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Processes
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Reliability and Engineering Applications (Volume II)
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Reliability and Engineering Applications (Volume II)

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 10010

Special Issue Editors

Dr. Hongyun So

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: MEMS (micro-electro-mechanical systems); harsh-environment sensors; biomedical microdevices; microfluidics; microfabrication; electronics cooling
Special Issues, Collections and Topics in MDPI journals
Dr. Jong-Won Park

E-Mail Website
Guest Editor
Department of Reliability Assessment, Korea Institute of Machinery and Materials, Daejeon 34103, Republic of Korea
Interests: accelerated life and degradation test; fuzzy control; sensor fusion; robot dynamics and manipulator design; hydropneumatic system
Special Issues, Collections and Topics in MDPI journals
Dr. Sunghan Kim

E-Mail Website
Guest Editor
School of Mechanical Engineering, Chung-Ang University, Dongjak-gu, Seoul 06974, Republic of Korea
Interests: bionanocomposties; micro/nanomechanics; sensors; nanofluid; surface/interface engineering; tribology; finite element analysis; hydrogen energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Reliable processes ranging from the nano to macro scale are critical in various fields, including chemical, mechanical, electrical, material, and biomedical engineering. Recently, theoretical and experimental reliability analysis has emerged as an advanced technology for reliability assessment, prognostics, and health management for various materials, systems, and industrial products.

This Special Issue, “Reliability and Engineering Applications (Volume II)”, aims to provide insights into recently developed advanced reliable processes, techniques, and applications that address challenging engineering problems. Unique and novel manufacturing processes, modeling, and numerical methods for any industrial application are of special interest. We invite contributions to this Special Issue on topics including, but not limited to, the following:

1. Materials and Characterization:

  • Fracture and failure;
  • Fatigue;
  • Corrosion;
  • Metals;
  • Alloys/composites.
2. Reliable Processes and Methods:
  • Reliability analysis and modeling;
  • Design for reliability;
  • Numerical methods;
  • Risk assessment and safety;
  • Non-destructive testing and evaluation (NDT&E).
3. PHM (Prognostics and Health Management):
  • Advanced sensors;
  • Big data analytics;
  • Prognostics methodology;
  • Smart manufacturing;
  • Structural health management;
  • Accelerated life and degradation tests.
4. Applications:
  • Mechanical engineering (manufacturing, devices, MEMS, robotics, microfluidics, etc.);
  • Aerospace engineering (aircraft, missile, satellites, drones, etc.);
  • Chemical engineering (synthesis, separation, mixing, mass transfer, etc.).
  • Energy systems (batteries, turbine, solar cell, electrical vehicles, etc.).

Dr. Hongyun So
Dr. Jong-Won Park
Dr. Sunghan Kim
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. Processes 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.

Keywords

  • reliability
  • manufacturing
  • materials
  • health management

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Published Papers (6 papers)

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Research

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13 pages, 2863 KiB  
Article
Performance Degradation Modeling and Continuous Worktime Assessment of Ultrasonic Vibration Systems
by Ruoyu Wang, Lei You and Xiaoping Hu
Processes 2024, 12(3), 439; https://doi.org/10.3390/pr12030439 - 21 Feb 2024
Viewed by 1037
Abstract
In order to assess the stable operating duration of an ultrasonic vibration system, a reliability-based analysis method for the stability of the ultrasonic vibration system is proposed. Firstly, the failure mechanisms of the ultrasonic vibration system are analyzed, and the resonant frequency and [...] Read more.
In order to assess the stable operating duration of an ultrasonic vibration system, a reliability-based analysis method for the stability of the ultrasonic vibration system is proposed. Firstly, the failure mechanisms of the ultrasonic vibration system are analyzed, and the resonant frequency and amplitude are selected as two degradation features of the system. Subsequently, accelerated degradation experiments under different force loads were conducted, and the degradation model of the ultrasonic vibration system was established by comparing experimental data with degradation, distribution, and acceleration models. Finally, Copula functions were introduced to connect the two degradation features, resonant frequency, and amplitude, and lifetime curves were plotted under the influence of univariate and bivariate degradation factors. Through the analysis of the lifetime curves, the conclusion is drawn that the decay of amplitude is the primary indicator of system lifetime, and it is predicted that the developed ultrasonic vibration system can operate continuously and stably for 26.69 h. This research is of great significance for enhancing the reliability and lifespan management of ultrasonic vibration systems. Full article
(This article belongs to the Special Issue Reliability and Engineering Applications (Volume II))
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23 pages, 4915 KiB  
Article
Research on a Small-Sample Fault Diagnosis Method for UAV Engines Based on an MSSST and ACS-BPNN Optimized Deep Convolutional Network
by Siyu Li, Zichang Liu, Yunbin Yan, Kai Han, Yueming Han, Xinyu Miao, Zhonghua Cheng and Shifei Ma
Processes 2024, 12(2), 367; https://doi.org/10.3390/pr12020367 - 10 Feb 2024
Cited by 1 | Viewed by 1037
Abstract
Regarding the difficulty of extracting fault information in the faulty status of UAV (unmanned aerial vehicle) engines and the high time cost and large data requirement of the existing deep learning fault diagnosis algorithms with many training parameters, in this paper, a small-sample [...] Read more.
Regarding the difficulty of extracting fault information in the faulty status of UAV (unmanned aerial vehicle) engines and the high time cost and large data requirement of the existing deep learning fault diagnosis algorithms with many training parameters, in this paper, a small-sample transfer learning fault diagnosis algorithm is proposed. First, vibration signals under the engine fault status are converted into a two-dimensional time-frequency map by multiple simultaneous squeezing S-transform (MSSST), which reduces the randomness of manually extracted features. Second, to address the problems of slow network model training and large data sample requirement, a transfer diagnosis strategy using the fine-tuned time-frequency map samples as the pre-training model of the ResNet-18 convolutional neural network is proposed. In addition, in order to improve the training effect of the network model, an agent model is introduced to optimize the hyperparameter network autonomously. Finally, experiments show that the algorithm proposed in this paper can obtain high classification accuracy in fault diagnosis of UAV engines compared to other commonly used methods, with a classification accuracy of faults as high as 97.1751%; in addition, we show that it maintains a very stable small-sample migratory learning capability under this condition. Full article
(This article belongs to the Special Issue Reliability and Engineering Applications (Volume II))
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15 pages, 1515 KiB  
Article
A Full-State Reliability Analysis Method for Remanufactured Machine Tools Based on Meta Action and a Markov Chain Using an Exercise Machine (EM) as an Example
by Yueping Luo and Yongmao Xiao
Processes 2023, 11(9), 2794; https://doi.org/10.3390/pr11092794 - 20 Sep 2023
Viewed by 924
Abstract
The reliability of an RMT can be regarded as an important indicator customers can use to recognize its quality; however, it is difficult to implement a full-state reliability analysis of an RMT due to its complicated structural functions. Therefore, a full-state reliability analysis [...] Read more.
The reliability of an RMT can be regarded as an important indicator customers can use to recognize its quality; however, it is difficult to implement a full-state reliability analysis of an RMT due to its complicated structural functions. Therefore, a full-state reliability analysis model is proposed herein based on meta action (MA) and a Markov chain for remanufactured exercise machine tools (REMTs). First, an analysis was carried out on individual levels by integrating the MAU decomposition method, and an MAU fault tree model was established layer by layer for the REMT. Second, full-state modeling was performed in view of the MAU characteristics of the REMT, whose operation processes are divided into MAU normal and failure states. A Markov decision-making process was introduced to integrate MAU states and establish our model, which was solved by means of an analytical method for the evaluation of reliability. Finally, an example of a remanufactured machine tool spindle is given to verify the effectiveness of the method. Full article
(This article belongs to the Special Issue Reliability and Engineering Applications (Volume II))
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20 pages, 6135 KiB  
Article
Gearbox Fault Diagnosis Based on Optimized Stacked Denoising Auto Encoder and Kernel Extreme Learning Machine
by Zhenghao Wu, Hao Yan, Xianbiao Zhan, Liang Wen and Xisheng Jia
Processes 2023, 11(7), 1936; https://doi.org/10.3390/pr11071936 - 27 Jun 2023
Cited by 3 | Viewed by 1222
Abstract
The gearbox is one of the key components of many large mechanical transmission devices. Due to the complex working environment, the vibration signal stability of the gear box is poor, the fault feature extraction is difficult, and the fault diagnosis accuracy makes it [...] Read more.
The gearbox is one of the key components of many large mechanical transmission devices. Due to the complex working environment, the vibration signal stability of the gear box is poor, the fault feature extraction is difficult, and the fault diagnosis accuracy makes it difficult to meet the expected requirements. To solve this problem, this paper proposes a gearbox fault diagnosis method based on an optimized stacked denoising auto encoder (SDAE) and kernel extreme learning machine (KELM). Firstly, the particle swarm optimization algorithm in adaptive weight (SAPSO) was adopted to optimize the SDAE network structure, and the number of hidden layer nodes, learning rate, noise addition ratio and iteration times were adaptively obtained to make SDAE obtain the best network structure. Then, the best SDAE network structure was used to extract the deep feature information of weak faults in the original signal. Finally, the extracted fault features are fed into KELM for fault classification. Experimental results show that the classification accuracy of the proposed method can reach 97.2% under the condition of low signal-to-noise ratio, which shows the effectiveness and robustness of the proposed method compared with other diagnostic methods. Full article
(This article belongs to the Special Issue Reliability and Engineering Applications (Volume II))
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20 pages, 5051 KiB  
Article
Effect of Flexible Operation on Residual Life of High-Temperature Components of Power Plants
by Jun Heo, Mingyu Park, Jeong-Myun Kim, Dong-Won Jang and Ji-Hoon Han
Processes 2023, 11(6), 1679; https://doi.org/10.3390/pr11061679 - 31 May 2023
Cited by 1 | Viewed by 1399
Abstract
Electricity generation from renewable energy sources is emerging as a result of global carbon emission reduction policies. However, most renewable energy sources are non-dispatchable and cannot be adjusted to meet the fluctuating electricity demands of society. A flexible operation process has been proposed [...] Read more.
Electricity generation from renewable energy sources is emerging as a result of global carbon emission reduction policies. However, most renewable energy sources are non-dispatchable and cannot be adjusted to meet the fluctuating electricity demands of society. A flexible operation process has been proposed as an effective solution to compensate for the unstable nature of renewable energy sources. Thermal load fluctuations during flexible operation may cause creep–fatigue damage to the high-temperature components of thermal power plants, as they are designed with a focus on creep damage under a constant power level. This study investigated the residual life of high-temperature components, such as a superheater tube and a reheater header, to failure under flexible operation conditions using finite element analysis and empirical models. First, we determined an analytical solution for the straightened superheater tube under thermal conditions and compared it with the numerical solution to verify the numerical models. Through the verified finite element model, the creep–fatigue life of the reheater header was estimated by considering flexible operation factors and employing the Coffin–Manson and Larson–Miller models. Although fatigue damage increases with decreasing minimum load and ramp rate, we confirmed that creep damage significantly affects the residual life during flexible operation. In addition, a surrogate model was proposed to evaluate the residual life of the reheater as a function of the flexible operation factors using the machine learning methodology, based on the results of finite element methods. It can be used to predict its residual life without performing complex thermo-structural analysis and relying on empirical models for fatigue and creep life. We expect our findings to contribute to the efficient operation of thermal power plants by optimizing the flexible operation factors. Full article
(This article belongs to the Special Issue Reliability and Engineering Applications (Volume II))
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Review

