Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.0
Journals
Energies
Special Issues
2017 Prognostics and System Health Management Conference
energies-logo
Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

2017 Prognostics and System Health Management Conference

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (31 May 2018) | Viewed by 18595

Special Issue Editors

Prof. Dr. Rui Xiong

grade E-Mail Website
Guest Editor
Department of Vehicle Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: intelligent electrified vehicle; batteries; energy storage; machine learning; AI; big-data; control; optimization and management.
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Datong Liu

E-Mail Website
Guest Editor
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China
Interests: automatic test and simulation; condition monitoring; system diagnostics and prognostics; industrial big data and industrial intelligence; lithium-ion battery management
Prof. Dr. Michael Gerard Pecht

E-Mail Website
Guest Editor
Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA
Interests: competitive product development; product characterization and qualification; supply chain creation and management; prognostics and health management; product reliability, risk assessment and mitigation
Special Issues, Collections and Topics in MDPI journals
Dr. Bin Zhang

E-Mail Website
Guest Editor
Department of Electrical engineering, College of Engineering and Computing, University of South Carolina, 301 Main St. Columbia, SC 29208, USA
Interests: prognostics and health management; robotics; unmanned systems; intelligent systems and control; dynamic systems; design; modeling; simulation and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to announce that the 2017 Prognostics and System Health Management Conference (PHM-2017 Harbin) will be held in Harbin, China, from 9–12 July, 2017. The PHM conference has become the top PHM forum in the Asia-Pacific area for connecting with leaders in PHM from around the world.

PHM-2017 Harbin serves as a premier interdisciplinary forum for researchers, scientists, and scholars in the domains of aeronautics and astronautics, energy and power systems, process industries, computers and telecommunications, and industrial automation. The most recent innovations, trends, concerns, challenges, and solutions will be presented and discussed.

Assoc. Prof. Dr. Rui Xiong
Prof. Michael Gerard Pecht
Assoc. Prof. Datong Liu
Dr. Bin Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • PHM for energy system

  • Circuits diagnostics and prognostics

  • Testability for PHM

  • PHM for electronic chips

  • PHM for electronic components or systems

  • PHM in spacecraft engineering

  • PHM in aerial engineering

  • PHM for transportation

  • PHM for mechanical system

  • System diagnostics

  • Machine learning in PHM

  • Virtual Simulation for diagnostics and prognostics

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

2639 KiB  
Article
Boolean Network-Based Sensor Selection with Application to the Fault Diagnosis of a Nuclear Plant
by Zhe Dong
Energies 2017, 10(12), 2125; https://doi.org/10.3390/en10122125 - 13 Dec 2017
Cited by 12 | Viewed by 3326
Abstract
Fault diagnosis is crucial for the operation of energy systems such as nuclear plants, and heavily relies on various types of sensors for temperature, pressure, concentration, etc. Due to the redundancy of sensors in each energy system, the sensor selection scheme can deeply [...] Read more.
Fault diagnosis is crucial for the operation of energy systems such as nuclear plants, and heavily relies on various types of sensors for temperature, pressure, concentration, etc. Due to the redundancy of sensors in each energy system, the sensor selection scheme can deeply influence the diagnostic efficiency. In this paper, a Boolean network (BN) with its linear representation is proposed for describing the fault propagation among sensors. Both the sufficient condition of fault detectability and that of fault discriminability are given. Then, a sensor selection method for fault detection and discrimination is proposed. Finally, the theoretic result is applied to realize the diagnosis oriented sensor selection for a nuclear steam supply system based on a modular high temperature gas-cooled reactor (MHTGR). The computation and simulation results verify the correctness of the theoretical results. Full article
(This article belongs to the Special Issue 2017 Prognostics and System Health Management Conference)
►▼ Show Figures

