Search

In the light of Industry 4.0 revolution, the Industrial Internet of Things (IIoT) allows digitalizing manufacturing processes and increasing the digital connectivity and automation in the smart factories and industrial systems. The reliability of a system is considered as a key performance indicator that defines how accurately and perfectly the system works. Ensuring reliability in the IIoT systems presents several challenges due to the presence of uncertain and dynamics in the industry environment as well as the complex nature of industrial systems. While there are no standard frameworks proposed and applied for the IIoT systems with multiple reliability levels, the challenges reveal many promising opportunities for the research and practical implementation including advancing technologies and systematic designs such as algorithms, architectures, and devices. However, there are a few works studying the reliability-related aspects of the industrial systems in the existing literature, they lack a framework to conduct the study and examination of reliability of IIoT systems in a holistic manner. In this regard, this article will elucidate and survey several performance-improvement methods and techniques for better reliability and further challenges to be overcome for highly reliable IIoT systems. Finally, by defining several open research issues, we expect to draw more attention for both the academic researchers and practitioners toward designing and developing reliable solutions and frameworks. [ABSTRACT FROM AUTHOR]

Working vacation queues with breakdowns and customers impatience have many applications in different real-life situations. The development of these models to determine their performance is extremely important. In this paper, we deal with a finite population Markovian multi-server machine system with breakdowns, repairs, Bernoulli feedback, balking, reneging, and retention of reneged customers, under multiple synchronous working vacations. The investigated model has a potential application in the real-world machine systems, such as the automated manufacturing systems with limited space. For the analysis purpose, we employ Q-matrix method in order to obtain the steady-state probabilities as well as closed-form expressions for several system characteristics. Particular cases of the current study are presented. We construct the expected cost function and develop an optimization problem to determine the optimum cost. The direct search method and the Quasi–Newton method are applied to find the optimum system capacity, the minimum number of servers, and the optimum service rates during both working vacation and regular busy periods at minimum cost. Lastly, a sensitivity analysis for the numerical simulation is carried out. [ABSTRACT FROM AUTHOR]

Degradation characteristics of lithium-ion battery pack system (LIBPs) cannot be well described directly by the existing life model of cell, such as the interference imposed by stochastic uncertainty and coupling effect of multiple cells. In this article, we devise a battery capacity estimation and prediction algorithm leveraging deep learning and least squares regression, and develop a stochastic model of capacity degradation obeying a lognormal distribution and a reliability evaluation methodology to reveal the internal evolutionary rules and health status of LIBPs with multi-state logic. We generate a comprehensive dataset consisting of 150 cells and 3 battery packs derived from the cloud platform for cross-validation and take real-world vehicle daily operating charging and discharging data as an input for the proposed framework. The validation results indicate that our best model achieves 0.33 % mean absolute percentage error and 0.44 % root mean squared percent error for state of health estimation and 0.317 mean absolute error in remaining lifetime prediction. Further, remaining capacity probability distribution curves are established with estimated and predicted results, and reliability fade tendencies are meticulously profiled based on the series-parallel structure, which enables accurate electric vehicle service time evaluation under different conditions of uncertainty factors introduced by battery inconsistency and randomness of driving behaviors. This work highlights the promise of deep learning to supplant resource- and time-consuming traditional approaches, and emphasizes the potential of employing reliability theory to rapidly design new-generation battery safety management frameworks. • A multi-channel separated hybrid neural network model aiming for achieving better accuracy and improved generalization. • Using only segments of the daily charging and discharging data with 30s sampling interval are capable of modelling and predicting the evolution of nonlinear battery systems. • Based on SOH estimation and RUL prediction at the cell level and reliability analysis at the pack level, the service time and safety status evaluation throughout the life cycle of electric vehicles is realized. • Premeditating the goal of building an excellent battery system, the impact of the series-parallel structure on the reliability of battery packs is investigated. [ABSTRACT FROM AUTHOR]

