Non-identical Components Research Articles

Novel condition-based opportunistic maintenance policy for series systems with dependent competing failure processes

This paper investigates a novel condition-based opportunistic maintenance model for a two-component series system. The system comprises two non-identical components, that is, Component 1 and Component 2, suffering from shocks with different intensities. The two components are subject to dependent competing failure processes, that is, soft and hard failures. The component fails whichever type of failure process occurs. At each inspection, the replacement decision for components is made according to the condition of both components. In addition, when Component 1 is preventively replaced and Component 2 reaches the opportunistic maintenance threshold, opportunistic maintenance (OM) is implemented on Component 2. The optimal inspection interval, preventive replacement (PR) threshold, and OM threshold are derived by minimizing the long-run expected maintenance cost rate. Finally, the superiority of the proposed maintenance model is verified by an illustrative example.

Statistical inference on multicomponent stress–strength reliability with non-identical component strengths using progressively censored data from Kumaraswamy distribution

Statistical inference on multicomponent stress–strength reliability with non-identical component strengths using progressively censored data from Kumaraswamy distribution

Improving reliability system in radar model based on two parameters Weibull distribution

In this paper, the two parameters Weibull distribution lifetime model has been used to improve the reliability of a system in radar structure. This model is flexible to any lifetime data analysis and describing all stages of failure rate. Redundant and reduction methods of improving the system reliability are provided, including cold, hot, and imperfect methods. The lifetimes of units are assumed to be independent and non-identical components. To distinguish between different methods, we computed the mean time to failure and γ -fractiles. Reliability equivalence factors are also derived. Simulation examples were given to differentiate between different improving methods.

Selective maintenance of continuously degrading systems with non-identical and stochastically dependent components

Selective maintenance (SM) is often performed on systems having limited maintenance resources to enhance mission reliability. In practical engineering, components within a system are typically distinct and correlated, leading to differences in the degradation processes and sensitivity to working conditions, as well as stochastic dependencies (S-dependencies) among the components. However, in most studies on the SM of continuously degrading systems (CDSs), these features are not considered, thereby limiting the accuracy of reliability evaluations and the efficiency of maintenance strategies. Therefore, considering the differences and S-dependencies of components, we investigate SM optimization for CDSs. Focusing on the diverse and stochastically dependent degradation processes of non-identical components, we first propose an extended degradation rate interaction model integrated with a general stochastic process to describe the multiple degradation processes of components and manifest the sensitivity differences to working conditions in the degradation states and rates. Next, a derivation method is developed to obtain explicit mission reliability functions for systems with arbitrary configurations. Subsequently, an SM optimization model that incorporates the effects of multiple differences and S-dependencies of the components is formulated and used to obtain an optimal maintenance strategy that considers resource and mission reliability constraints. Finally, the effectiveness of the proposed method is demonstrated using two numerical examples.

Multivariate stochastic comparisons of sequential order statistics with non-identical components

Multivariate stochastic comparisons of sequential order statistics with non-identical components

1-out-of-n standby systems with storages and activation moment-dependent component operation time limit

This paper pioneers the modeling and optimization of a 1-out-of-n standby system with a required mission time and non-identical components, whose operation times are limited and dependent on their activation time and dynamic resource supply and storage units. Each component contains a main operating unit characterized by certain time-to-failure distribution and mode-dependent resource demands, a resource generator characterized by a time-varying resource generation rate, and a storage unit characterized by an initial amount of resource and the maximum capacity. The resource generator and storage interact collaboratively to supply the dynamic resource demand of the main unit. Based on an event transition probabilistic model, a new numerical algorithm is proposed for assessing the mission success probability (MSP) of the considered standby system. An optimization problem is further formulated and solved, which determines the activation sequence of standby components maximizing the MSP. A detailed case study of a standby sensor system with the photo-voltaic power supply source and battery storage is provided to demonstrate the proposed model and examine the effects of several system parameters on the MSP and optimization solutions. Several useful findings related to the MSP improvement are revealed from the case study.

