Multi-state System Research Articles

Assessment of a generalized discrete time mixed [formula omitted]-shock model for the multi-state systems

In this paper, the life behavior of a shock model is studied, when the external shocks occur according to a binomial process whose interarrival times between successive shocks follow a geometric distribution. The system transits into a lower partially working state upon the occurrence of each interarrival time between two successive shocks less than a critical threshold, say δ. The system fails when k out of interarrival times between two successive shocks are less than δ, or the magnitude of the shock is larger than the other critical level, say γ. Such a model creates a multi-state system having a number of different states. The probability mass functions of system’s life time, the time spent by the system in a perfectly functioning state, and the total time spent by the system in partially working states are derived for the proposed model. The corresponding probability generating functions are also derived. The case of Markov shock occurrences is also studied. To illustrate the model studied in this paper, some numerical examples are also considered. Finally, an application in insurance is also provided.

Reliability and maintenance strategy for systems with aging‐dependent components

AbstractAn innovative approach is presented for the reliability analysis of aging multistate systems that considers the subsystems and their components' dependency. A reliability function is determined for an aging series system with the component dependency following the local load‐sharing rule, and a reliability function is determined for an aging “m out of n” system with the component dependency following the equal load‐sharing rule. Linking the results of those load‐sharing models, a mixed‐dependency model for multistate “m out of l”‐series systems is constructed by assuming the dependence between subsystems connected in series under the local load‐sharing rule and the dependence between their components under the equal load‐sharing rule. As a special case, the reliability of this system, modeled using piecewise exponential reliability functions, is considered, and the results are applied to characterize shipyard rope elevator reliability. Finally, the maintenance of this elevator as a repairable multistate system is analyzed with the time of renovation ignored.

A run shock-erosion model

Suppose that a system is subject to independent random shocks over discrete time periods, with the shocks occurring each period with a probability of p, and suppose that and are two critical levels. Assume that the occurrence of a shock run of length causes a partial failure, leading to a decrease in the efficiency of the system, and that a complete failure will happen when a shock run of length occurs. The occurrence of shocks under the mentioned conditions makes a multi-state system, which leads to the full active working and the partially working, with their distributions being under study here. In addition, the present study discusses the mean of full active working and that of the partially working and obtains the joint distribution for the number of partial failures and the failure time of the system. There will also be a discussion of the mean failure time of the system and some of its properties under different conditions of occurrence or nonoccurrence of partial failures. Finally, the present study examines the mean failure time of the system in a different scheme in which, erosion can also cause failure of the system.

Classification of Data Stream in Sensor Network With Small Samples

Compared with conventional narrowband radar, ultra-wideband (UWB) radar has strong anti-interference performance, low-frequency, and wide-frequency characteristics, a good penetrating ability, a high-resolution range, and a good target-recognition ability. It is effective for the detection of weak signals such as breathing or motion of the human body. Therefore, a UWB radar is widely used in the field of human body target detection for earthquake and snow disasters. At present, through-wall human target detection technology using a UWB radar has two challenging problems. First, UWB radar data has a dynamic acquisition process, whereas existing detection technologies mostly employ static learning models, resulting in an inability to process and learn data features online. There is a need to address target recognition based on sensor-network data stream. Second, UWB radar faces the problem of unbalanced data classification in the identification of through-wall targets, i.e., target recognition under small-sample conditions. To address these issues, this paper proposes an adaptive incremental recursive least-squares regression parameter estimation method based on an adaptive variable sliding window, which performs Gaussian function fitting on the data streams and adapts to the mean square error and self-adaptation variable sliding window threshold comparison to adaptively block dynamic data streams. The tensor space theory is used to extract and fuse the multisensor data, and the tensor depth learning algorithm is used to improve the recognition accuracy of the target detection under small-sample conditions. To evaluate the feasibility of the proposed algorithm, we use three UWB radars to build a multistate recognition system for human targets. The data stream adaptive block effect, single and multisensor classification effects were tested under small-sample conditions. The experimental results indicate that the proposed algorithm not only accurately segmented the dynamic data streams but also effectively realized multisensor information fusion and improved the real-time target monitoring under small-sample conditions.