This paper presents a system reliability assessment of the structure of ballast-less railway tracks (SBLRTs) in which the major structural elements are considered. First, limit state functions (LSFs) with respect to serviceability and safety, including the maximum vertical deflection of rail, margin of safety of bending moment strength of upper and lower concrete beams, tensile yielding of steel bars reinforcing the upper concrete beam, along with the maximum pressure on subgrade are defined. Then, an adaptive soft Monte Carlo simulation (ASMCS) is established to model uncertainties associated with dynamic loads, characteristics of viscoelastic models, thicknesses of upper and lower concrete beams, and the train speed. Results of reliability analysis confirm the accuracy and computational efficiency of the ASMCS to take into account the uncertainties in the components of the SBLRTs. Sensitivity analysis indicates that the dynamic amplification factor as well as the stiffness of connection layer and subgrade course are the most influential random parameters. Meanwhile, providing a more uniform structure for these layers leads to the significant increase of reliability indices of all failure scenarios. Overall, the system reliability analysis of the components indicates that the tensile yielding of steel bars serves a crucial function in ensuring the safety of SBLRTs. [ABSTRACT FROM AUTHOR]

• Reliability estimation through simulation for multistate flow networks. • MP-based simulation without prior knowledge of boundary for demand. • Investigating reliability degradation through the analysis of time series. • Large-scale case study of Taiwan Academic Network. A multistate flow network is useful for the effective and efficient model construction and reliability evaluation of large and complex systems. To evaluate the reliability of a multistate flow network, boundary points for specified demands are generated in the existing path- and cut-based methodologies. Using the boundary points, the system reliability can be calculated using analytical or simulation methodologies. However, evaluating reliability using boundary points is an NP-hard problem. To improve the time efficiency of reliability estimation, a path-based simulation approach without boundary points is proposed in this study, where the minimal path without any cycles is used. The contributions of this study are threefold: First, the proposed path-based simulation complements the cut-based simulation to improve the applicability of reliability estimation for various network topologies. Second, the proposed simulation algorithm presents linear time complexity, whereas conventional boundary-based analytical methodologies or simulations consume exponential/factorial time. Third, the time attribute is included in the proposed simulation approach to analyze the behavior of reliability degradation over time. The experimental results, including those of a case study, indicate that the proposed path-based simulation is more effective and efficient than existing boundary-based approaches, particularly for large and complex systems. [ABSTRACT FROM AUTHOR]

It is still a challenging task to implement efficient methods for reliability analysis, especially for complex engineering systems that have implicit limit state surfaces and multiple failure modes. In this paper, an adaptive Kriging-assisted system reliability method is proposed to solve system reliability assessment problems. The proposed method uses an adaptive Kriging-based trust region to obtain the design point of each component failure mode based on the first-order reliability method in the first stage. By adding barrier functions to the potential limit state functions (LSFs) around the candidate design point, a new limit state function is obtained and used for the next failure mode identification in the second stage. After identifying all the potential failure modes, the system reliability assessment can be initially approximated by direct integration or binomial bounds theory. The accuracy of approximating failure probability can be enhanced by importance sampling or active learning in the third stage. Two case studies with quantitative landslide risk assessment are used to show the efficiency and accuracy of the proposed method. With the help of the smoothed-particle hydrodynamics method (SPH), the large deformation behaviours which may induce a strong nonlinear limit state surface can be used as the indicators of the failure consequences. The results show that the method proposed in this paper is capable of solving high non-linearity implicit LSFs. It also exhibits potential in handling problems with small failure probabilities. [ABSTRACT FROM AUTHOR]

• Integrated reliability data from subsystems and components to create an optimized RQT plan. • Combined Monte Carlo Simulation with RQT to ensure precise and comprehensive planning. • Objective approach to determining posterior risk despite limited knowledge. • Innovative methods optimized RQT plan, reducing test time while fulfilling risk requirements. During the development of systems, traditional System Reliability Qualification Testing (SRQT) is typically utilized to assess whether they meet predefined reliability standards. However, this approach often demands substantial sample sizes and prolonged test durations, rendering it impractical for costly, highly reliable systems with limited sample sizes. Additionally, the extended test duration may not align with practical time-to-market pressures or budget constraints. To overcome these challenges, the study integrates subsystem data into the monopropellant engine system RQT plan's design. By leveraging Monte-Carlo simulation, subsystem data is modeled to create a system parameter distribution, enabling the formulation of SRQT plans based on posterior risks. An example of a monopropellant liquid rocket engine system is provided to demonstrate the advantages and applications of the proposed methodology. [ABSTRACT FROM AUTHOR]

