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Journal articles on the topic 'Industrial multi-component systems'

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Author: Grafiati

Published: 11 March 2023

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1

Zhao, Jing Rui, Kai Ming Cheng, Yong Du, and Li Jun Zhang. "Thermodynamic Calculation of Liquidus Projection of Multiple Aluminum Alloys." Materials Science Forum 913 (February 2018): 589–95. http://dx.doi.org/10.4028/www.scientific.net/msf.913.589.

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Most aluminum alloys used in industrial manufacturing are multi-component systems. Unfortunately, multi-component systems are still lack of systematic research. The lack of research is especially evident for the liquidus surface projection of the multi-component system where most of the projections are currently drawn by hand. Thermodynamic calculations were performed for the Al–Cu–Mg–Mn quaternary system and the A1–Cu–Fe–Mg–Mn quinary system. The temperatures and compositions of the liquidus invariant reactions in the A1–rich corner for the two systems were calculated and compared with the experimental data. The current work provides a straightforward and accurate expression for the liquidus surface projections of multi-component systems. The expressions will facilitate the understanding of the multi-component alloy systems that are frequently used in industrial applications.

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2

Bucaioni, Alessio, Saad Mubeen, Federico Ciccozzi, Antonio Cicchetti, and Mikael Sjödin. "Modelling multi-criticality vehicular software systems: evolution of an industrial component model." Software and Systems Modeling 19, no. 5 (April 30, 2020): 1283–302. http://dx.doi.org/10.1007/s10270-020-00795-5.

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Abstract Software in modern vehicles consists of multi-criticality functions, where a function can be safety-critical with stringent real-time requirements, less critical from the vehicle operation perspective, but still with real-time requirements, or not critical at all. Next-generation autonomous vehicles will require higher computational power to run multi-criticality functions and such a power can only be provided by parallel computing platforms such as multi-core architectures. However, current model-based software development solutions and related modelling languages have not been designed to effectively deal with challenges specific of multi-core, such as core-interdependency and controlled allocation of software to hardware. In this paper, we report on the evolution of the Rubus Component Model for the modelling, analysis, and development of vehicular software systems with multi-criticality for deployment on multi-core platforms. Our goal is to provide a lightweight and technology-preserving transition from model-based software development for single-core to multi-core. This is achieved by evolving the Rubus Component Model to capture explicit concepts for multi-core and parallel hardware and for expressing variable criticality of software functions. The paper illustrates these contributions through an industrial application in the vehicular domain.

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3

Niu, Gang, and Junjie Jiang. "Prognostic control-enhanced maintenance optimization for multi-component systems." Reliability Engineering & System Safety 168 (December 2017): 218–26. http://dx.doi.org/10.1016/j.ress.2017.04.011.

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4

Kramer, Vivian, and Annemarie Reimschuessel. "Characterization of multi-component polymer blends by multi-step staining techniques." Proceedings, annual meeting, Electron Microscopy Society of America 46 (1988): 930–31. http://dx.doi.org/10.1017/s0424820100106715.

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Morphology is one of the important parameters that affects polymer properties and performance. A standard procedure used when characterizing the morphology of many polymer blends by TEM is to stain one of the components with a heavy element rendering it opaque to the electron beam. Among the more common stains used are phosphotungstic acid (PTA) for nylon and OSO4 for unsaturated rubbers. Although these stains work well for simple two component systems, innovative techniques are often essential when the material contains three or more phases, as necessitated by ever expanding industrial demands. This paper describes two such techniques used in characterizing three multi-phase polymer systems.One multi-component system consisted of a polybutadiene-polymer blend, nylon, and a saturated rubber. OSO4 reacts with the double bonds of the butadiene rendering it opaque while slightly darkening its matrix. PTA reacts with the amine groups of nylon rendering it opaque.

