Subsystems In Series Research Articles

Parameter estimation of Wiener-Hammerstein system based on multi-population self-adaptive differential evolution algorithm

PurposeThe Wiener-Hammerstein nonlinear system is made up of two dynamic linear subsystems in series with a static nonlinear subsystem, and it is widely used in electrical, mechanical, aerospace and other fields. This paper considers the parameter estimation of the Wiener-Hammerstein output error moving average (OEMA) system.Design/methodology/approachThe idea of multi-population and parameter self-adaptive identification is introduced, and a multi-population self-adaptive differential evolution (MPSADE) algorithm is proposed. In order to confirm the feasibility of the above method, the differential evolution (DE), the self-adaptive differential evolution (SADE), the MPSADE and the gradient iterative (GI) algorithms are derived to identify the Wiener-Hammerstein OEMA system, respectively.FindingsFrom the simulation results, the authors find that the estimation errors under the four algorithms stabilize after 120, 30, 20 and 300 iterations, respectively, and the estimation errors of the four algorithms converge to 5.0%, 3.6%, 2.7% and 7.3%, which show that all four algorithms can identify the Wiener-Hammerstein OEMA system.Originality/valueCompared with DE, SADE and GI algorithm, the MPSADE algorithm not only has higher parameter estimation accuracy but also has a faster convergence speed. Finally, the input–output relationship of laser welding system is described and identified by the MPSADE algorithm. The simulation results show that the MPSADE algorithm can effectively identify parameters of the laser welding system.

Reliability optimization for series and parallel systems with series subsystem and comprising of dependent components under random shock environment

Reliability optimization for series and parallel systems with series subsystem and comprising of dependent components under random shock environment

Abstract 5932: Novel screening technology for early detection of breast cancer using liquid biopsy

Abstract Breast cancer screening is the first line-of-defense in detecting this disease early, and consequently changing its outcomes, dramatically. We introduce Crystalogram as a novel, radiation-free, blood-based, breast cancer screening tool, for asymptomatic adult women of all ages, regardless of their risk level. While Crystalogram could be used in tandem with Mammogram, it has unique characteristics that could make it effective as an independent tool, particularly for those who currently have no standard-of-care recommendation for breast screening, i.e., asymptomatic, average risk women younger than 40 or 45 in the U.S. or Europe, respectively. In the realm of cancer screening, positive predictive value (PPV) plays a crucial role. A low or moderate PPV (as in Mammogram) could result in a large number of false positive patients, who would subsequently have to undergo unnecessary tests, some of which are invasive, such as tissue biopsy. By targeting a remarkably high PPV, Crystalogram is aimed to (while offering an attractive sensitivity) reduce the number of false positive patients, and hence offer a true screening platform. Most methods for cancer screening are based on either imaging or (if molecular) cell-free tumor DNA (ctDNA). Crystalogram uses circulating tumor cells (CTCs) in conjunction with whole genome sequencing (WGS). Molecular analyses using CTCs pose many technical challenges; however (unlike ctDNA) they provide a complete view of a cell, and hence the related cellular perturbations that are indicative of breast cancer. In ctDNA-based cancer screening methods, the source of nucleic acid for each sequenced fragment is lost. In a CTC-based system, however, the source of all sequenced fragments (i.e., cells they originate from) is known. Additionally, since the nucleic acid for each cell is independently amplified, the number of available fragments per cell is significantly higher as compared to ctDNA originating from comparable cells. Through a CTC-based and molecular technology, Crystalogram can deliver a superior PPV for breast cancer screening, and at a reasonable sensitivity. This is particularly true for breast cancer subtypes that have more dire outcomes and yet evade current screening modalities (e.g., triple negative breast cancer). The crux of Crystalogram’s high specificity is its two-tiered architecture, where a sample may be called negative at either of the two subsystems (in series), each of which is highly specific: 1) Sample Qualification System (SQS), which operates on a relationship built on the proprietary antibody staining features of the cells along with their physical properties; 2) Whole Genome Sequencing (WGS), where the sample can be marked as negative if it does not show the expected proprietary signatures for breast cancer. Since SQS and WGS systems are in series, a modest specificity of 99% for each subsystem would result in a specificity of 99.99% for the complete Crystalogram system. Citation Format: Bahram G. Kermani. Novel screening technology for early detection of breast cancer using liquid biopsy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5932.

