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Machines
Special Issues
Reliability Evaluation for Industrial Systems: State of the Art
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Reliability Evaluation for Industrial Systems: State of the Art

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Industrial Systems".

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 7762

Special Issue Editors

Prof. Dr. Hongshuang Li

E-Mail Website
Guest Editor
College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Interests: robust design optimization; reliability based design stochastic optimization; structural optimization
Prof. Dr. Yan-Fu Li

E-Mail Website
Guest Editor
Department of Industrial Engineering, Tsinghua University, 100084 Beijing, China
Interests: system reliability evaluation; RAM (reliability, availability, and maintainability) optimization; prognostics and health management (PHM)
Prof. Dr. Xufeng Zhao

E-Mail Website
Guest Editor
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Interests: probability theory; stochastic process; reliability and maintenance theory; and applications in computer and industrial systems

Special Issue Information

Dear Colleagues,

The complexity of industrial systems and the high requirements for mission reliability have posed great challenges for reliability evaluation and the design of all types of machines. Therefore, effective modeling, simulation techniques, and methods for assisting reliability evaluation and design have been demanding. At the same time, failure physics analysis, reliability testing techniques, and effective data processing methods are required for verification and/or support of the assessment of design of those systems. With this Special Issue, we intend to collect state-of-the-art developments on reliability theories and engineering practices related to industrial systems and to highlight important directions as well as challenges for further development.

The new wave of big data has posed new challenges to the reliability research community, given that traditional reliability models/methods were developed upon small/medium sized datasets. Therefore, new methods for big data such as deep learning need to be integrated into reliability models to cope with the new challenges.

This Special Issue will focus on but is not limited to the following topics:

  • reliability modeling
  • reliability simulations
  • reliability testing
  • failure modes
  • failure physics of machines
  • system reliability evaluation
  • reliability prediction and improvement
  • structural reliability analysis
  • design for reliability
  • maintenance modeling
  • design for maintainability
  • resilient design
  • robust design
  • reliability techniques
  • reliability-centered maintenance
  • accelerated testing
  • fault tolerance systems
  • risk analysis
  • maintenance 4.0
  • built-in redundancy
  • prognostics and health management
  • predicative maintenance

Prof. Dr. Hongshuang Li
Prof. Dr. Yan-Fu Li
Prof. Dr. Xufeng Zhao
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. Machines 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 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.

Published Papers (4 papers)