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5

Adjoul, Oussama, Khaled Benfriha, Chawki El Zant, and Améziane Aoussat. "Algorithmic Strategy for Simultaneous Optimization of Design and Maintenance of Multi-Component Industrial Systems." Reliability Engineering & System Safety 208 (April 2021): 107364. http://dx.doi.org/10.1016/j.ress.2020.107364.

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6

Si, Shubin, Gregory Levitin, Hongyan Dui, and Shudong Sun. "Component state-based integrated importance measure for multi-state systems." Reliability Engineering & System Safety 116 (August 2013): 75–83. http://dx.doi.org/10.1016/j.ress.2013.02.023.

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7

Van Horenbeek, Adriaan, and Liliane Pintelon. "A dynamic predictive maintenance policy for complex multi-component systems." Reliability Engineering & System Safety 120 (December 2013): 39–50. http://dx.doi.org/10.1016/j.ress.2013.02.029.

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8

Zhu, Zhicheng, and Yisha Xiang. "Condition-based maintenance for multi-component systems: Modeling, structural properties, and algorithms." IISE Transactions 53, no. 1 (April 27, 2020): 88–100. http://dx.doi.org/10.1080/24725854.2020.1741740.

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9

Assaf, Roy, Phuc Do, Samia Nefti-Meziani, and Philip Scarf. "Wear rate–state interactions within a multi-component system: a study of a gearbox-accelerated life testing platform." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 4 (August 2018): 425–34. http://dx.doi.org/10.1177/1748006x18764061.

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The degradation process of complex multi-component systems is highly stochastic in nature. A major side effect of this complexity is that components of such systems may have unexpected reduced life and faults and failures that decrease the reliability of multi-component systems in industrial environments. In this work, we provide maintenance practitioners with an explanation of the nature of some of these unpredictable events, namely, the degradation interactions that take place between components. We begin by presenting a general wear model where the degradation process of a component may be dependent on the operating conditions, the component’s own state and the state of the other components. We then present our methodology for extracting accurate health indicators from multi-component systems by means of a time–frequency domain analysis. Finally, we present a multi-component system degradation analysis of experimental data generated by a gearbox-accelerated life testing platform. In doing so, we demonstrate the importance of modelling the interactions between the system components by showing their effect on component lifetime reduction.

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10

Park, Jong-Kweon, Seung-Kook Ro, Byung-Sub Kim, Woo-Cheol Shin, and Hyeon-Hwa Lee. "Precision Component Technologies for Microfactory Systems Developed at KIMM." International Journal of Automation Technology 4, no. 2 (March 5, 2010): 127–37. http://dx.doi.org/10.20965/ijat.2010.p0127.

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This paper presents some of the recent research activities in the ongoing project for the development of high-tech component technologies for Microfactories at the Korea Institute of Machinery and Materials. Component technologies for Microfactory systems are key technologies for future development in this area, both in the R&D and industrial sectors, since many components are not currently available on the market. In this project, some key component technologies for Microfactory systems were chosen for development: multi-axis miniature air-bearing stages (including a planar motion stage), miniature high-speed spindles using air and magnetic bearings, shape memory alloy-based clamping devices for micro-tools and workpieces, optical multi-axis sensors for monitoring micro-tools and spindle rotation, and a numerical control system for the realization of flexible control algorithms. The design concepts and results for selected component technologies are briefly illustrated in this paper.

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11

Fu, Yichuan, Zhiwei Gao, Yuanhong Liu, Aihua Zhang, and Xiuxia Yin. "Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques." Processes 8, no. 9 (September 1, 2020): 1066. http://dx.doi.org/10.3390/pr8091066.