A four-unit repairable system with three subsystems in series and one subsystem containing n components in parallel under environmental failure

A four-unit repairable system with three subsystems in series and one subsystem containing n components in parallel under environmental failure

Availability analysis considering two subsystem failures simultaneously via markov modeling

This paper discusses the performance analysis of the alloy wheel plant (AWP). Till now researchers (1, 5, 9, 14, and 25) took assumption that only one failure at one time but this paper considered two failures at one time. The AWP mainly consists of four subsystems in series (Gdc Machine, Cutting Machine, Heat Treatment Machine and Shot Blasting Machine). The failure and repair rates are implicit to be constant and statistically independent. A transition diagram developed form the interrelationship among the working and failed state. Mathematical modeling of this system is done by applying markov- birth – death processes. These equations are solved by recursive method and optimized reliability is calculated for failure and repair rates. The obtaining result is helpful to improve the overall performance of alloy wheel plant.

Adaptive region tracking control for underwater vehicles with large initial deviation and general uncertainty

A region tracking control system is developed for underwater vehicles with large initial deviation and general uncertainty. The developed system is a nonlinear cascaded system, consisting of two subsystems in series. The fixed-gain controller of the first subsystem is designed to compensate for the region tracking error caused by the large initial deviation. In the second subsystem, the radial basis function neural network is adopted to approximate the general uncertainty along with the external disturbance and modelling uncertainty. According to the Lyapunov theory, the control law of the two subsystems and adaptive law of the second subsystem are derived to ensure the region tracking errors asymptotically converge to zero. The validity of the control system is verified by a series of comparisons on simulation results.

Synthesize dual goals: A study on China’s ecological poverty alleviation system

How to break the vicious cycle of poverty and ecological degradation is widely concerned and discussed. In the poverty alleviation practices in China, ecological poverty alleviation (EPA) is regarded as an important way to synthesize the dual goals of poverty reduction and environmental protection and to achieve the win-win outcomes. Many Chinese researchers have fruitful research achievements on EPA yet they do not recognize that EPA is not a simple combination of various policies, but a complex system involving multiple policy instruments, governmental agencies, social forces, and agents. However, few studies abroad illustrate EPA in detail. They focus more on specific components of EPA such as payment for ecosystem services while overlooking the integral concept of EPA and practices from China. Based on field research in Guizhou Province, China, this paper proposes a framework of EPA with an effective multi-agent and co-government system, for demonstrating the concept and practice of EPA. With case analysis, we illustrate key elements in this system and their relationships, and how they play a vital role in pursuing win-win outcomes in environmental protection and poverty alleviation. The three dimensions of this EPA system have been discussed. The first is to strengthen the interaction among the agents. Many efforts should be made for establishing an efficient communication routine and a stable relationship among their interests. The second is to reinforce the connection between diverse elements in each policy subsystem, such as the systematization and coordination of the ecological industry development, the systematization of the links before, during, and after the relocation of ecological immigrants, etc. The third is to promote the interactions between the three subsystems, so that ecological enhancement, ecological compensation, and industrial development, and migration and relocation can promote each other, and ultimately promote the coordination of poverty alleviation and ecological protection in poverty-stricken areas. Thus, this paper analyzes how to establish the communication routine among the relevant agents of EPA, the interaction among the internal elements of the subsystems, and the relationship between the subsystems in series, trying to reveal the basic operating mechanism of the system.

Analysis of a Markov chain system model of a power generating system composed of generating units and transformer units

The paper analyzes a power generating system model consisting of two sub-systems in series. Sub-system-1 consists of four generating units in parallel and sub-system -2 consists of two transformer units in parallel. Three generating units are sufficient to supply the required power and the fourth is added as a redundant unit to enhance the reliability of the system. Similarly, one transformer unit is sufficient to run the system satisfactorily and the second is added to increase the system reliability further. The expressions for several system characteristics useful to system manager, engineers, training supervisors have been obtained using supplementary variable technique.

Mission success probability optimization for phased-mission systems with repairable component modules

Mission success probability (MSP) is an important measurement to evaluate the performance of phased-mission systems (PMSs). The PMSs consist of some subsystems in series, and each subsystem is made of some k-out-of-n structures with repairable component modules. Adjusting the reliability and maintainability of components can improve the MSP of repairable PMSs. Therefore, the objective of MSP optimization for PMSs with repairable component modules is to maximize the MSP considering the cost constraints on the adjustment process. The improved optimization algorithm combines the importance measure-based local search method with the ant colony optimization (ACO) algorithm, which is called the importance measure-based ACO (IMACO) algorithm, for solving the MSP optimization. Three numerical experiments are implemented to illustrate the parameter selection and the performance of IMACO algorithm. Comparing with ACO algorithm, the results show that the IMACO algorithm is more effective when the number of component modules is small, while the running time becomes shorter with the increase of the component modules. According to the numerical example of heavy-lift systems with multi-rotor unmanned aerial vehicles (UAVs), the improvement priority of components is analyzed, and the reliability variables of the indispensable components with more missions should be improved at first.

