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Special Issue "Structural Prognostics and Health Management in Power & Energy Systems"

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

Deadline for manuscript submissions: 31 December 2018

Special Issue Editors

Guest Editor
Dr. Dong Wang

Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China
Website | E-Mail
Phone: 852-3442 4604
Interests: statistical modeling; machine fault diagnosis; prognostics and health management; mechanical signal processing; statistical signal processing; digital/adaptive signal processing; data mining; non-destructive testing; system diagnostics; energy systems
Guest Editor
Assoc. Prof. Dr. Shun-Peng Zhu

Center for System Reliability & Safety, University of Electronic Science and Technology of China, Chengdu 611731, China
Website | E-Mail
Interests: probabilistic physics of failure modeling; damage accumulation; reliability and risk analysis; life prediction; uncertainty quantification; Bayesian inference; probability-based design; degradation modeling and analysis; structural reliability; prognostics and health management; structural health monitoring; gas/steam turbine technologies
Guest Editor
Prof. Dr. Xiancheng Zhang

Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
Website | E-Mail
Interests: multi-physics damage modeling; high temperature fatigue; fatigue-creep interaction; life design and prediction; structural integrity; damage tolerance
Guest Editor
Prof. Gang Chen

School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
Website | E-Mail
Interests: reliability testing and statistics; life prediction; advanced testing techniques; chemical equipment; power plant technologies; damage modeling; fracture mechanics; fatigue; damage tolerance; structural integrity assessment
Guest Editor
Dr. José A.F.O. Correia

INEGI, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
Website | E-Mail
Interests: pressure vessels; fatigue; probabilistic fatigue modelling; cyclic plasticity; failure mechanisms; structural integrity; life prediction; probabilistic damage tolerance
Guest Editor
Dr. Guian Qian

Laboratory for Nuclear Materials, Paul Scherrer Institute (PSI), Switzerland
Website | E-Mail
Interests: steam turbine; power plant technologies; failure mechanisms; probabilistic damage tolerance; structural integrity; fatigue and fracture analysis of nuclear components and structures; nuclear energy and safety; pressurized thermal shock analysis of reactor pressure vessels; leak-before-break analysis of nuclear piping; nuclear materials

Special Issue Information

Dear Colleagues,

In order to ensure the safety and reliability of power and energy systems, including wind turbines, gas/steam turbines, power plants, etc., failure mechanism, reliability assessment, prognostics, and health management (PHM) have becoming recent developments in integrity analysis of these systems. For many countries, such as the European countries, England and the USA, currently facing a potential future mismatch from energy production and transformation, currently increasing interests are being paid on new techniques to discover and understand the remaining life and integrity assessment of power and energy systems.

To prevent any unexpected machine breakdowns and accidents, early faults of critical components in these systems should be detected as soon as possible. Once early faults of critical components are diagnosed, their performance degradation assessment and remaining useful life estimation should be conducted to maximize lifetime of power and energy systems. Moreover, due to unexpected ageing related degradations/damaging, mechanical properties, microstructures and structural resistance of systems/components often require stochastic considerations related to failure mechanism modeling and analysis. In addition, various sources of uncertainty/variability arising from a simplified representation of the actual physical process (often through semi-empirical or empirical models) and/or sparse information on manufacturing, material properties, and loading profiles contribute to stochastic behavior under operation.

Accordingly, continued improvements on PHM have been possible through advanced signature analysis, performance degradation assessment, as well as accurate modeling of failure mechanisms by introducing advanced mathematical approaches/tools. Through combining the deterministic and probabilistic modeling techniques, researches on PHM and structural health monitoring (SHM) can provide assurance for new structures at the design stage and ensure the integrity in the construction at the fabrication phase. Specifically, power and energy system failure occurs under multi-sources of uncertainty/variability, resulting from load variation in usages, material properties, geometry variations within tolerances, and other uncontrolled variations. Thus, advanced methods and applications for theoretical, numerical, and experimental contributions that address these issues on PHM are desired and expected, which attempts to prevent over-design and unnecessary inspection and provide the tools to enable a balance between safety and economy to be achieved.

The aim of this Special Issue would be to provide the data, models and tools necessary to performing PHM from structural to the system, resulting in the use of advanced mathematical, numerical and experimental techniques. Therefore, researchers are invited to provide original research and review articles that seek for accurate and efficient machine fault diagnosis and prognosis, remaining life assessment, condition-based maintenance, and so forth. Potential topics include, but are not limited to:

  • wind/gas/steam turbine technologies
  • power plant technologies
  • failure mechanisms
  • damage/degradation
  • digital/adaptive signal processing
  • statistical signal processing
  • prognostics and health management
  • probabilistic damage tolerance
  • probabilistic physics of failure
  • structural integrity assessment
  • structural reliability
  • reliability testing and statistics
  • life prediction
  • degradation modeling and analysis
  • structural health monitoring
  • system diagnostics

Dr. Dong Wang
Dr. Shun-Peng Zhu
Prof. Dr. Xiancheng Zhang
Prof. Dr. Gang Chen
Dr. José A.F.O. Correia
Dr. Guian Qian
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 papers will be 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 monthly 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 1600 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.