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Research

21 pages, 3950 KiB  
Article
Sequential Reliability Analysis for the Adjusting Mechanism of Tail Nozzle Considering Wear Degradation
by Huanhuan Hu, Pan Wang and Hanyuan Zhou
Machines 2022, 10(8), 613; https://doi.org/10.3390/machines10080613 - 26 Jul 2022
Cited by 2 | Viewed by 1205
Abstract
The adjusting mechanism is an important part of an aero engine, and the wear degradation of clearance is widely present in its hinges. In this work, an adjusting mechanism with hinge clearance is analyzed by dynamic simulation and the wear depth is predicted [...] Read more.
The adjusting mechanism is an important part of an aero engine, and the wear degradation of clearance is widely present in its hinges. In this work, an adjusting mechanism with hinge clearance is analyzed by dynamic simulation and the wear depth is predicted precisely using a wear model. Based on that, a sequential reliability analysis of motion accuracy is carried out. In order to avoid the expensive computational cost of simulation, the adaptive radial-based importance sampling method combined with the adaptive Kriging model (AK-ARBIS) is employed, which describes the decrease of reliability in the standard normal space sphere by sphere with the updated Kriging model. To further utilize the information about each state of wear degradation, the advanced AK-ARBIS method is investigated. Through analytical examples of two typical mechanisms and the engineering application of the adjustment mechanism, the results show that the calculation cost of the sequential reliability analysis under different states can be effectively reduced. Full article
(This article belongs to the Special Issue Reliability Evaluation for Industrial Systems: State of the Art)
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21 pages, 6230 KiB  
Article
Multidisciplinary Collaborative Design and Optimization of Turbine Rotors Considering Aleatory and Interval Mixed Uncertainty under a SORA Framework
by Rong Yuan, Haiqing Li, Tianwen Xie, Zhiyuan Lv, Debiao Meng and Wenke Yang
Machines 2022, 10(6), 445; https://doi.org/10.3390/machines10060445 - 5 Jun 2022
Cited by 1 | Viewed by 1605
Abstract
The turbine rotor is the key component of the turbine, which has a great impact on the construction cost and power generation efficiency of an entire hydropower station. Receiving the torque of the runner transmission and completing the specified power generation is its [...] Read more.
The turbine rotor is the key component of the turbine, which has a great impact on the construction cost and power generation efficiency of an entire hydropower station. Receiving the torque of the runner transmission and completing the specified power generation is its main function. There are many uncertain factors in the design, manufacture, and operation environment of a turbine rotor. Therefore, it is necessary to optimize the mechanism on the premise of ensuring that the mechanical system meets high reliability and high safety levels. This article uses the multidisciplinary reliability analysis and optimization method under random and interval uncertainty to quantitatively analyze the uncertainty factors, and then optimally solves the RBMDO problem of the turbine rotor mechanism. Through the finite element simulation analysis of the optimized design scheme, the rationality and feasibility of the obtained results are further verified. Full article
(This article belongs to the Special Issue Reliability Evaluation for Industrial Systems: State of the Art)
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22 pages, 9673 KiB  
Article
Fracture Mechanism Analysis and Design Optimization of a Wheelset Lifting Mechanism Based on Experiments and Simulations
by Pengpeng Zhi, Zhonglai Wang, Zongrui Tian, Junwen Lu, Jiang Wu, Xinkai Guo and Zhijie Liu
Machines 2022, 10(5), 397; https://doi.org/10.3390/machines10050397 - 19 May 2022
Cited by 2 | Viewed by 1706
Abstract
In this study, material and dynamic stress experiments are combined with finite element (FE) simulations to reveal the fracture mechanism of the wheelset lifting apparatus, and a structural design optimization scheme based on the double-layer Kriging surrogate model is proposed. The fracture mechanism [...] Read more.
In this study, material and dynamic stress experiments are combined with finite element (FE) simulations to reveal the fracture mechanism of the wheelset lifting apparatus, and a structural design optimization scheme based on the double-layer Kriging surrogate model is proposed. The fracture mechanism of the wheelset lifting apparatus is first clarified through the material analysis of macro/micro and dynamic stress tests. Static strength and modal analyses are then performed to perfect the mechanism analysis in terms of structural performance. An efficient, robust, fatigue design optimization method based on the double-layer Kriging surrogate model and improved non-dominated sorting genetic algorithm II (NSGA-II) is finally proposed to improve the original design scheme. For the wheelset lifting mechanism’s fracture, the crack source is found on the transition fillet surface of the lifting lug and lifting ring, where the fracture has the characteristics of two-way, multisource, high-cycle, low-stress fatigue. It is further revealed that the vibration fatigue occurring at the point of maximum stress is the main cause of the fracture. The effectiveness of the proposed design optimization method is validated via comparative analysis. Full article
(This article belongs to the Special Issue Reliability Evaluation for Industrial Systems: State of the Art)
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20 pages, 4494 KiB  
Article
Functional Safety Analysis and Design of Sensors in Robot Joint Drive System
by Lingyu Chen, Dapeng Fan, Jieji Zheng and Xin Xie
Machines 2022, 10(5), 360; https://doi.org/10.3390/machines10050360 - 10 May 2022
Cited by 6 | Viewed by 2144
Abstract
The reliable operation of the sensors of robot joint drive systems (RJDs) is a key factor in ensuring the safety of equipment and personnel. Over the years, additional safety-related systems have been designed to prevent safety incidents caused by robot failures, ignoring the [...] Read more.
The reliable operation of the sensors of robot joint drive systems (RJDs) is a key factor in ensuring the safety of equipment and personnel. Over the years, additional safety-related systems have been designed to prevent safety incidents caused by robot failures, ignoring the functional safety issues of the robot sensors themselves. In view of this, based on IEC61508, a functional safety analysis and design method for sensors of RJDs is proposed in this paper. Firstly, the hazard analysis and risk assessment clarified the goals that the safety protection function of the RJD’s sensor should achieve. Then, by establishing the motor drive model and transmission model, a model-based sensor fault diagnosis and isolation strategy is proposed. Considering the fault-tolerant operation of system, a fail-operational hardware architecture of the safety-related system is designed. Markov analysis shows that the safety integrity level (SIL) of safety-related systems can reach SIL3. Finally, experiments are designed to validate the proposed fault diagnosis and fault tolerance strategy. The results show that the safety-related system can effectively locate sensor failures, realize fault-tolerant control when a single sensor fails and perform safe torque off (STO) protection when multiple sensors fail. Full article
(This article belongs to the Special Issue Reliability Evaluation for Industrial Systems: State of the Art)
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