Figure 1

4441 KiB  
Article
Failure Prognosis of High Voltage Circuit Breakers with Temporal Latent Dirichlet Allocation
by Gaoyang Li, Xiaohua Wang, Aijun Yang, Mingzhe Rong and Kang Yang
Energies 2017, 10(11), 1913; https://doi.org/10.3390/en10111913 - 20 Nov 2017
Cited by 5 | Viewed by 6308
Abstract
The continual accumulation of power grid failure logs provides a valuable but rarely used source for data mining. Sequential analysis, aiming at exploiting the temporal evolution and exploring the future trend in power grid failures, is an increasingly promising alternative for predictive scheduling [...] Read more.
The continual accumulation of power grid failure logs provides a valuable but rarely used source for data mining. Sequential analysis, aiming at exploiting the temporal evolution and exploring the future trend in power grid failures, is an increasingly promising alternative for predictive scheduling and decision-making. In this paper, a temporal Latent Dirichlet Allocation (TLDA) framework is proposed to proactively reduce the cardinality of the event categories and estimate the future failure distributions by automatically uncovering the hidden patterns. The aim was to model the failure sequence as a mixture of several failure patterns, each of which was characterized by an infinite mixture of failures with certain probabilities. This state space dependency was captured by a hierarchical Bayesian framework. The model was temporally extended by establishing the long-term dependency with new co-occurrence patterns. Evaluation of the high voltage circuit breakers (HVCBs) demonstrated that the TLDA model had higher fidelities of 51.13%, 73.86%, and 92.93% in the Top-1, Top-5, and Top-10 failure prediction tasks over the baselines, respectively. In addition to the quantitative results, we showed that the TLDA can be successfully used for extracting the time-varying failure patterns and capture the failure association with a cluster coalition method. Full article
(This article belongs to the Special Issue 2017 Prognostics and System Health Management Conference)
►▼ Show Figures

Figure 1

1680 KiB  
Article
Approximate Analysis of Multi-State Weighted k-Out-of-n Systems Applied to Transmission Lines
by Xiaogang Song, Zhengjun Zhai, Yangming Guo, Peican Zhu and Jie Han
Energies 2017, 10(11), 1740; https://doi.org/10.3390/en10111740 - 30 Oct 2017
Cited by 3 | Viewed by 3481
Abstract
Multi-state weighted k-out-of-n systems are widely applied in various scenarios, such as multiple line (power/oil transmission line) transmission systems where the capability of fault tolerance is desirable. However, the complex operating environment and the dynamic features of load demands influence the [...] Read more.
Multi-state weighted k-out-of-n systems are widely applied in various scenarios, such as multiple line (power/oil transmission line) transmission systems where the capability of fault tolerance is desirable. However, the complex operating environment and the dynamic features of load demands influence the evaluation of system reliability. In this paper, a stochastic multiple-valued (SMV) approach is proposed to efficiently predict the reliability of two models of systems with non-repairable components and dynamically repairable components. The weights/performances and reliabilities of multi-state components (MSCs) are represented by stochastic sequences consisting of a fixed number of multi-state values with the positions being randomly permutated. Using stochastic sequences with L multiple values, linear computational complexities with parameters n and L are required by the SMV approach to compute the reliability of different multi-state k-out-of-n systems at a reasonable accuracy, compared to the complexities of universal generating functions (UGF) and fuzzy universal generating functions (FUGF) that increase exponentially with the value of n. The analysis of two benchmarks shows that the proposed SMV approach is more efficient than the analysis using UGF or FUGF. Full article
(This article belongs to the Special Issue 2017 Prognostics and System Health Management Conference)
►▼ Show Figures

Figure 1

1310 KiB  
Article
A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena
by Jianxun Zhang, Xiao He, Xiaosheng Si, Changhua Hu and Donghua Zhou
Energies 2017, 10(11), 1687; https://doi.org/10.3390/en10111687 - 25 Oct 2017
Cited by 21 | Viewed by 4109
Abstract
A lithium-Ion battery is a typical degradation product, and its performance will deteriorate over time. In its degradation process, regeneration phenomena have been frequently encountered, which affect both the degradation state and rate. In this paper, we focus on how to build the [...] Read more.
A lithium-Ion battery is a typical degradation product, and its performance will deteriorate over time. In its degradation process, regeneration phenomena have been frequently encountered, which affect both the degradation state and rate. In this paper, we focus on how to build the degradation model and estimate the lifetime. Toward this end, we first propose a multi-phase stochastic degradation model with random jumps based on the Wiener process, where the multi-phase model and random jumps at the changing point are used to describe the variation of degradation rate and state caused by regeneration phenomena accordingly. Owing to the complex structure and random variables, the traditional Maximum Likelihood Estimation (MLE) is not suitable for the proposed model. In this case, we treat these random variables as latent parameters, and then develop an approach for model identification based on expectation conditional maximum (ECM) algorithm. Moreover, depending on the proposed model, how to estimate the lifetime with fixed changing point is presented via the time-space transformation technique, and the approximate analytical solution is derived. Finally, a numerical simulation and a practical case are provided for illustration. Full article
(This article belongs to the Special Issue 2017 Prognostics and System Health Management Conference)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop