Reliability optimization for non-repairable series-parallel systems with a choice of redundancy strategies: Erlang time-to-failure distribution

2017 ◽  
Vol 231 (5) ◽  
pp. 587-604
Author(s):  
Meisam Sadeghi ◽  
Emad Roghanian
Keyword(s):  
Closed Form ◽  
System Reliability ◽  
High Performance ◽  
Parallel Systems ◽  
Erlang Distribution ◽  
Time To Failure ◽  
Allocation Model ◽  
Design Problems ◽  
Proposed Model ◽  
Engineering Design Problems

This article deals with a new redundancy allocation model for non-repairable series-parallel systems with multiple strategy choices. The proposed model simultaneously determines the type of components, number of active and standby components to maximize system reliability subject to design constraints. Traditionally, due to complexity and difficulty in obtaining the closed form version of system reliability, a convenient lower-bound on system reliability has been widely applied to approximate it. Assuming that switching mechanism time-to-failure is exponentially distributed, the closed form version of the reliability of subsystems with cold standby redundancy is derived analytically for the first time. This is successfully performed using Markov process and solving the relevant set of differential-difference equations. With respect to the obtained formulation, a semi-analytical expression for the reliability of subsystems with mixed redundancy strategy is also extracted. Component time-to-failure is assumed to follow an Erlang distribution which is suitable for most engineering design problems. The presented model is linear and in the form of standard zero-one integer programs and thus using integer programming algorithms guarantees optimal solutions. The computational results of solving a well-known example indicate the high performance of the proposed model in improving system reliability.

Reliability optimization for non-repairable series-parallel systems with a choice of redundancy strategies and heterogeneous components: Erlang time-to-failure distribution

2020 ◽  
pp. 1748006X2095257
Author(s):  
Meisam Sadeghi ◽  
Emad Roghanian ◽  
Hamid Shahriari ◽  
Hassan Sadeghi
Keyword(s):  
Lower Bound ◽  
Closed Form ◽  
System Reliability ◽  
Parallel Systems ◽  
Closed Form Expression ◽  
Erlang Distribution ◽  
Time To Failure ◽  
Form Expression ◽  
Integrodifference Equation ◽  
Cold Standby

The redundancy allocation problem (RAP) of non-repairable series-parallel systems considering cold standby components and imperfect switching mechanism has been traditionally formulated with the objective of maximizing a lower bound on system reliability instead of exact system reliability. This objective function has been considered due to the difficulty of determining a closed-form expression for the system reliability equation. But, the solution that maximizes the lower bound for system reliability does not necessarily maximize exact system reliability and thus, the obtained system reliability may be far from the optimal reliability. This article attempts to overcome the mentioned drawback. Under the assumption that component time-to-failure is distributed according to an Erlang distribution and switch time-to-failure is exponentially distributed, a closed-form expression for the subsystem cold standby reliability equation is derived by solving an integrodifference equation. A semi-analytical expression is also derived for the reliability equation of a subsystem with mixed redundancy strategy. The accuracy and the correctness of the derived equations are validated analytically. Using these equations, the RAP of non-repairable series-parallel systems with a choice of redundancy strategies is formulated. The proposed mathematical model maximizes exact system reliability at mission time given system design constraints. Unlike most of the previous formulations, the possibility of using heterogeneous components in each subsystem is provided so that the active components can be of one type and the standby ones of the other. The results of an illustrative example demonstrate the high performance of the proposed model in determining optimal design configuration and increasing system reliability.

scholarly journals Representing Complex Analogues Using a Function Model to Support Conceptual Design

2018 ◽  
Author(s):  
L. Siddharth ◽  
Amaresh Chakrabarti ◽  
Srinivasan Venkataraman
Keyword(s):  
Complex Systems ◽  
Engineering Design ◽  
Levels Of Abstraction ◽  
Function Model ◽  
Design Problems ◽  
Web Based ◽  
Proposed Model ◽  
Engineering Design Problems ◽  
Biological Domain ◽  
Engineered Systems

Analogical design has been a long-standing approach to solve engineering design problems. However, it is still unclear as to how analogues should be presented to engineering design in order to maximize the utility of these. The utility is minimal when analogues are complex and belong to other domain (e.g., biology). Prior work includes the use of a function model called SAPPhIRE to represent over 800 biological and engineered systems. SAPPhIRE stands for the entities: States, Actions, Parts, Phenomena, Inputs, oRgans, and Effects that together represent the functionality of a system at various levels of abstraction. In this paper, we combine instances of SAPPhIRE model for representing complex systems (also from the biological domain). We use an electric buzzer to illustrate and compare the efficacy of this model in explaining complex systems with that of a well-known model from literature. The use of multiple-instance SAPPhIRE model instances seems to provide a more comprehensive explanation of a complex system, which includes elements of description that are not present in other models, providing an indication as to which elements might have been missing from a given description. The proposed model is implemented in a web-based tool called Idea-Inspire 4.0, a brief introduction of which is also provided.