ISHM-Oriented Time Decision-Making for Condition-Based Maintenance of Multistate Systems

In this paper, an integrated system health management oriented condition monitoring and maintenance decision-making model is proposed, which uses intelligent mobile agents to achieve real-time monitoring and to realize life cycle maintenance decision-making for multistate systems (MSSs). The model is applied to an aircraft engine system numerical example, the results of which illustrate that the proposed system and model have the ability to provide efficient and accurate engine system maintenance decision-making information and provide detailed health condition information. The feasibility of the proposed system and model is demonstrated and MSS frequent maintenance decisions are recorded and revealed.

Multi-state balanced systems in a shock environment

Reliability analysis of balanced systems has become an important research topic. In previous studies, a balanced system only has two states, i.e., perfect functioning and complete failure. However, most practical systems have more than two states because of the complex system structure and complicated working environment. To fill in this research gap, a general multi-state balanced system is proposed by considering that the components in the system and the whole system both have multiple states. In this paper, component state transitions are assumed to be caused by external shocks. Based on the operating states of all components, the multiple states of the system are determined according to different system balance degrees, formulated by a balance function. Multi-state balanced systems based on state distance and symmetric state distance are constructed in detail based on two specific balance functions. The corresponding state probability functions and some other reliability indexes are derived by using a two-step finite Markov chain imbedding approach. Finally, numerical examples are presented and some future research topics are discussed.

A two-stage intervened decision system with multi-state decision units and dynamic system configuration

This paper develops the performability and cost–benefit models for a two-stage intervened decision system with majority voting rule and binary input and output. The decision process of the system contains two stages: an inspection stage (stage 1) and a result submission stage (stage 2). During the first stage, each decision unit in the system will have multiple states and a supervisor will come to visit each unit and check its state for at most twice. The supervisor will conduct the first visit to each unit for certain. However, the behavior of the second visit to each unit will be determined by its state during the first visit. In addition, each decision unit may be removed from the system given certain states during each visit. Therefore the structure of system may change during the decision process. The units which are not removed during the first stage can submit the result at any time during the second stage. However, the performance of each remaining unit will be determined by the ending state of the first stage. Moreover, in order to improve the efficiency of the decision process, a check point is added to the second stage. The performability and cost–benefit models for this dynamic system are developed by considering the distribution of states at the end of the first stage. A three-step method will be proposed for model optimization. Some numerical examples for the three-step method will be presented. The proposed intervened decision system in this paper can be applied in many contexts such as financial investment, paper submission review and proposal evaluation, credit evaluation and loan application and product release and recall.

Trade-off between maintenance and protection for multi-state performance sharing systems with transmission loss

In a multi-state system with common bus performance sharing, the performance surplus from one element can be transmitted to other deficient elements as long as the total amount of transmission does not exceed the transmission capacity of the common bus. Existing literature fails to consider the transmission loss during the performance sharing process. Transmission loss widely exists in practice and is affected by many external factors. For example, the power transmission loss is affected by the distance, humility and temperature. In this research, we study a common bus performance sharing system consisting of N independent elements with consideration of performance loss during transmission. We propose three different transmission loss models that are frequently encountered in practice, and suggest their corresponding reliability evaluation algorithms. To improve the system availability, maintenance actions are taken to improve the component reliability, and protection is provided to defend the system against external impacts. We analyze the optimal trade-off between protection and maintenance by minimizing the system cost while satisfying a pre-specified system availability requirement. The proposed reliability models and algorithms are illustrated by a regional power distribution system. The numerical study has demonstrated the importance of considering transmission loss in reliability modeling and optimization.