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In response to the high demand of the operation reliability and predictive maintenance, health monitoring and fault diagnosis and classification have been paramount for complex industrial systems (e.g., wind turbine energy systems). In this study, data-driven fault diagnosis and fault classification strategies are addressed for wind turbine energy systems under various faulty scenarios. A novel algorithm is addressed by integrating fast Fourier transform and uncorrelated multi-linear principal component analysis techniques in order to achieve effective three-dimensional space visualization for fault diagnosis and classification under a variety of actuator and sensor faulty scenarios in 4.8 MW wind turbine benchmark systems. Moreover, comparison studies are implemented by using multi-linear principal component analysis with and without fast Fourier transform, and uncorrelated multi-linear principal component analysis with and without fast Fourier transformation data pre-processing, respectively. The effectiveness of the proposed algorithm is demonstrated and validated via the wind turbine benchmark.

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12

Zinicovscaia, Inga, Alexey Safonov, Varvara Tregubova, Victor Ilin, Liliana Cepoi, Tatiana Chiriac, Ludmila Rudi, and Marina V. Frontasyeva. "Uptake of Metals from Single and Multi-Component Systems by Spirulina Platensis Biomass." Ecological Chemistry and Engineering S 23, no. 3 (September 1, 2016): 401–12. http://dx.doi.org/10.1515/eces-2016-0028.

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Abstract Spirulina platensis biomass is widely applied for different technological purposes. The process of lanthanum, chromium, uranium and vanadium accumulation and biosorption by Spirulina platensis biomass from single- and multi-component systems was studied. The influence of multi-component system on the spirulina biomass growth was less pronounced in comparison with the single-component ones. To trace the uptake of metals by spirulina biomass the neutron activation analysis was used. In the experiment on the accumulation the efficiency of studied metal uptake changes in the following order: La(V) > Cr(III) > U(VI) > V(V) (single-metal solutions) and Cr(III) > La(V) > V(V) > U(VI) (multi-metal system). The process of metals biosorption was studied during a two-hour experiment. The highest rate of metal adsorption for single-component systems was observed for lanthanum and chromium. While for the multi-component system the significant increase of vanadium and chromium content in biomass was observed. In biosorption experiments the rate of biosorption and the Kd value were calculated for each metal. Fourier transform infrared spectroscopy was used to identify functional groups responsible for metal binding. The results of the present work show that spirulina biomass can be implemented as a low-cost sorbent for metal removal from industrial wastewater.

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13

Bian, Linkan, and Nagi Gebraeel. "Stochastic modeling and real-time prognostics for multi-component systems with degradation rate interactions." IIE Transactions 46, no. 5 (February 5, 2014): 470–82. http://dx.doi.org/10.1080/0740817x.2013.812269.

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14

Eppert, Joseph J., Richard E. DeVor, Shiv G. Kapoor, and N. Rajagopalan. "The Effect of Chip Adsorption on Selective Depletion From a Multi-Component Synthetic Metalworking Fluid." Journal of Manufacturing Science and Engineering 125, no. 4 (November 1, 2003): 703–8. http://dx.doi.org/10.1115/1.1615794.

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Certain components found in metalworking fluid formulations are known to be prone to selective depletion via adsorption onto the surfaces of particulate generated during metal cutting operations. The characteristics of adsorption for a multi-component synthetic metalworking fluid are investigated in order to determine the affect that machining particulate contamination has on component depletion from this fluid. Experimental measurements indicate that the corrosion inhibiting component of the formulation is especially prone to depletion via the adsorption mechanism. A Langmuir isotherm/mass balance based predictive model is developed to determine the effect of chip adsorption on depletion from industrial metalworking fluid systems and is experimentally verified. An industrial case study is applied to further investigate selective component depletion via the adsorption mechanism.

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15

Dinh, Duc-Hanh, Phuc Do, and Benoit Iung. "Multi-level opportunistic predictive maintenance for multi-component systems with economic dependence and assembly/disassembly impacts." Reliability Engineering & System Safety 217 (January 2022): 108055. http://dx.doi.org/10.1016/j.ress.2021.108055.

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16

Zaretalab, Arash, Vahid Hajipour, and Madjid Tavana. "Redundancy allocation problem with multi-state component systems and reliable supplier selection." Reliability Engineering & System Safety 193 (January 2020): 106629. http://dx.doi.org/10.1016/j.ress.2019.106629.