Optimal Designs by Means of Genetic Algorithms

2021 ◽  
pp. 344-354
Author(s):  
Lata Nautiyal ◽  
Preeti Shivach ◽  
Mangey Ram
Keyword(s):  
Genetic Algorithms ◽  
Modeling And Simulation ◽  
Engineering Design ◽  
Design Problem ◽  
High Performance ◽  
Mechanical Engineering ◽  
Design Parameters ◽  
Design Problems ◽  
Simulation Tools ◽  
Engineering Design Problems

With the advancement in contemporary computational and modeling skills, engineering design completely depends upon on variety of computer modeling and simulation tools to hasten the design cycles and decrease the overall budget. The most difficult design problem will include various design parameters along with the tables. Finding out the design space and ultimate solutions to those problems are still biggest challenges for the area of complex systems. This chapter is all about suggesting the use of Genetic Algorithms to enhance maximum engineering design problems. The chapter recommended that Genetic Algorithms are highly useful to increase the High-Performance Areas for Engineering Design. This chapter is established to use Genetic Algorithms to large number of design areas and delivered a comprehensive conversation on the use, scope and its applications in mechanical engineering.

Optimal Designs by Means of Genetic Algorithms

2018 ◽  
pp. 151-161
Author(s):  
Lata Nautiyal ◽  
Preeti Shivach ◽  
Mangey Ram
Keyword(s):  
Genetic Algorithms ◽  
Modeling And Simulation ◽  
Engineering Design ◽  
Design Problem ◽  
High Performance ◽  
Mechanical Engineering ◽  
Design Parameters ◽  
Design Problems ◽  
Simulation Tools ◽  
Engineering Design Problems

With the advancement in contemporary computational and modeling skills, engineering design completely depends upon on variety of computer modeling and simulation tools to hasten the design cycles and decrease the overall budget. The most difficult design problem will include various design parameters along with the tables. Finding out the design space and ultimate solutions to those problems are still biggest challenges for the area of complex systems. This chapter is all about suggesting the use of Genetic Algorithms to enhance maximum engineering design problems. The chapter recommended that Genetic Algorithms are highly useful to increase the High-Performance Areas for Engineering Design. This chapter is established to use Genetic Algorithms to large number of design areas and delivered a comprehensive conversation on the use, scope and its applications in mechanical engineering.

Reliability of demand-based warm standby system with common bus performance sharing

2018 ◽  
Vol 233 (4) ◽  
pp. 580-592 ◽  
Author(s):  
Heping Jia ◽  
Rui Peng ◽  
Yi Ding ◽  
Yonghua Song
Keyword(s):  
System Reliability ◽  
Time To Failure ◽  
Engineering Systems ◽  
Exponential Distributions ◽  
Numerical Studies ◽  
Proposed Model ◽  
Different Types ◽  
The Common ◽  
Standby System ◽  
Warm Standby

Redundancy techniques have been extensively utilized to enhance the reliability of engineering systems. There are three different types of standby techniques, cold, hot, and warm. Warm standby is adopted for less energy consumption and shorter leading time compared with hot standby and cold standby, respectively. Besides redundancy, performance sharing is another strategy to enhance system reliability, where the subsystems with sufficient performance can share the surplus performance with other subsystems with deficient performance. This article considers a demand-based warm standby system with a common bus performance-sharing mechanism, where the subsystems can share performance through the common bus and each subsystem can be configured with warm standby components in order to meet its demand. To be more general, the imperfect switching for the activations of warm standby components is also considered. Moreover, the multi-valued decision-diagram technique is developed to analyze the reliability for the proposed model. The proposed technique can handle systems whose time-to-failure distributions can follow arbitrary distributions in addition to the common utilized exponential distributions. Numerical studies are provided to validate the proposed model and technique.

scholarly journals Copper-graphene heterostructure for back-end-of-line compatible high-performance interconnects

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Myungwoo Son ◽  
Jaewon Jang ◽  
Yongsu Lee ◽  
Jungtae Nam ◽  
Jun Yeon Hwang ◽  
...  
Keyword(s):  
High Performance ◽  
Direct Synthesis ◽  
Chemical Vapor ◽  
Multilayer Graphene ◽  
Time To Failure ◽  
Practical Applications ◽  
Cu Interconnects ◽  
Pressure Chemical ◽  
Cu Interconnect ◽  
Pure Cu