Published Papers (2 papers)

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Open AccessFeature PaperArticle A Non-Probabilistic Solution for Uncertainty and Sensitivity Analysis on Techno-Economic Assessments of Biodiesel Production with Interval Uncertainties
Energies 2018, 11(3), 588; https://doi.org/10.3390/en11030588
Received: 19 January 2018 / Revised: 28 February 2018 / Accepted: 1 March 2018 / Published: 8 March 2018
Cited by 4 | PDF Full-text (1456 KB) | HTML Full-text | XML Full-text
Techno-economic assessments (TEA) of biodiesel production may comply with various economic and technical uncertainties during the lifespan of the project, resulting in the variation of many parameters associated with biodiesel production, including price of biodiesel, feedstock price, and rate of interest. Engineers may
[...] Read more.
Techno-economic assessments (TEA) of biodiesel production may comply with various economic and technical uncertainties during the lifespan of the project, resulting in the variation of many parameters associated with biodiesel production, including price of biodiesel, feedstock price, and rate of interest. Engineers may only collect very limited information on these uncertain parameters such as their variation intervals with lower and upper bound. This paper proposes a novel non-probabilistic strategy for uncertainty analysis (UA) in the TEA of biodiesel production with interval parameters, and non-probabilistic reliability index (NPRI) is employed to measure the economically feasible extent of biodiesel production. A sensitivity analysis (SA) indicator is proposed to assess the sensitivity of NPRI with regard to an individual uncertain interval parameter. The optimization method is utilized to solve NPRI and SA. Results show that NPRI in the focused biodiesel production of interest is 0.1211, and price of biodiesel, price of feedstock, and cost of operating can considerably affect TEA of biodiesel production. Full article

Figure 1

Open AccessArticle Remaining Useful Life Estimation of Aircraft Engines Using a Modified Similarity and Supporting Vector Machine (SVM) Approach
Energies 2018, 11(1), 28; https://doi.org/10.3390/en11010028
Received: 25 November 2017 / Revised: 13 December 2017 / Accepted: 18 December 2017 / Published: 23 December 2017
PDF Full-text (2080 KB) | HTML Full-text | XML Full-text
As the main power source for aircrafts, the reliability of an aero engine is critical for ensuring the safety of aircrafts. Prognostics and health management (PHM) on an aero engine can not only improve its safety, maintenance strategy and availability, but also reduce
[...] Read more.
As the main power source for aircrafts, the reliability of an aero engine is critical for ensuring the safety of aircrafts. Prognostics and health management (PHM) on an aero engine can not only improve its safety, maintenance strategy and availability, but also reduce its operation and maintenance costs. Residual useful life (RUL) estimation is a key technology in the research of PHM. According to monitored performance data from the engine’s different positions, how to estimate RUL of an aircraft engine by utilizing these data is a challenge for ensuring the engine integrity and safety. In this paper, a framework for RUL estimation of an aircraft engine is proposed by using the whole lifecycle data and performance-deteriorated parameter data without failures based on the theory of similarity and supporting vector machine (SVM). Moreover, a new state of health indicator is introduced for the aircraft engine based on the preprocessing of raw data. Finally, the proposed method is validated by using 2008 PHM data challenge competition data, which shows its effectiveness and practicality. Full article

Figure 1

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Tentative title: Online Real-Time Monitoring System through Using Adaptive Angular-Velocity VKF Order Tracking

Author: Min-chun Pan

E-Mail: pan_minc@cc.ncu.edu.tw

Affiliation: Department of Mechanical Engineering, National Central University, No. 300, Jhongda Rd., Jhongli 320, Taiwan

Abstract: When a rotary machine is running, from which the acquired vibro-acoustic signals enable to reveal its operation status and health condition. The study proposed a DSP-based adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm with an online real-time nature for signal interpretation and machine condition monitoring. Theoretical derivation and numerical implementation of computation schemes are briefly introduced. An online real-time monitoring system based on the AV2KF_OT algorithm, which was implemented through both a digital signal processor (DSP) and a user interface coded by using
LabVIEWâ , was developed. Two experimental tasks were applied to justify the proposed technique, including (i) the detection of startup on the fluid-induced whirl performed through a journal-bearing rotor rig, and (ii) the decoupling of crossing orders from the measured signals of a multi-axle ball-bearing bench.

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