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17

Wang, Cheng, Jianxin Xu, Hongjun Wang, and Zhenming Zhang. "A criticality importance-based spare ordering policy for multi-component degraded systems." Eksploatacja i Niezawodnosc - Maintenance and Reliability 20, no. 4 (September 18, 2018): 662–70. http://dx.doi.org/10.17531/ein.2018.4.17.

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18

Barata, J., C. Guedes Soares, M. Marseguerra, and E. Zio. "Simulation modelling of repairable multi-component deteriorating systems for ‘on condition’ maintenance optimisation." Reliability Engineering & System Safety 76, no. 3 (June 2002): 255–64. http://dx.doi.org/10.1016/s0951-8320(02)00017-0.

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19

Rath, Markus, and Ernst Kozeschnik. "Coupled Grain Growth and Precipitation Modeling in Multi-Phase Systems." Materials Science Forum 753 (March 2013): 357–60. http://dx.doi.org/10.4028/www.scientific.net/msf.753.357.

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In this work, we utilize recent software for precipitation kinetics simulation and couple it with models for grain growth. Basis of our studies is the thermo-kinetic software MatCalc, which has been designed for simulation of the evolution of precipitates in general multi-component multi-phase alloy systems. Grain growth approaches of different complexity are incorporated into the precipitation kinetics framework, investigated with respect to their coupling behavior with precipitation and precipitate dissolution and, finally, benchmarked on typical grain growth problems of industrial practice. The example presented in this work comprises austenite grain growth studies accompanying TiN and NbC precipitation/dissolution during austenitization of a Ti/Nb microalloyed steel. It is demonstrated that the present approach represents a versatile tool for analysis of simultaneous grain growth and precipitation in industrially important alloy systems.

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20

Liao, Hung-Chang. "Multi-response optimization using weighted principal component." International Journal of Advanced Manufacturing Technology 27, no. 7-8 (February 23, 2005): 720–25. http://dx.doi.org/10.1007/s00170-004-2248-7.

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21

Piotter, V., J. Prokop, H. J. Ritzhaupt-Kleissl, A. Ruh, and J. Hausselt. "Multi-component microinjection moulding-trends and developments." International Journal of Advanced Manufacturing Technology 47, no. 1-4 (March 5, 2009): 63–71. http://dx.doi.org/10.1007/s00170-009-1985-z.

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22

Chang, Fengtian, Guanghui Zhou, Chao Zhang, Zhongdong Xiao, and Chuang Wang. "A service-oriented dynamic multi-level maintenance grouping strategy based on prediction information of multi-component systems." Journal of Manufacturing Systems 53 (October 2019): 49–61. http://dx.doi.org/10.1016/j.jmsy.2019.09.005.

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23

Wang, Xiaolin, Min Xie, and Lishuai Li. "On optimal upgrade strategy for second-hand multi-component systems sold with warranty." International Journal of Production Research 57, no. 3 (July 1, 2018): 847–64. http://dx.doi.org/10.1080/00207543.2018.1488087.

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24

Zhang, Lin, Xiaohui Chen, Abdelhakim Khatab, and Youjun An. "Optimizing imperfect preventive maintenance in multi-component repairable systems under s-dependent competing risks." Reliability Engineering & System Safety 219 (March 2022): 108177. http://dx.doi.org/10.1016/j.ress.2021.108177.

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25

Nguyen, Kim-Anh, Phuc Do, and Antoine Grall. "Joint predictive maintenance and inventory strategy for multi-component systems using Birnbaum’s structural importance." Reliability Engineering & System Safety 168 (December 2017): 249–61. http://dx.doi.org/10.1016/j.ress.2017.05.034.