AbstractHere, we demonstrate the fabrication of a Cu-graphene heterostructure interconnect by the direct synthesis of graphene on a Cu interconnect with an enhanced performance. Multilayer graphene films were synthesized on Cu interconnect patterns using a liquid benzene or pyridine source at 400 °C by atmospheric pressure chemical vapor deposition (APCVD). The graphene-capped Cu interconnects showed lower resistivity, higher breakdown current density, and improved reliability compared with those of pure Cu interconnects. In addition, an increase in the carrier density of graphene by doping drastically enhanced the reliability of the graphene-capped interconnect with a mean time to failure of >106 s at 100 °C under a continuous DC stress of 3 MA cm−2. Furthermore, the graphene-capped Cu heterostructure exhibited enhanced electrical properties and reliability even if it was a damascene-patterned structure, which indicates compatibility with practical applications such as next-generation interconnect materials in CMOS back-end-of-line (BEOL).

scholarly journals Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 651
Author(s):  
Shengyi Zhao ◽  
Yun Peng ◽  
Jizhan Liu ◽  
Shuo Wu
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Model Comparison ◽  
Research Direction ◽  
Disease Diagnosis ◽  
Tomato Leaf ◽  
Identification Accuracy ◽  
Main Research ◽  
Proposed Model ◽  
Complex Features

Crop disease diagnosis is of great significance to crop yield and agricultural production. Deep learning methods have become the main research direction to solve the diagnosis of crop diseases. This paper proposed a deep convolutional neural network that integrates an attention mechanism, which can better adapt to the diagnosis of a variety of tomato leaf diseases. The network structure mainly includes residual blocks and attention extraction modules. The model can accurately extract complex features of various diseases. Extensive comparative experiment results show that the proposed model achieves the average identification accuracy of 96.81% on the tomato leaf diseases dataset. It proves that the model has significant advantages in terms of network complexity and real-time performance compared with other models. Moreover, through the model comparison experiment on the grape leaf diseases public dataset, the proposed model also achieves better results, and the average identification accuracy of 99.24%. It is certified that add the attention module can more accurately extract the complex features of a variety of diseases and has fewer parameters. The proposed model provides a high-performance solution for crop diagnosis under the real agricultural environment.

Standards, Codes of Practice and Expert Systems

1988 ◽  
Vol 21 (1) ◽  
pp. 5-9 ◽  
Author(s):  
E G McCluskey ◽  
S Thompson ◽  
D M G McSherry
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Engineering Design ◽  
Performance Criteria ◽  
Design Problems ◽  
Codes Of Practice ◽  
Engineering Design Problems ◽  
And Performance

Many engineering design problems require reference to standards or codes of practice to ensure that acceptable safety and performance criteria are met. Extracting relevant data from such documents can, however, be a problem for the unfamiliar user. The use of expert systems to guide the retrieval of information from standards and codes of practice is proposed as a means of alleviating this problem. Following a brief introduction to expert system techniques, a tool developed by the authors for building expert system guides to standards and codes of practice is described. The steps involved in encoding the knowledge contained in an arbitrarily chosen standard are illustrated. Finally, a typical consultation illustrates the use of the expert system guide to the standard.

scholarly journals Copula-Based Bayesian Reliability Analysis of a Product of a Probability and a Frequency Model for Parallel Systems When Components Are Dependent

2021 ◽  
Vol 11 (4) ◽  
pp. 1697
Author(s):  
Shi-Woei Lin ◽  
Tapiwa Blessing Matanhire ◽  
Yi-Ting Liu
Keyword(s):  
System Reliability ◽  
Classification Tree ◽  
Parallel Systems ◽  
Reliability Model ◽  
Key Factors ◽  
Frequency Model ◽  
Independent Components ◽  
Aggregate Analysis ◽  
Dependence Assumption ◽  
Disaggregate Analysis

While the dependence assumption among the components is naturally important in evaluating the reliability of a system, studies investigating the issues of aggregation errors in Bayesian reliability analyses have been focused mainly on systems with independent components. This study developed a copula-based Bayesian reliability model to formulate dependency between components of a parallel system and to estimate the failure rate of the system. In particular, we integrated Monte Carlo simulation and classification tree learning to identify key factors that affect the magnitude of errors in the estimation of posterior means of system reliability (for different Bayesian analysis approaches—aggregate analysis, disaggregate analysis, and simplified disaggregate analysis) to provide important guidelines for choosing the most appropriate approach for analyzing a model of products of a probability and a frequency for parallel systems with dependent components.

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