delivery Times, Rate-Modifying Activity, and Resource Allocation

Li Sun | Bin Wu | Lei Ning
  • Special Issue
  • - Volume 2020
  • - Article ID 6650155
  • - Research Article

Complex Behavior of Droplet Transfer and Spreading in Cold Metal Transfer

Shuai Yang | Yanfeng Xing | ... | Juyong Cao
  • Special Issue
  • - Volume 2020
  • - Article ID 6694732
  • - Research Article

Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network

Li-li Li | Kun Chen | ... | Hui Li
Shock and Vibration
 Journal metrics
Acceptance rate36%
Submission to final decision92 days
Acceptance to publication38 days
CiteScore2.200
Impact Factor1.543
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.