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26

Vu, Hai Canh, Phuc Do, Mitra Fouladirad, and Antoine Grall. "Dynamic opportunistic maintenance planning for multi-component redundant systems with various types of opportunities." Reliability Engineering & System Safety 198 (June 2020): 106854. http://dx.doi.org/10.1016/j.ress.2020.106854.

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27

Shi, Yue, Weihang Zhu, Yisha Xiang, and Qianmei Feng. "Condition-based maintenance optimization for multi-component systems subject to a system reliability requirement." Reliability Engineering & System Safety 202 (October 2020): 107042. http://dx.doi.org/10.1016/j.ress.2020.107042.

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28

Zhou, Xiaojun, Kaimin Huang, Lifeng Xi, and Jay Lee. "Preventive maintenance modeling for multi-component systems with considering stochastic failures and disassembly sequence." Reliability Engineering & System Safety 142 (October 2015): 231–37. http://dx.doi.org/10.1016/j.ress.2015.05.005.

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29

Liu, Qinming, and Wenyuan Lv. "Multi-component manufacturing system maintenance scheduling based on degradation information using genetic algorithm." Industrial Management & Data Systems 115, no. 8 (September 14, 2015): 1412–34. http://dx.doi.org/10.1108/imds-04-2015-0150.

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Purpose – The traditional maintenance scheduling strategies of multi-component systems may result in maintenance shortage or overage, while system degradation information is often ignored. The purpose of this paper is to propose a multi-phase model that better integrates degradation information, dependencies and maintenance at the tactical level. Design/methodology/approach – This paper proposes first a maintenance optimization model for multi-component systems with economic dependence and structural dependence. The cost of combining maintenance activities is lower than that of performing maintenance on components separately, and the downtime cost can be reduced by considering structural dependence. Degradation information and multiple maintenance actions within scheduling horizon are considered. Moreover, the maintenance resources can be integrated into the optimization model. Then, the optimization model adopting one maintenance activity is extended to multi-phase optimization model of the whole system lifetime by taking into account the cost and the expected number of downtime. Findings – The superiority of the proposed method compared with periodic maintenance is demonstrated. Thus, the values of both integrated degradation information and considering dependencies are testified. The advantage of the proposed method is highlighted in the cases of high system utilization, long maintenance durations and low maintenance costs. Originality/value – Few studies have been carried out to integrate decisions on degradation, dependencies and maintenance. Their considerations are either incomplete or not realistic enough. A more comprehensive and realistic multi-phase model is proposed in this paper, along with an iterative solution algorithm for it.

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30

Zaretalab, Arash, Mani Sharifi, Pedram Pourkarim Guilani, Sharareh Taghipour, and Seyed Taghi Akhavan Niaki. "A multi-objective model for optimizing the redundancy allocation, component supplier selection, and reliable activities for multi-state systems." Reliability Engineering & System Safety 222 (June 2022): 108394. http://dx.doi.org/10.1016/j.ress.2022.108394.

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31

Kim, Mi-Young, Shahin Atakishiyev, Housam Khalifa Bashier Babiker, Nawshad Farruque, Randy Goebel, Osmar R. Zaïane, Mohammad-Hossein Motallebi, et al. "A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence." Machine Learning and Knowledge Extraction 3, no. 4 (November 18, 2021): 900–921. http://dx.doi.org/10.3390/make3040045.

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The rapid growth of research in explainable artificial intelligence (XAI) follows on two substantial developments. First, the enormous application success of modern machine learning methods, especially deep and reinforcement learning, have created high expectations for industrial, commercial, and social value. Second, the emerging and growing concern for creating ethical and trusted AI systems, including compliance with regulatory principles to ensure transparency and trust. These two threads have created a kind of “perfect storm” of research activity, all motivated to create and deliver any set of tools and techniques to address the XAI demand. As some surveys of current XAI suggest, there is yet to appear a principled framework that respects the literature of explainability in the history of science and which provides a basis for the development of a framework for transparent XAI. We identify four foundational components, including the requirements for (1) explicit explanation knowledge representation, (2) delivery of alternative explanations, (3) adjusting explanations based on knowledge of the explainee, and (4) exploiting the advantage of interactive explanation. With those four components in mind, we intend to provide a strategic inventory of XAI requirements, demonstrate their connection to a basic history of XAI ideas, and then synthesize those ideas into a simple framework that can guide the design of AI systems that require XAI.