System reliability analysis aims at computing the probability of failure of an engineering system given a set of uncertain inputs and limit state functions. Active-learning solution schemes have been shown to be a viable tool but as of yet they are not as efficient as in the context of component reliability analysis. This is due to some peculiarities of system problems, such as the presence of multiple failure modes and their uneven contribution to failure, or the dependence on the system configuration (e.g., series or parallel). In this work, we propose a novel active learning strategy designed for solving general system reliability problems. This algorithm combines subset simulation and Kriging/PC-Kriging, and relies on an enrichment scheme tailored to specifically address the weaknesses of this class of methods. More specifically, it relies on three components: (i) a new learning function that does not require the specification of the system configuration, (ii) a density-based clustering technique that allows one to automatically detect the different failure modes, and (iii) sensitivity analysis to estimate the contribution of each limit state to system failure so as to select only the most relevant ones for enrichment. The proposed method is validated on two analytical examples and compared against results gathered in the literature. Finally, a complex engineering problem related to power transmission is solved, thereby showcasing the efficiency of the proposed method in a real-case scenario. • Efficient active learning method for system reliability in arbitrary configuration. • Kriging or PC-Kriging to build surrogates of the limit state functions. • Algorithm combining a system learning function, clustering and sensitivity analysis. • Limit-states calls divided by two compared to the recent state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Offshore wind is planned to become the first source of energy by 2050. That requires installing turbines in deeper seas. It is shown that only floating wind turbines will allow dealing with this challenge while keeping a reasonable cost of energy production and transport according to the levelized cost of electricity. A Floating Offshore Substation will be needed in many sites. This technology is still at a low technology readiness level. This paper aims to analyze the system reliability of such a structure for which the failure rates of structural components such as mooring lines and dynamic power cables are close to the ones of electro-technical systems. Consequently, only a system reliability assessment of the floating offshore substation will allow accurately quantifying its availability and the most sensitive components. Usually, structural reliability aims at quantifying the probability of failures, while electro-technical reliability relies on feedback and observed failure rates. The paper first unifies these two concepts in a single formulation and then evaluates the system’s reliability and availability. This methodology is illustrated in a study case localized in the French coasts of the Mediterranean Sea, where the effect of several mooring and substation designs on the system reliability is compared.

In classical survival analysis, it is assumed that all the individuals will experience the event of interest. However, if there is a proportion of subjects who will never experience the event, then a standard survival approach is not appropriate, and cure models should be considered instead. This paper deals with the problem of adapting a machine learning approach for classical survival analysis to a situation when cure (i.e., not suffering the event) is a possibility. Specifically, a brief review of cure models and recent machine learning methodologies is presented, and an adaptation of machine learning approaches to account for cured individuals is introduced. In order to validate the proposed methods, we present an extensive simulation study in which we compare the performance of the adapted machine learning algorithms with existing cure models. The results show the good behavior of the semiparametric or the nonparametric approaches, depending on the simulated scenario. The practical utility of the methodology is showcased through two real-world dataset illustrations. In the first one, the results show the gain of using the nonparametric mixture cure model approach. In the second example, the results show the poor performance of some machine learning methods for small sample sizes.

The maintenance priority indexes-based (MPIs-based) selective maintenance policy (SMP) for a complex system with degradation components is studied in this work. The SMP is performed in a scheduled break when the system just completed the current mission. During the break, the MPIs, including maintenance importance, structure importance, and reliability importance, are comprehensively considered to support the system for executing the next mission. The objective of the MPIs-based SMP is to find the optimal maintenance decision for the system subject to maintenance cost and time constraints. Besides, the maintenance quality related to component degradation, as well as the economic dependence, are also considered as the maintenance information to be integrated when performing a maintenance decision. The complicated optimization problem is solved by using a simulated annealing algorithm (SAA). The simulation example of an aero-engine control system is presented to depict the maintenance decision process and illustrate the advantage of MPIs-based SMP.