Data-driven joint optimization of maintenance and spare parts provisioning for deep space habitats: A stochastic programming approach

The utilization of sensor data to make informed decisions on maintenance schedule and spare parts provisioning will be determinant to the development of self-aware habitable facilities–so called SmartHabs–in deep space regions. To address this novel and challenging problem, this work presents a chance constrained stochastic mixed integer program to jointly optimize maintenance and spare parts provisioning for ensuring reliable and cost-efficient operations of SmartHabs. A SmartHab is modeled as a multi-unit system with non-identical components that randomly degrade over time. The proposed optimization model is driven by telemetry sensor data as the uncertainty of the remaining lifetime of the components is estimated by data-driven prognostic algorithms. The model further incorporates characteristic operational conditions of deep space habitats such as a long planning horizon that requires multiple maintenance decisions for each component, maintenance task assignment to humans and robots, long lead times between resupply trips, and high penalties due to stock-outs of spare parts. The formulation introduces chance constraints restricting the unavailability of critical components and the number of corrective repairs with high probability under the uncertainty of component failures. The nonlinearities induced by the chance constraints are circumvented by the derivation of two linearization methods based on scenario representation and safe approximations, resulting in their tractable mixed integer linear reformulations. The proposed model is validated by simulations, showing reliable and effective plans against benchmark approaches while evidencing to be computationally scalable as the number of components increases.

Age replacement policy for heterogeneous parallel systems

The optimization policy on age replacement mostly focuses on systems comprised of identical components. In this paper, both discrete and continuous time age replacement policies are considered by relaxing the assumption of identical components and working with heterogeneous parallel system, i.e. system with not necessarily identical components. In particular, necessary conditions are obtained for the existence and uniqueness of optimal replacement cycle/time for the parallel system with two nonidentical components under the proposed policy. The extension of the results to a system with more than two components is also presented.

Efficient performability analysis of dynamic multi-state k-out-of-n: G systems

A dynamic multi-state k-out-of-n: G system, denoted by DMS(k, n, G) is a system where the system and its components exhibit multiple performance levels, and the system has different requirements on the number of working components in different states. DMS(k, n, G) abounds in both industrial and military applications. In this paper, a novel and efficient analytical method based on multi-valued decision diagrams (MDDs) is proposed for performability assessment of DMS(k, n, G) with non-identical components. Unlike existing approaches where multiple combination operations between models of lower performance levels are needed for constructing the model of a higher performance level, the proposed MDD generation algorithm constructs the system performability MDD in a top-down manner by considering multiple system state requirements simultaneously. A smart home lighting control system is analyzed to demonstrate the application of the proposed method. A detailed case study of an oil supply system is provided to verify the correctness of the proposed method and illustrate component sensitivity analysis. Complexity analysis and comprehensive empirical studies are performed to demonstrate that the efficiency of the proposed model construction is greatly improved as compared to the existing method, enabling fast model generation and efficient analysis of large-scale DMS(k, n, G).

State-based transition probabilities for systems subject to periodic inspection interval

ABSTRACT In this paper, we present a general formula to calculate transition probabilities for six different types of systems based on their redundancy strategy and the status of the components. The systems are under periodic inspection policy and their components are repairable. The investigated systems include system I – active redundancy without any component to be replaced or repaired; system II – active with the component(s) to be replaced and repaired; system III – active with the component(s) to be repaired; system IV – standby without any component(s) to be replaced or repaired; system V – standby with the component(s) to be replaced and repaired; and system VI – standby with the component(s) to be repaired. In addition, all components in a system are considered non-identical. To calculate the transition probabilities for systems IV, V, and VI, we first consider a system with n non-identical components and cold standby configuration (NIC-CSC) and calculate the system state probabilities using Markov theory. Then, we present the general formula transition probabilities for systems IV, V, and VI using the results of the NIC-CSC system inspection interval. We demonstrate how to calculate the transition probabilities and matrixes for the six above-mentioned systems using the provided formulas. Moreover, we use the provided formulas to optimize the inspection interval of these systems using a modified full enumeration technique.

EPISTEMOLOGICAL POTENTIAL OF TARGET TEXTS: A CASE STUDY OF THE RUSSIAN-JAPANESE PARALLEL FICTION CORPUS

This paper discusses the possibility of using a parallel text corpus to study grammatical issues of the target language, and the epistemological value of target texts is argued. Parallel text corpora are more often used in their comparative aspect, even though they have a significant content and epistemological potential for the systematization of linguistic knowledge about the target language as well. This work, carried out as a part of a general study of Japanese agentivity, describes an attempt to research the corpus material to identify recurrent patterns in the target language and to determine the distribution regularities of agentive features in Japanese. This paper was carried out using the material from the author’s parallel Russian-Japanese corpus of fiction texts. The main criterion used to collect this dataset is the markedness of sentences with inanimate subjects, which is imposed by such cognitive and cultural factors as the linguistic and social subject-object hierarchy: human beings, as the highest creatures on the animacy scale, could not be affected by an inanimate subject. The set of grammatically non-identical components of the syntactic structure of the source and target texts identified in the material of the corpus became the starting point for a detailed analysis of the mechanism for distributing syntactic and semantic roles in a Japanese sentence.