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32

You, Ming-Yi, and Guang Meng. "A modularized framework for predictive maintenance scheduling." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 226, no. 4 (December 21, 2011): 380–91. http://dx.doi.org/10.1177/1748006x11431209.

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This paper presents a modularized, easy-to-implement framework for predictive maintenance scheduling. With a modularization treatment of a maintenance scheduling model, a predictive maintenance scheduling model can be established by integrating components’ real-time, sensory-updated prognostics information with a classical preventive maintenance/condition-based maintenance scheduling model. With the framework, a predictive maintenance scheduling model for multi-component systems is established to illustrate the framework’s use; such a predictive maintenance scheduling model for multi-component systems has not been reported previously in the literature. A numerical example is provided to investigate the individual-orientation and dynamic updating characteristics of the optimal preventive maintenance schedules of the established predictive maintenance scheduling model and to evaluate the performance of these preventive maintenance schedules. It is hoped that the presented framework will facilitate the implementation of predictive maintenance policies in various industrial applications.

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33

Kohansal, Akram, Shirin Shoaee, and Mohammad Z. Raqab. "Estimating the stress-strength parameter in multi-component systems based on adaptive hybrid progressive censoring." International Journal of Industrial and Systems Engineering 41, no. 3 (2022): 363. http://dx.doi.org/10.1504/ijise.2022.124069.

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Kohansal, Akram, SHIRIN SHOAEE, and Mohammad Z. Raqab. "Estimating the stress-strength parameter in multi-component systems based on adaptive hybrid progressive censoring." International Journal of Industrial and Systems Engineering 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijise.2020.10039570.

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35

Schmidt-Hohagen, Frank, and Ivan Egry. "Surface Tension Measurements of Industrial Alloys." Materials Science Forum 508 (March 2006): 573–78. http://dx.doi.org/10.4028/www.scientific.net/msf.508.573.

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Industrial alloys are generally multi-component systems with volatile and highly reactive components. Consequently, their processing in the liquid state requires special precautions. Therefore, an existing electromagnetic-levitation device with a high-vacuum chamber, designed for surface tension measurements, was modified and improved. For the measurements of the surface tensions with the help of the oscillating drop technique the temperatures and the video sequences of the levitated, molten alloy were recorded without contact by pyrometer and by high-speed camera, respectively. This method yields the frequencies of the surface oscillations of the sample around the equilibrium shape and - using the modified Rayleigh law - eventually the values of the surface tension. With the help of this method two commercial industrial steels were processed in the new EML-apparatus for ESA`s Thermolab project [1]. The results of these measurements are presented and discussed

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36

Wang, Ling, Hong Xu, Jinjin Wu, Xiai Chen, and Wenbo Na. "Agent-based modeling of availability for complex multiple units systems." Journal of Quality in Maintenance Engineering 23, no. 1 (March 13, 2017): 71–81. http://dx.doi.org/10.1108/jqme-06-2013-0033.