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27 pages, 3312 KiB  
Review
Research Progress and Development Trend of Prognostics and Health Management Key Technologies for Equipment Diesel Engine
by Zichang Liu, Cuixuan Zhang, Enzhi Dong, Rongcai Wang, Siyu Li and Yueming Han
Processes 2023, 11(7), 1972; https://doi.org/10.3390/pr11071972 - 29 Jun 2023
Cited by 6 | Viewed by 2569
Abstract
The diesel engine, as the main power source of equipment, faces practical problems in the maintenance process, such as difficulty in fault location and a lack of preventive maintenance techniques. Currently, breakdown maintenance and cyclical preventive maintenance are the main means of maintenance [...] Read more.
The diesel engine, as the main power source of equipment, faces practical problems in the maintenance process, such as difficulty in fault location and a lack of preventive maintenance techniques. Currently, breakdown maintenance and cyclical preventive maintenance are the main means of maintenance support after a diesel engine failure, but these methods require professional maintenance personnel to carry out manual fault diagnosis, which is time-consuming. Prognostics and health management (PHM), as a new technology in the field of equipment maintenance support, has significant advantages in improving equipment reliability and safety, enhancing equipment maintenance support capability, and reducing maintenance support costs. In view of this, when introducing PHM into diesel engine maintenance support, the research progress and development trend of the key technologies of PHM for diesel engines are carried out with the objective of achieving precise maintenance and scientific management of diesel engines, and the key technologies demand traction. Firstly, the development history of PHM technology is reviewed, and its basic concept and main functions are introduced. Secondly, the system architecture of PHM for diesel engines is constructed, and its key technologies are summarized. Then, the research progress in the field of PHM for diesel engines is reviewed from four aspects: data acquisition, data processing, fault diagnosis, and health status assessment. Finally, the challenges faced by diesel engine PHM in engineering applications are analyzed, effective solutions to address these challenges are explored, and the future development trend is foreseen. Full article
(This article belongs to the Special Issue Reliability and Engineering Applications (Volume II))
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