System reliability analysis for independent and nonidentical components based on survival signature

Survival signatures have been widely used for analyzing arbitrary system structures having independent failure events. It is challenging to study system reliability when diverse components are present since the fundamental premise of the survival signatures is that components should be interchangeable within the same subsystem. In this research, two methods based on the fundamental survival signature idea are suggested to examine the reliability of complex systems with independently but not necessarily identically distributed (INID) components. The first algorithm is based on the weighted random sampling (WRS) method to calculate the survival signatures. The second algorithm adopts the idea of divide-and-conquer in computing the probability structure. The application of these two algorithms and the exhaustive algorithm in analyzing the example systems are presented. The results show that the two algorithms can significantly reduce the computation time compared to the traditional methods. Finally, the developed algorithms are used in the reliability analysis of a real practical problem, the Tsinghua campus water supply pipeline system. The results regarding the predictions of system reliability as well as the component importance index are discussed.

Reliability and Optimal Replacement Policy for a Consecutive k-out-of-n:F System with Independent and Nonidentical Distributed Components

In this study, a preventive maintenance policy for a linear (or circular) consecutive [Formula: see text]-out-of-[Formula: see text]:[Formula: see text] system with independent and nonidentical distribution components is proposed. A set of all possible states of working system is used to calculate the system’s reliability. The primary objective of this study is to determine optimal replacement time for systems with nonidentical components. The costs for each component in the proposed optimal replacement policy are different.

Which black hole formed first? Mass-ratio reversal in massive binary stars from gravitational-wave data

ABSTRACT Population inference of gravitational-wave catalogues is a useful tool to translate observations of black hole mergers into constraints on compact-binary formation. Different formation channels predict identifiable signatures in the astrophysical distributions of source parameters, such as masses and spins. One example within the scenario of isolated binary evolution is mass-ratio reversal: even assuming efficient core–envelope coupling in massive stars and tidal spin-up of the stellar companion by the first-born black hole, a compact binary with a lighter, non-spinning first-born black hole and a heavier, spinning second-born black hole can still form through mass transfer from the initially more to less massive progenitor. Using current LIGO/Virgo observations, we measure the fraction of sources in the underlying population with this mass–spin combination and interpret it as a constraint on the occurrence of mass-ratio reversal in massive binary stars. We modify commonly used population models by including negligible-spin subpopulations and, most crucially, non-identical component spin distributions. We do not find evidence for subpopulations of black holes with negligible spins and measure the fraction of massive binary stars undergoing mass-ratio reversal to be consistent with zero and $\lt 32{{\ \rm per\ cent}}$ ($99{{\ \rm per\ cent}}$ confidence). The dimensionless spin peaks around 0.2–0.3 appear robust, however, and are yet to be explained by progenitor formation scenarios.

MTTF and availability of semi-Markov missions with non-identical generally distributed component lifetimes

We analyze mean time to failure and availability of systems that perform semi-Markov missions. The mission process is the minimal semi-Markov process associated with a Markov renewal process. Therefore, the successive phases of the mission follow a Markov chain, and the phase durations are generally distributed. The lifetimes of the non-identical components in the system are assumed to be generally distributed and are modeled using intrinsic aging concepts. Moreover, the lifetime parameters of the components and the configuration of the system change depending on the phases of the mission. We characterize the mean time to failure through solving a Poisson equation, and we analyze the system availability assuming that repair duration has a general distribution which is dependent on the phase of the mission during which the failure has occurred and on the deterioration level of the system.

An algorithmic reliability evaluation approach for a multi-state k-out-of-n:G system with nonidentical and large number of components

This paper deals with the dynamic reliability analysis of two different three-state k-out-of- n:G systems that consist of independent and nonidentical components. Marginal and joint survival functions are represented by using permanents. Due to the permanent-based representations, it is hard to find out the survival probabilities when the number of components in the systems is large. Thus, we propose an effective algorithmic approach to evaluate the survival probabilities for the systems with large number of components. Also, for the dynamic performance evaluation of the three-state systems, non-homogeneous continuous time Markov (NHCTM) degradation process assumption is used. Survival probabilities of each system at different states with different number of components are provided and the results are discussed for both major and minor degradation processes.