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Purpose The purpose of this paper is to propose an availability modeling method of complex multiple units system (CMUS) based on the multi-agent technique. Design/methodology/approach Based on the multi-agent technique, this paper describes the availability model structure for CMUS and develops agent-based models of components, maintenance policies, maintenance tools, maintenance fields, and maintenance staff, as well as the communication method among the different agents. On the basis of the agent-based availability modeling theory, the availability simulation scheme of CMUS is given using MATLAB. Thus, the availability modeling theory of CMUS and its simulation method are developed. To demonstrate the applicability of the proposed availability modeling method, a numerical example is given. Findings The proposed agent-based modeling method is applicable to availability modeling of CMUS, including the modeling of component failure, maintenance tools/fields/staff, maintenance policy, and structural/economic dependence among components. Practical implications As a bottom-top, modular, expandable, and reusable modeling theory, the agent-based modeling method might be useful for availability modeling of different CMUSs in reality. Originality/value The multi-agent technique is introduced into availability modeling of multi-component systems in this paper. Thus, it is possible to model failure of many components, maintenance policies, maintenance tools, maintenance fields, and maintenance staff together for availability analysis of complex systems of equipment.

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37

Wang, Siqi, Xian Zhao, Zhigang Tian, and Ming J. Zuo. "Optimum component reassignment for balanced systems with multi-state components operating in a shock environment." Reliability Engineering & System Safety 210 (June 2021): 107514. http://dx.doi.org/10.1016/j.ress.2021.107514.

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38

Xu, Jun, Zhenglin Liang, Yan-Fu Li, and Kaibo Wang. "Generalized condition-based maintenance optimization for multi-component systems considering stochastic dependency and imperfect maintenance." Reliability Engineering & System Safety 211 (July 2021): 107592. http://dx.doi.org/10.1016/j.ress.2021.107592.

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Nguyen, Khanh T. P., Kamal Medjaher, and Christian Gogu. "Probabilistic deep learning methodology for uncertainty quantification of remaining useful lifetime of multi-component systems." Reliability Engineering & System Safety 222 (June 2022): 108383. http://dx.doi.org/10.1016/j.ress.2022.108383.

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40

Wang, Yukun, Xiaopeng Li, Junyan Chen, and Yiliu Liu. "A condition-based maintenance policy for multi-component systems subject to stochastic and economic dependencies." Reliability Engineering & System Safety 219 (March 2022): 108174. http://dx.doi.org/10.1016/j.ress.2021.108174.

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41

Dong, Wenjie, Sifeng Liu, Suk Joo Bae, and Yingsai Cao. "Reliability modelling for multi-component systems subject to stochastic deterioration and generalized cumulative shock damages." Reliability Engineering & System Safety 205 (January 2021): 107260. http://dx.doi.org/10.1016/j.ress.2020.107260.

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42

Li, Wei, and Ming J. Zuo. "Optimal design of multi-state weighted k-out-of-n systems based on component design." Reliability Engineering & System Safety 93, no. 11 (November 2008): 1673–81. http://dx.doi.org/10.1016/j.ress.2008.01.009.

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43

He, Rui, Zhigang Tian, Yifei Wang, Mingjian Zuo, and Ziwei Guo. "Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency." Reliability Engineering & System Safety 234 (June 2023): 109167. http://dx.doi.org/10.1016/j.ress.2023.109167.

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44

Okumura, Susumu, Yuuki Matsumoto, Yuji Hatanaka, and Kazunori Ogohara. "Simultaneous Evaluation of Environmental Impact and Incurred Cost on Selection of End-Of-Life Products Recovery Options." International Journal of Automation Technology 10, no. 5 (September 5, 2016): 699–707. http://dx.doi.org/10.20965/ijat.2016.p0699.

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Conventional production and consumption systems, in which industrial products are manufactured, consumed, and then finally disposed, have significant environmental impacts. Reusing and recycling product components in the manufacture of industrial products has recently become popular as an effective way of conserving natural resources. In this study, we propose a method to assign each product component a reasonable end-of-life (EOL) option (reuse, recycling, and disposal) in the product design phase. We develop a method, in which a product tree is generated by a multi-agent system, to determine EOL options considering component combinations based on environmental impact and incurred cost. In addition, we optimize the disassembly level for better reuse and recycling. The proposed determination method of EOL options for components in a product is justified by numerical examples using an inkjet printer.