The shear strength reduction method provides an effective tool of numerical analysis for landslides reliability analysis. However, it ignores the failure probability of the secondary failure surfaces and requires huge computational cost. To avoid these common criticisms, an intelligent multiple response surfaces method for system reliability using multiple response-surface method (MRSM) and least-squares support vector machine (LSSVM) is presented to evaluate the stability of complex multistage historic landslides with multiple sliding surfaces. Deterministic analysis of each sliding surface is first performed using the finite element method of sliding surface stress analysis, which is applied to obtain the safety factors of different sliding surfaces from the stress fields generated by finite element simulations. The LSSVM model with excellent nonlinear fitting ability is then employed to construct the multiple response-surface method (MRSM) of the sliding surfaces and a genetic algorithm (GA) is adopted to optimize the LSSVM. This proposed methodology is finally applied to investigate the probability of system failure of the Zhenggang landslide in southwestern China. The results indicate that the proposed approach can reduce the computational cost of finite element analysis in direct Monte Carlo simulation (MCS) by proper training using a limited of samples, and the calculation accuracy meets the engineering requirements of complex multistage historic landslides.

In risk assessment of spatially distributed infrastructure, the probability of demand exceeding capacity is evaluated across the system. We describe and compare two levee system reliability analysis frameworks for seismic and high-water demands. The first approach considers spatial correlations and distributions of demand and capacity between “segments” (i.e., elemental levee lengths) through Monte Carlo simulation. We apply a capacity correlation model derived from seismic case histories in Japan. The seismic demand correlation model is based on global ground motion data, whereas the high-water correlation is taken as unity. The second approach examines the distribution and correlation of capacities and demands between physics-based “reaches” (i.e., length of levee having uniform statistical distributions of capacity and demand). Statistics and spatial correlation of the limit state function are computed using a first-order reliability method procedure. The probability of failure of the reach is then computed using level-crossing statistics. For implementation of level-crossing statistics, we replace Markov-type correlation functions for levee capacity with a Gaussian function. We illustrate both methods for a levee system subjected to realistic demand and capacity distributions and show that characteristic lengths (defined as lengths of levee that can be considered as statistically independent) are comparable for high-water and seismic demands. This outcome is specific to the considered failure mechanisms and is driven by use of similar capacity correlation models, whereas differences in demand correlation models have limited impact.

Electricity propels economic development through improvement in the quality of life. Even though government's strategy to increase power generation and supply exists, Uganda experiences regular power outages and has one of the lowest electrification rates in the world. This study employs a systematic review approach and extends securitisation theory to the electricity sector to characterise electricity security and assess its barriers in Uganda. In this context, five dimensions of electricity security are identified and compared. Based on this set of dimensions, results show that Uganda is electricity “insecure.” Further, six (6) barriers to electricity security in Uganda are identified, and each of them is assessed as either existential, potential, or both. Thus, interventions that alleviate or mitigate these barriers can improve Uganda's electricity security. A quantitative empirical analysis using longitudinal data could offer superior evidence and conclusion on electricity security in Uganda.

In this paper, a strategic level of a supply chain is studied. To deal with its objective, restructuring distribution and manufacturing centers under the parameter uncertainty and system disruption are formulated as a mixed-integer nonlinear programming (MINLP) model. Because of the complexity of such a hard model, it is converted to a mixed-integer linear programming (MILP) model by conducting a simple linearization method. Capacity expansion and reduction, capacity consolidation, outsourcing, and transshipment strategies are used to reconfigure the considered supply chain. An inexact interval fixed-mix fuzzy approach is applied to deal with the parameter uncertainty, and an efficient two-stage model is conducted to analyze the system reliability. The developed model is solved by an efficient and effective Lagrangian relaxation procedure. Furthermore, a case study of dairy products is studied to present the model application and validation. Finally, several sensitivity analyses are carried out to identify the model behavior and solution algorithm validation. [ABSTRACT FROM AUTHOR]

Dynamic fault trees (DFTs) are widely adopted in industry to assess the dependability of safety-critical equipment. Since many systems are too large to be studied numerically, DFTs dependability is often analysed using Monte Carlo simulation. A bottleneck here is that many simulation samples are required in the case of rare events, e.g. in highly reliable systems where components seldom fail. Rare event simulation (RES) provides techniques to reduce the number of samples in the case of rare events. In this article, we present a RES technique based on importance splitting to study failures in highly reliable DFTs, more precisely, on a variant of repairable fault trees (RFT). Whereas RES usually requires meta-information from an expert, our method is fully automatic. For this, we propose two different methods to derive the so-called importance function. On the one hand, we propose to cleverly exploit the RFT structure to compositionally construct such function. On the other hand, we explore different importance functions derived in different ways from the minimal cut sets of the tree, i.e., the minimal units that determine its failure. We handle RFTs with Markovian and non-Markovian failure and repair distributions—for which no numerical methods exist—and implement the techniques on a toolchain that includes the RES engine FIG, for which we also present improvements. We finally show the efficiency of our approach in several case studies. [ABSTRACT FROM AUTHOR]