System reliability-redundancy optimization with cold-standby strategy by fitness-distance balance stochastic fractal search algorithm

Reliability-redundancy allocation problem (RRAP) is an interesting subject in the field of reliability engineering that attracted attention of many researchers. RRAP tries to maximize the system reliability while creating a tradeoff between the component reliability and level of redundancy for each subsystem. Early studies in the cold-standby strategy used the lower bound formula to estimate the system reliability. But in this paper, a newly introduced Markovian process-based approach is applied for calculating the exact reliability values of cold-standby systems with the imperfect switching system. A newly developed evolutionary algorithm called fitness-distance balance stochastic fractal search is adjusted for solving the RRAP as an NP-hard optimization model, and the obtained results are compared with other counterparts by using numerical examples on three well-known benchmark problems. Finally, to justify the performance of the applied Markovian method in practical viewpoint, a pump system with non-identical components in a chemical plant is analysed as a real-world case study.

The cancellation heuristic in intertemporal choice shifts people\u2019s time preferences

Building on past research in risky decision making, the present research investigated whether the cancellation heuristic is evident in intertemporal choice. Specifically, the cancellation heuristic posits that whenever choice options are partitioned into multiple components, people ignore seemingly identical components and compare the non-identical components. We nudged people to employ the cancellation heuristic by partitioning both the smaller earlier reward and the larger later reward into a seemingly identical component and a non-identical component. Given diminishing marginal utility, we hypothesized that people would perceive an identical difference between the smaller earlier reward and the larger later reward as being subjectively greater when both amounts are smaller in magnitude, thereby increasing the relative attractiveness of the larger later reward in the partition condition. We conducted four studies, including two incentive-compatible lab experiments, one incentive-compatible lab-in-the-field experiment, and one survey study using choices among both gains and losses. We consistently found that this choice architecture intervention significantly increased people’s likelihood of choosing the larger later reward. Furthermore, we provide evidence of the underlying mechanism-people’s intertemporal decisions shifted to a greater extent in the cancellation condition, particularly if their marginal utility diminished faster. The findings indicate that two features of human psychology-diminishing marginal utility and the cancellation heuristic-can be simultaneously utilized to nudge people to make decisions that would be better for them in the long run.

Optimizing a joint reliability-redundancy allocation problem with common cause multi-state failures using immune algorithm

Redundancy-reliability allocation problem (RRAP) is a well-known problem in reliability area. In general, this problem aims to maximize a system’s reliability or minimize a system’s costs under some constraints. In this paper, we develop a RRAP for a series-parallel system with multi-state components. Thus, the subsystems’ components, the system’s subsystems, and the system have different working states with corresponding working probabilities. The RAP in the paper is a RAP with mix components (RAPMC). We consider the choice of allocating non-identical components to each sub-system. Moreover, we consider the common cause failure (CCF) for the components, which causes simultaneous failure of all identical components of a subsystem. We assume the component’s failure state probability is reduced by conducting technical activities, and the reduced probability is added to the component’s working states’ probabilities. The model’s objective function is to minimize the system’s costs under a minimum reliability level and other constraints by allocating the optimal set of components to each subsystem and determining each component’s technical activities level. Since the RRAP belongs to the Np-Hard category of problems, an immune algorithm is used to solve the developed problem. The results indicate considering the technical activities decreases the system’s costs.

Genetic Context Significantly Influences the Maintenance and Evolution of Degenerate Pathways.

Understanding the evolution of novel physiological traits is highly relevant for expanding the characterization and manipulation of biological systems. Acquisition of new traits can be achieved through horizontal gene transfer (HGT). Here, we investigate drivers that promote or deter the maintenance of HGT-driven degeneracy, occurring when processes accomplish identical functions through nonidentical components. Subsequent evolution can optimize newly acquired functions; for example, beneficial alleles identified in an engineered Methylorubrum extorquens strain allowed it to utilize a “Foreign” formaldehyde oxidation pathway substituted for its Native pathway for methylotrophic growth. We examined the fitness consequences of interactions between these alleles when they were combined with the Native pathway or both (Dual) pathways. Unlike the Foreign pathway context where they evolved, these alleles were often neutral or deleterious when moved into these alternative genetic backgrounds. However, there were instances where combinations of multiple alleles resulted in higher fitness outcomes than individual allelic substitutions could provide. Importantly, the genetic context accompanying these allelic substitutions significantly altered the fitness landscape, shifting local fitness peaks and restricting the set of accessible evolutionary trajectories. These findings highlight how genetic context can negatively impact the probability of maintaining native and HGT-introduced functions together, making it difficult for degeneracy to evolve. However, in cases where the cost of maintaining degeneracy was mitigated by adding evolved alleles impacting the function of these pathways, we observed rare opportunities for pathway coevolution to occur. Together, our results highlight the importance of genetic context and resulting epistasis in retaining or losing HGT-acquired degenerate functions.