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45

Xu, Zhuoran, Qianmu Li, Linfang Qian, and Manyi Wang. "Multi-Sensor Fault Diagnosis Based on Time Series in an Intelligent Mechanical System." Sensors 22, no. 24 (December 17, 2022): 9973. http://dx.doi.org/10.3390/s22249973.

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Intelligent mechanical systems are a focused area nowadays. One of the requirements of intelligent mechanical systems is to achieve intelligent fault diagnosis through the real-time acquisition and analysis of data from various sensors installed on mechanical components. In this paper, a new fault diagnosis method is proposed to solve the problems of difficulty in integrating the fault diagnosis algorithm and locating fault parts due to the complexity of modern mechanical systems. The complexity of modern industrial intelligent systems is due to the fact that the systems are composed of multiple components and there are various connections between them. Common fault diagnosis is to design specialized fault identification algorithms for the physical characteristics of each component, and the integration of different algorithms is a major challenge for system performance. Therefore, this paper investigates a general algorithm for the fault diagnosis of complex systems using the timing characteristics of sensors and transfer entropy. The fault diagnosis algorithm is based on the prediction of multi-dimensional long time series using Autoformer, and fault identification is performed based on the deviation of the predicted value from the actual value. After fault identification, a root cause analysis method of faults based on transfer entropy is proposed. The method can locate the component where the fault occurs more accurately based on the analysis of the cause–effect relationship of each component and help maintenance personnel to troubleshoot the fault.

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Falkowski, Michał J., Paweł D. Domański, and Ewa Pawłuszewicz. "Causality in Control Systems Based on Data-Driven Oscillation Identification." Applied Sciences 12, no. 8 (April 8, 2022): 3784. http://dx.doi.org/10.3390/app12083784.

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This paper addresses the subject of causality analysis using simulation data and data collected from a real control system. Simulated data includes Gaussian and Cauchy noise signals. Real-time series include various, mostly unknown distortions, like trends, oscillations, and noises. Presented research focuses on the oscillatory component in data and its propagation in multi-loop control systems. Oscillation identification is based on a deep decomposition process for control error time series. Identified periodic signals are used for further causality processing. The analysis uses the Transfer Entropy approach. This method belongs to the group of model-free methods. The determination of information pathways is conducted without any model or a priori process knowledge. The research investigates the impact of the oscillation time-series component on the Transfer Entropy causality analysis. The summary shows the observations obtained for given simulated datasets and those collected from real processes. The obtained results show that simulated analysis works properly. On the contrary, the direct application of the oscillation decomposition in real industrial cases may be misleading. Large datasets demand modification in the methodology. Different variants are tested. They show that oscillation propagation is biased in real systems and, therefore, the decomposition should be applied with caution. Furthermore, it is important to remember that the algorithm transition from simulated data to real industrial ones is demanding and should be done with the utmost care.

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Gupta, Aparna, and Chaipal Lawsirirat. "Strategically optimum maintenance of monitoring‐enabled multi‐component systems using continuous‐time jump deterioration models." Journal of Quality in Maintenance Engineering 12, no. 3 (July 2006): 306–29. http://dx.doi.org/10.1108/13552510610685138.

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Torrado, Nuria. "Optimal component-type allocation and replacement time policies for parallel systems having multi-types dependent components." Reliability Engineering & System Safety 224 (August 2022): 108502. http://dx.doi.org/10.1016/j.ress.2022.108502.

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Oakley, Jordan L., Kevin J. Wilson, and Pete Philipson. "A condition-based maintenance policy for continuously monitored multi-component systems with economic and stochastic dependence." Reliability Engineering & System Safety 222 (June 2022): 108321. http://dx.doi.org/10.1016/j.ress.2022.108321.

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Zhang, Nailong, and Wujun Si. "Deep reinforcement learning for condition-based maintenance planning of multi-component systems under dependent competing risks." Reliability Engineering & System Safety 203 (November 2020): 107094. http://dx.doi.org/10.1016/j.ress.2020.107094.

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