Several applications of darkroom is observed in modern technological applications. A darkroom can be made completely dark to allow the processing of the light-sensitive photographic materials, including film and photographic paper. Various equipments are used in the darkroom, including an enlarger, baths containing chemicals and running water. It is used to process photographic film, to make prints and to carry out other associated tasks. Also, in non-destructive testing such as magnetic particle inspection a darkroom is essential. Here, we studied the reliability of the illumination of a darkroom under different scenarios of the lighting systems. We have developed three models for illuminating the darkroom with a single operating switch with two tube lights, a single operating switch with multiple tube lights and multiple switches operating individual tube light. We have designed the simple unconstrained model and also the constrained redundancy allocation models. Moreover, we have considered the uncertainty models in the form of intuitionistic fuzzy number as it is the more generalization of the fuzzy numbers. In these imprecise models, the parameters along with the component reliabilities are taken as triangular intuitionistic fuzzy numbers. Before solving these imprecise models, we have crispified these into deterministic form by using suitable and efficient crispification method (BADD). The constrained redundancy allocation problems of integer programming type are obtained in each model and we have implemented evolutionary algorithm WQPSO to solve these problems successfully. Result analysis with comparison has been presented with the recommendation that the third model is more reliable in illuminating the darkroom. [ABSTRACT FROM AUTHOR]

Copyright of Fly Ash Comprehensive Utilization is the property of Hebei Fly Ash Comprehensive Utilization Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The analysis of the water distribution network is complicated and requires several assumptions to simplify its problem definition. Demand Driven Analysis (DDA) is typically used to analyse the network assuming that all network nodes can deliver the required demand regardless of the available pressure. In the case of analysing an existing network under deficit condition such as pipe breakage or extra demand required for firefighting, assumptions used to simulate the network with DDA is not valid. Node Head Flow Relationship (NHFR) should be considered through Pressure Driven Analysis (PDA) to analyse the network. Most PDA methods assume that the networks are airtight which means that if the pressure at any demand node is negative, delivered demand will be equal to zero and the flow is permitted in the connected pipes (Siphonic flow). This assumption is hydraulically incorrect since the air is allowed to get into the connected pipes and prevent their flow leading to node isolation. In this paper, a new Pressure Driven Analysis to Prevent Siphonic Flow (PDA-SF) approach is proposed to analyze the network under deficit conditions and consider isolating the nodes that show available head less than node elevation. The PDA-SF was tested and compared to previous methods in four case studies under steady state analysis or extended period simulation. The case studies cover also different network conditions whether node isolation is needed or not. The PDA-SF was able to solve different networks where other methods failed to achieve the required demand or service pressure. The new PDA-SF method shall enable peers and modelers to better simulate and analysis water distribution networks. [ABSTRACT FROM AUTHOR]

Driven by the ever-growing diversity of software and hardware agents available on the market, Internet-of-Things (IoT) systems, functioning as heterogeneous multi-agent systems (MASs), are increasingly required to provide a level of reliability and fault tolerance. In this paper, we develop an approach to generalized quantifiable modeling of fault-tolerant and reliable MAS. We propose a novel software architectural model, the Intelligence Transfer Model (ITM), by which intelligence can be transferred between agents in a heterogeneous MAS. In the ITM, we propose a novel mechanism, the latent acceptable state, which enables it to achieve improved levels of fault tolerance and reliability in task-based redundancy systems, as used in the ITM, in comparison with existing agent-based redundancy approaches. We demonstrate these improvements through experimental testing of the ITM using an open-source candidate implementation of the model, developed in Python, and through an open-source simulator that tested the behavior of ITM-based MASs at scale. The results of these experiments demonstrated improvements in fault tolerance and reliability across all MAS configurations we tested. Fault tolerance was observed to improve by a factor of between 1.27 and 6.34 in comparison with the control group, depending on the ITM configuration tested. Similarly, reliability was observed to improve by a factor of between 1.00 and 4.73. Our proposed model has broad applicability to various IoT applications and generally in MASs that have fault tolerance or reliability requirements, such as in cloud computing and autonomous vehicles. [ABSTRACT FROM AUTHOR]