A numerical method for system residual life prediction based on Simpson formula

Residual life prediction for complex system is attracting increasing attentions in practice. However, it is usually difficult to obtain analytical solution due to system complexity. In this paper, based on Simpson formula, a numerical method is proposed to simplify the computation, by which the point estimate of system’s residual life can be derived according to the structure characteristics of the system. Four typical reliability subsystems, including series, parallel, voting and cold standby subsystems, are studied, and the analytic relationships between the residual life of each subsystem and the reliability functions of components is discussed respectively. Then, the integral procedure of the Simpson formula is used to obtain the point estimations of residual life of the subsystems as well as the entire system. Finally, an illustrative example is given and the proposed method is verified by Monte Carlo simulation.

Approximate Reliability Evaluation of Large-Scale Multistate Series-Parallel Systems

Multistate series-parallel system (MSSPS) is a widely used model for representing engineering systems, whose reliability has been extensively analyzed. Universal generating function (UGF) is an efficient method for evaluating the reliability of MSSPS. However, when facing the large-scale MSSPS, where the number of system components and possible states are enormous, calclating the exact system reliability can be rather time-consuming. To evaluate the reliability of large-scale MSSPS more efficiently, this paper proposes an approximation method, named continuization discretization approximation (CDA) method. The CDA approach consists of continuization and discretization processes. The continuization process applies Gaussian approximation method based on the central limit theory and the UGF technique to evaluate parallel subsystems. While the discretization process discretizes the continuous distribution to a discrete one, and proposes an algorithm to evaluate the series subsystems efficiently. The efficiency and accuracy performance of the CDA method can be adjusted by parameters according to the computational resource and the system scale. The newly proposed method is compared to the existing methods in evaluating the large-scale MSSPS. Numerical examples show that the CDA method has evident advantage in computational efficiency with satisfactory accuracy performance.

Multi-objective optimization in redundant system considering load sharing

Reliability allocation problem (RAP) deals with the dilemma between reliability (or availability) increase and some undesirable consequences, as an increase in cost. Therefore, new installations or maintenance investments should be optimally allocated to maximize the reliability (or availability), considering all restrictions. This paper proposes a solution for the RAP problem, taking into account failure dependency among redundant components. Because of that, a stochastic approach, based on Markov Chain, is applied. The multi-objective problem is solved with NSGA-II (Non-Dominated Sorting Genetic Algorithm II), assuming availability and overall costs as objective functions. Two examples are tested with the proposed approach. The first case is a hypothetical system with 5 subsystems in series. The second example is a real industrial application. In both cases, a Pareto front is obtained, allowing an analysis of viable solutions to be adopted. Additionally, the failure dependency effect is also present in the optimization results. Therefore, it is possible to investigate the influence on the system availability and final cost.

A note on a double‐serial switch‐spring‐parallel semi‐active connection element

AbstractSubject is a semi‐active connection element consisting of two identical subsystems in series. Each subsystem is composed of one spring and one switch in parallel. The switch can prevent spring movement when closed. Thus, the potential energy in the spring is stored. The use of two subsystems in series allows to direct power from extension and compression into different springs, yielding two spring forces with different directions. Due to the mass inertia, fast switching between the two spring forces controls the position of a connected mass. Thus, the semi‐active connection element behaves similar to an actuator with the limitation that the average work vanishes. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Multi-objective design optimisation of repairable k-out-of-n subsystems in series with redundant dependency

Several researches have been investigated on Multi-Objective Redundancy Allocation Problems (MORAPs), but none of them have considered the redundant dependency at the design stage. This latter which is a special kind of failure dependency can affect significantly the system performance. Due to this fact, this paper deals with the multi-objective system design optimisation with dependent components by focusing on two objectives: maximisation of system availability and minimisation of system cost with components choice and weight constraints. A system consisting of many k-out-of-n repairable subsystems connected in series is considered. The components of a subsystem are supposed to be identical and may be dependent. They are selected from a set of available component types. In addition to the redundancy level and the number of repair teams allocated to each subsystem, the choice of components type and the dependency level are also considered as decision variables. Since the described problem is NP hard, we propose three multi-objective meta-heuristic algorithms based on Non-dominated Sorting Genetic Algorithm (NSGA II) and Strength Pareto Evolutionary Algorithm (SPEA II) with different constraints handling. An exact method is also applied. To analyse their performances, numerical applications are provided and comparisons based on different well-known metrics are presented.

