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Applied Sciences
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Modelling Dependent Failure Processes
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Modelling Dependent Failure Processes

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 May 2022) | Viewed by 5703

Special Issue Editors

Dr. Zhiguo Zeng

E-Mail Website
Guest Editor
Centrale Supélec, Laboratory of Industrial Engineering, University of Paris-Saclay, 91190 Gif-sur-Yvette, France
Interests: characterization and modeling of the failure/repair/maintenance behavior of components; complex systems and their reliability; maintainability; prognostics; safety; vulnerability and security
Special Issues, Collections and Topics in MDPI journals
Dr. Jie Liu

E-Mail Website
Guest Editor
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Interests: machine learning; failure prognostics; fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Dr. Qingqing Zhai

E-Mail Website
Guest Editor
School of Management, Shanghai University, Shanghai, China
Interests: industrial statistics; reliability engineering; degradation modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

One fundamental assumption in traditional reliability models is that the involved failure processes are independent of one another. This assumption, although greatly simplifying the models, does not always hold in practice. For example, it is well known from experimental data that erosion and corrosion can enhance each other, resulting in faster degradation. Another example is that when test specimens are susceptible to high temperatures and heavy loads, fatigue can interact with creep so that the specimens’ lifetimes are severely reduced. How to accurately model the failure behaviors with dependency has, then, become an important yet challenging problem in risk and reliability.

The present Special Issue is devised as a collection of articles reporting both concise reviews of recently obtained results and new findings produced in this broad research area. The topics covered include but are not limited to:

  • Dependent competing failure process;
  • Physics-of-failure-based dependent failure behavior modeling;
  • Prognostics and health management considering dependent failure behaviors;
  • System failure modeling considering component-level dependencies;
  • Maintenance optimization considering failure dependencies;
  • Life testing and accelerated life testing considering dependent failures.

Dr. Zhiguo Zeng
Dr. Jie Liu
Dr. Qingqing Zhai
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. Applied Sciences 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 2400 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

  • dependent failure
  • common cause failure
  • physics of failure
  • prognostics and health management
  • shock
  • degradation

Published Papers (2 papers)

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Research

33 pages, 4289 KiB  
Article
A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
by Hassan Kanj, Wael Hosny Fouad Aly and Sawsan Kanj
Appl. Sci. 2022, 12(10), 5062; https://0-doi-org.brum.beds.ac.uk/10.3390/app12105062 - 17 May 2022
Cited by 11 | Viewed by 2316
Abstract
Static risk analysis techniques (SRATs) use event graphs and risk analysis assessment models. Those techniques are not time-based techniques and hence are inadequate to model dynamic stochastic systems. This paper proposes a novel dynamic approach to model such stochastic systems using Dynamic Fault [...] Read more.
Static risk analysis techniques (SRATs) use event graphs and risk analysis assessment models. Those techniques are not time-based techniques and hence are inadequate to model dynamic stochastic systems. This paper proposes a novel dynamic approach to model such stochastic systems using Dynamic Fault Trees (DFT). The proposed model is called Generic Dynamic Agent-Based Model (GDABM) for risk analysis. GDABM is built on top of the well-known Agent-Based Modeling and Simulation (ABMS) technique. GDABM can model the dynamic system agents in both nominal (failure-free) and degraded (failure) modes. GDABM shows the propagation of failure between system elements and provides complete information about the system’s configurations. In this paper, a complete detailed case study is provided to show the GDABM capabilities to model and study the risk analysis for such dynamic systems. In the case study, the GDABM models the risk analysis for a chemical reactor/operator and performs a complete risk analysis for the entire system. The GDABM managed to simulate the dynamic behavior of the system’s components successfully using Repast Simphony 2.0. Detailed agent behavioral modes and failure modes are provided with various scenarios, including different time stamps. The proposed GDABM is compared to a reference model. The reference model is referred to as the ABM model. GDABM has given very promising results. A comparison study was performed on three performance measures. The performance measures used are (1) Accuracy, (2) response time, and (3) execution time. GDABM has outperformed the reference model by 15% in terms of accuracy and by 27% in terms of response time. GDABM incurs a slightly higher execution time (13%) when compared to the ABM reference model. It can be concluded that GDABM can deliver accepted performance in terms of accuracy and response time without incurring much processing overhead. Full article
(This article belongs to the Special Issue Modelling Dependent Failure Processes)
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15 pages, 3184 KiB  
Article
Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model
by Yuanxing Huang, Zhiyuan Lu, Wei Dai, Weifang Zhang and Bin Wang
Appl. Sci. 2021, 11(11), 5011; https://0-doi-org.brum.beds.ac.uk/10.3390/app11115011 - 28 May 2021
Cited by 14 | Viewed by 2661
Abstract
In manufacturing, cutting tools gradually wear out during the cutting process and decrease in cutting precision. A cutting tool has to be replaced if its degradation exceeds a certain threshold, which is determined by the required cutting precision. To effectively schedule production and [...] Read more.
In manufacturing, cutting tools gradually wear out during the cutting process and decrease in cutting precision. A cutting tool has to be replaced if its degradation exceeds a certain threshold, which is determined by the required cutting precision. To effectively schedule production and maintenance actions, it is vital to model the wear process of cutting tools and predict their remaining useful life (RUL). However, it is difficult to determine the RUL of cutting tools with cutting precision as a failure criterion, as cutting precision is not directly measurable. This paper proposed a RUL prediction method for a cutting tool, developed based on a degradation model, with the roughness of the cutting surface as a failure criterion. The surface roughness was linked to the wearing process of a cutting tool through a random threshold, and accounts for the impact of the dynamic working environment and variable materials of working pieces. The wear process is modeled using a random-effects inverse Gaussian (IG) process. The degradation rate is assumed to be unit-specific, considering the dynamic wear mechanism and a heterogeneous population. To adaptively update the model parameters for online RUL prediction, an expectation–maximization (EM) algorithm has been developed. The proposed method is illustrated using an example study. The experiments were performed on specimens of 7109 aluminum alloy by milling in the normalized state. The results reveal that the proposed method effectively evaluates the RUL of cutting tools according to the specified surface roughness, therefore improving cutting quality and efficiency. Full article
(This article belongs to the Special Issue Modelling Dependent Failure Processes)
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