To enhance the computational efficiency in slope reliability analyses, a binary classification method (BCM) that takes advantage of a judgement-based strength reduction method (SRM) and an active-learning support vector machine (SVM) was developed to conduct system reliability analyses of layered soil slopes. The SVM was naturally employed to establish a binary classifier via the judgment technique to approximate the true limit state function because the stability state (e.g. stable or unstable) of a slope can be determined without calculating its exact factor of safety according to the SRM. An active-learning technique was developed to iteratively search training samples in the vicinity of the border between safe and failure domains, to update the SVM classifier with the aid of a modified initial sampling rule. Then, Latin hypercube sampling was employed, together with the obtained SVM classifier, to compute the slope system probability of failure. Three representative examples taken from the literature were employed to evaluate the performance of the proposed method. The proposed BCM shows great computational efficiency compared with existing methods. It reduced the computational cost to several minutes for simple slopes and to approximately 30 minutes for a complex real case, while maintaining a good computational accuracy. [ABSTRACT FROM AUTHOR]

Various random factors in the bridge construction process directly affect the safety of the bridge life cycle. The existing theories on the reliability of bridge structure mainly focus on the reliability of components and the reliability of the bridge structure system in the completion and operation stages, while the research on the reliability of the structure system in the construction stage is relatively lacking. Therefore, this paper proposed using the Copula function to calculate the reliability index of the bridge structure construction process system. The basic theory of the Copula function was introduced in detail, and the formula was improved according to the actual situation of bridge construction. Finally, the sensitivity analysis of bridge system reliability was carried out. The research results showed that the method proposed in this paper based on Copula theory to calculate the reliability index of the bridge structure construction process system has strong applicability, simple calculation, and can be used in conjunction with the "interval estimation method", which is suitable for large and complex bridge structural engineering. At the same time, the conclusion that the influence of failure mode correlation on structural reliability should not be ignored in the actual engineering construction process is confirmed. [ABSTRACT FROM AUTHOR]

Conventional interview surveys for assessing customer satisfaction require supplementary resources and yield a low response rate. Covering fewer customers who generally face service interruptions is another limitation that hinders the identification of the root causes of failure. The survey findings might mislead the utilities by showing unrealistic satisfaction levels. Customer dissatisfaction risk subjects to the time between the complaint registration and the utility response pending full resolution of the issue. In the present research, bowtie analysis (BTA) reveals an innovative synergetic correlation between system reliability and customer satisfaction with water utilities. Using complaints reported due to service failures, the fault tree analysis (FTA) assesses the reliability of system components (pressure, water quality, and structure). On the opposite side of the bowtie, event tree analysis (ETA) evaluates the efficiency of the utility response and translates it into customer dissatisfaction risk. Exploring over 1500 registered work orders in a utility operating in British Columbia (Canada) reveals that 80% of complaints originated from structural (primarily service connections) issues. Simulating the BTA for five improvements showed that economical proactive maintenance and an efficient complaint response mechanism improve the system reliability from 58 to 86% and reduce the cumulative risk by 62%. The remaining risk reduction requires expensive improvements, e.g., source change and conventional treatment installation, which require customer approval based on willingness to pay. The study reveals that a complete bowtie analysis effectively integrates both the FTA and ETA and provides a novel customer-driven decision-support tool for water supply systems and other elsewhere applicable. [ABSTRACT FROM AUTHOR]

The last few decades have witnessed advancements in intelligent metaheuristic approaches and system reliability optimization. The huge progress in metaheuristic approaches can be viewed as the main motivator behind further refinement in the system reliability optimization process. Researchers have intensively studied system reliability optimization problems (SROPs) to obtain the optimal system design with several constraints in order to optimize the overall system reliability. This article proposes a modified wild horse optimizer (MWHO) for SROPs and investigates the reliability allocation of two complex SROPs, namely, complex bridge system (CBS) and life support system in space capsule (LSSSC), with the help of the same process. The effectiveness of this framework based on MWHO is demonstrated by comparing the results obtained with the results available in the literature. The proposed MWHO algorithm shows better efficiency, as it provides superior solutions to SROPs.