An improved particle swarm optimization model for solving homogeneous discounted series-parallel redundancy allocation problems

In this study, we propose an improved particle swarm optimization algorithm (IPSOA) to solve discounted redundancy allocation problems (DRAPs) in series-parallel systems. The system consists of subsystems in series with parallel components in each subsystem. Homogeneous redundant components are used to achieve a desirable system reliability. The components of each subsystem are characterized by their cost, weight, and reliability where the cost is calculated under an all unit discount policy. The goal is to find the optimum combination of the components for each subsystem so that the system reliability is maximized. After formulating the mathematical model, the proposed IPOSA is implemented to achieve the solution. Moreover, an experimental design approach is used to calibrate the algorithm’s parameters. Three numerical problems, each of which considered under several configurations, are discussed to demonstrate the applicability of the proposed procedures. In order to evaluate the accuracy and performance of our IPSOA, all the problems are also solved using two other meta-heuristics, namely, the homogeneous particle swarm optimization algorithm and tabu search. The numerical results show that, when solving DRAPs, IPSOA behaves better than the other two algorithms considered from both a solution quality and a computational viewpoint.

A multi-objective multi-state series-parallel redundancy allocation model using tuned meta-heuristic algorithms

ABSTRACTIn this paper, we study a series-parallel multi-objective multi-state redundancy allocation problem (MSRAP) with known performance levels and corresponding state probabilities. The problem is comprised of multiple subsystems in series and each subsystem is comprised of multiple components in parallel. The system components have a range of performance level from complete working to complete failure. The subsystems contain homogenous redundant components and the component prices come under an all-unit discount policy if a unique brand (type) is chosen for purchasing all subsystem components. Each component is characterised by its cost, weight and availability. The goals are to find the optimal combination of the components in each subsystem that maximises system availability and minimises the total cost under a weight constraint. We propose a multi-objective harmony search (MOHS) algorithm, a non-dominated sorting genetic algorithm (NSGA-II), and a multi-objective genetic algorithm (MOGA) to solve this problem. In addition, the Taguchi method is utilised to tune the parameters in each algorithm. We use a number of numerical examples to demonstrate the applicability and exhibit the efficacy of the three algorithms. The results show that the MOHS outperforms the NSGA-II and MOGA with respect to all of the considered metrics.

Joint reliability based design and periodic preventive maintenance policy for systems sold with warranty

Purpose – The authors consider a system which is a part of a complex equipment (e.g. aircraft, automobile, medical equipment, production machine, etc.), and which consists of N independent series subsystems. The purpose of this paper is to determine simultaneously the system design (reliability) and its preventive maintenance (PM) replacements periodicity which minimize the total average cost per time unit over the equipment useful life, taking into account a minimum required reliability level between consecutive replacements. Design/methodology/approach – The problem is tackled in the context of reliability-based design (RBD) considering at the same time the burn-in of components, the warranty commitment and the maintenance strategy to be adopted. A mathematical model is developed to express the total average cost per time unit to be minimized under a reliability constraint. The total average cost includes the cost of acquiring and assembling components, the burn-in of each component, preventive and corrective replacements performed during the warranty and post-warranty periods. A numerical procedure is proposed to solve the problem. Findings – For any given set of input data including components reliability, their cost and the costs of their preventive and corrective replacements, the system design (reliability) and the periodicity of preventive replacement during the post-warranty period is obtained such as the system’s total average cost per time unit is minimized. The obtained results clearly indicate that a decrease in the number of PM actions to be performed during the post-warranty period increases the number of components to be added at each subsystem at the design stage. Research limitations/implications – Given that the objective function (cost rate function) to be minimized is non-linear and involves several integer variables, it has not been possible to derive the optimal solution. A numerical procedure based on a heuristic approach has been proposed to solve the problem finding a nearly optimal solution for a given set of input data. Practical implications – This paper offers to manufacturers a comprehensive approach to look for the most economical combination of the reliability level to be given to their products at the design stage, on one hand, and the PM policy to be adopted, on the other hand, given the offered warranty and service for the products and reliability requirements during the life cycle. Originality/value – While the RBD problem has been largely treated, most of the published works have focussed on the development or the improvement of solving techniques used to find the optimal configuration. In this paper the authors provide a more comprehensive approach that considers simultaneously RBD, the burn-in and warranty periods, along with the maintenance policy to be adopted. The authors also consider the context of products whose component failures cannot be rectified through repair actions. They can only be fixed by replacement.

Bayesian survival analysis in reliability for complex system with a cure fraction

In traditional methods for reliability analysis, one complex system is often considered as being composed by some subsystems in series. Usually, the failure of any subsystem would be supposed to lead to the failure of the entire system. However, some subsystems' lifetimes are long enough and even never fail during the life cycle of the entire system. Moreover, such subsystems' lifetimes will not be influenced equally under different circumstances. In practice, such interferences will affect the model's accuracy, but it is seldom considered in traditional analysis. To address these shortcomings, this paper presents a new approach to do reliability analysis for complex systems. Here a certain of the subsystems is defined as a cure fraction under the consideration that such subsystems' lifetimes are long enough and even never fail during the life cycle of the entire system. By introducing environmental covariates and the joint power prior, the proposed model is developed within the Bayesian survival analysis framework, and thus the problem for censored (or truncated) data in reliability tests can be resolved. In addition, a Markov chain Monte Carlo computational scheme is implemented and a numeric example is discussed to demonstrate the proposed model.

Genetic algorithm for solving bi-objective redundancy allocation problem with k-out-of-n subsystems

Reliability optimization problem is an important type of optimization problems that has many practical applications in the real-world systems such as manufacturing systems, telecommunication systems, transformation systems and electrical systems. This research focuses on redundancy allocation problem (RAP) that is a special type of reliability optimization problems. A bi-objective RAP, which is related to a system of s independent k-out-of-n subsystems in series, is considered in this study. Maximization of the system reliability and minimization of the system cost are the objectives of the problem, and the system is constrained by a predefined weight. The components of a subsystem are supposed to be non identical. To deal with this problem, we propose some multi-objective meta-heuristic algorithms based on the elitist non-dominated sorting genetic algorithm (NSGA-II). New modified methods of diversity preservation and constraint handling are introduced in this study. According to these methods and some existing methods, we propose four multi-objective genetic algorithms for solving the considered problem. A numerical example, a statistical method and three performance metrics are utilized for analyzing and comparing the performance of these four genetic algorithms. The comparison represents the positive effect of modified methods of diversity preservation and constraint handling on the performance of the algorithms.

An approach to enhance availability of repairable systems: a case study of SPMs

Purpose – The purpose of this paper is to demonstrate a tactical approach to cope with the issues related to low availability of repairable machines or systems because of their poor reliability and maintainability. It not only explores the significance of availability, but also embarks upon a step-by-step procedure to earmark a relevant replenishment plan to check the mean time between failure (MTBF) and the mean time to repair (MTTR) efficiently. Design/methodology/approach – The literature review identifies the extent to which availability depends on reliability and maintainability, and highlights the diversified challenges appearing among repairable systems. Different improvement initiatives have been suggested to avoid downtime, after analyzing the failure and repair time data graphically. Relevant plots and growth curves captured the historical deviations and trends along with the time, which further helps to create more robust action plans to enrich the respective reliability and maintainability of machines. During the case study, the proposed methodology has been tested on four SPMs and successfully validated the claims after achieving around a 98 percent availability at the end. Findings – Graphical analysis is the key to developing suitable action plans to enhance the corresponding reliability and maintainability of a machine or system. By increasing the MTBF, the reliability level can be improved and similarly quick maintenance activities can help to restore the prospect of maintainability. Both of these actions ultimately reduce the downtime or increase the associated availability exponentially. Research limitations/implications – The work revolves around the availability of SPMs. Moreover, SPMs have been divided only into series sub-systems. The testability and supportability aspects have not been considered thoroughly during the fabrication of the approach. Originality/value – The work focusses on the availability of systems and proposed frameworks that helps to reduce downtime or its associated expenditure, which is generally being ignored. As a case study-based work especially on SPMs in the auto sector this paper is quite rare and will motivate affiliated engineers and practitioners to achieve future breakthroughs.