The geometric programming problem (GPP) is a beneficial mathematical programming problem for modeling and optimizing nonlinear optimization problems in various engineering fields. The structural configuration of the GPP is quite dynamic and flexible in modeling and fitting the reliability optimization problems efficiently. The work's motivation is to introduce a bounded solution approach for the GPP while considering the variation among the right-hand-side parameters. The bounded solution method uses the two-level mathematical programming problems and obtains the solution of the objective function in a specified interval. The benefit of the bounded solution approach can be realized in that there is no need for sensitivity analyses of the results output. The demonstration of the proposed approach is shown by applying it to the system reliability optimization problem. The specific interval is determined for the objective values and found to be lying in the optimal range. Based on the findings, the concluding remarks are presented. [ABSTRACT FROM AUTHOR]

Planning of biorefinery operations is complicated by the stochastic nature of physical and chemical characteristics of biomass feedstock, such as, moisture level and carbohydrate content. Biomass characteristics affect the performance of the equipment which feed the reactor and the efficiency of the conversion process in a biorefinery. We propose a stochastic optimization model to identify a blend of feedstocks, inventory levels, and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor while meeting the requirements of the biochemical conversion process. We propose a sample average approximation (SAA) of the model, and develop an efficient algorithm to solve the SAA model. A feedstock preprocessing process consists of two-stage grinding and pelleting is used to develop a case study. Extensive numerical analysis are conducted which lead to a number of observations. Our main observation is that sequencing bales based on moisture level and carbohydrate content leads to robust solutions that improve processing time and processing rate of the reactor. We provide a number of managerial insights that facilitate the implementation of the model proposed. Note to Practitioners—This paper is motivated by the challenges faced in the bioenergy industry. The focus of this paper is on plants which use the biochemical conversion process to generate liquid fuels. It has been observed that variations in biomass characteristics, such as moisture content, cause variations in feeding of the system which lead to under-utilization of equipment. A requirement of biochemical conversion process is to maintain the carbohydrate content of biomass processed by the reactor, larger than a threshold. We propose a model that identifies the inventory levels and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor. The goal is to improve equipment utilization while satisfying the requirements of the conversion process. The model is tested using real-life data. We found out that by sequencing bales based on moisture level and carbohydrate content, a plant can reduce variability in the system leading to improved system reliability, higher processing rates of the reactor, and higher throughput. [ABSTRACT FROM AUTHOR]

This article presents a novel system reliability-based framework for multi-objective optimisation of preventive maintenance (PM) management of in-service asphalt pavement. To accurately predict the international roughness index (IRI) sequence of pavement sections, a long short-term memory (LSTM) neural network that considers the spatiotemporal correlations between IRI sequences is trained with data retrieved from the long-term pavement performance (LTPP) program. Based on time-dependent limit-state functions (LSFs) incorporating the uncertainty associated with LSTM neural network prediction and the observational error involved in IRI measurement, Monte Carlo simulation (MCS) with importance sampling (IS) is adopted to calculate the reliability of pavement sections. Pavement sections located in New Mexico and Montana are selected as illustrative examples. Tri-objective optimisation processes are investigated by maximising user benefits (i.e. improved system reliability) and agency benefits (i.e. extended service life) while minimising the associated life-cycle cost (LCC) (i.e. user and agency costs) with multi-objective genetic algorithms (GAs). The obtained Pareto solution sets may assist decision-makers in the selection of well-balanced solutions to identify the optimal timing for applying PM treatments to in-service asphalt pavement. [ABSTRACT FROM AUTHOR]

System with self-healing mechanism has been successfully applied in many practical engineering fields. When we discuss the reliability performance of system subject to shocks, cumulative shocks have the greatest impact on most systems fields such as high-speed railway systems or civil structural components. In the present paper, we deal with two different types of cumulative shock models in discrete time with self-healing effect. We study a system with self-healing mechanism from a reliability point of view under these two shock models. The self-healing system will fail when the cumulative damage effect exceeds the given threshold. In the first model, there are shock events at instant of time i (i = 1 , 2 , ...) , while in the second model at some time point there was no shock occurring event. Along with the work in this article, the system reliability formulas and the means and variances of their lifetimes are given under two different types shock models proposed, then simulation methods are adopted to analyze the reliability of the system for the second cumulative model. Finally, numerical examples and future researches are discussed. [ABSTRACT FROM AUTHOR]