Economic impact and chronic obstructive pulmonary disease outcomes of a comprehensive inhaler to nebulization therapy protocol implementation in a large multi-state healthcare system

Objective: There are currently 39 FDA-approved metered-dose (MDI) or dry-powder inhalers (DPI) on the US market. Most are high cost with significantly more drug in the device than needed for a typical average length of stay in acute care hospitals, which leads to significant waste. The objective was to assess the financial impact and chronic obstructive pulmonary disease (COPD) outcomes of a comprehensive inhaler to nebulization protocol implemented in a large multi-state US health system.Methods: The retrospective study evaluated respiratory drug costs at 28 hospitals in the health system after a phased implementation of the automatic inhaler to nebulization protocol. Purchasing data was collected for all respiratory medications impacted by the protocol for the twelve months preceding as well as the two subsequent twelve-month periods following implementation at each facility. COPD length of stay (LOS) and 30 day readmissions were also reviewed. An attempt was made to evaluate the impact on respiratory therapy (RT) department workload.Results: Compared to pre-implementation, system-wide drug expenditures declined $1,561,011 (38.5%) and $1,646,411 (40.6%) in post-implementation year 1 (PY1) and post-implementation year 2 (PY2), respectively. COPD LOS and 30 day readmissions were not adversely affected and remained relatively stable in comparative periods. Objective impact on RT productivity and labor statistics was not ascertained due to complicated variables and multiple service lines.Conclusions: In an era of increased drug costs incurred by hospitals, a comprehensive inhaler to nebulization protocol significantly decreased costs without incurring any negative observed trends in COPD LOS or readmissions.

Group Acupuncture Model in a Cancer Institute: Improved Access and Affordability.

Cancer patients may experience significant symptom mitigation from acupuncture. However, this service may not be easily accessible or affordable at all cancer institutions. The development of a group acupuncture program provided one institution with improved availability, lower cost to patients, and a foundation for oncology acupuncture research. This care delivery model was deployed at a large southeastern cancer institution within a multistate academic-community hybrid hospital system. The cancer institute serves >15,000 patients annually. Acupuncture is provided through the institute's Integrative Medicine section of the Department of Supportive Oncology. The purpose of this commentary is to describe the successful transition from an individual to group acupuncture model at this cancer institute. With the implementation of group acupuncture, patient visits increased 275% from individual care delivery. Although successful implementation of a group acupuncture model may be affordable and clinically positive, the authors also share unique challenges learned through the development and expansion of this program.

Optimization of Multilevel Inspection Strategy for Nonrepairable Multistate Systems

The reliability function of a specific individual system can be dynamically updated by utilizing inspection data collected over time. However, due to limited inspection resources, such as time, budget, and manpower, it is oftentimes impossible to collect all the inspection data for all the components, subsystems, and the entire system simultaneously. There is an urgent need to optimally allot the limited inspection resources across multiple physical levels of a system, so as to identify the health status of a system and/or its subset of components of interest as accurately as possible to facilitate the ensuing system health management. To address the above research question, a metric is put forth in this paper to quantify the effectiveness of a particular multilevel inspection strategy for multistate systems (MSSs). Based on the proposed metric, an optimization problem is formulated to seek the optimal multilevel inspection strategy which possesses the maximum effectiveness of revealing the true state of a system and/or its subset of components of interest under limited inspection resources. The resulting optimization problem is resolved by a tailored ant colony optimization algorithm. The findings from our illustrative examples are 1) the proposed metric is capable of quantifying the effectiveness of various multilevel inspection strategies; 2) the analytical solution of the proposed metric is exactly the same with the simulation results, but much more computationally efficient; and 3) the optimal inspection strategy varies with respect to the operation time of a specific individual system.

Efficient construction of generalized master equation memory kernels for multi-state systems from nonadiabatic quantum-classical dynamics

Methods derived from the generalized quantum master equation (GQME) framework have provided the basis for elucidating energy and charge transfer in systems ranging from molecular solids to photosynthetic complexes. Recently, the nonperturbative combination of the GQME with quantum-classical methods has resulted in approaches whose accuracy and efficiency exceed those of the original quantum-classical schemes while offering significant accuracy improvements over perturbative expansions of the GQME. Here, we show that, while the non-Markovian memory kernel required to propagate the GQME scales quartically with the number of subsystem states, the number of trajectories required scales at most quadratically when using quantum-classical methods to construct the kernel. We then present an algorithm that allows further acceleration of the quantum-classical GQME by providing a way to selectively sample the kernel matrix elements that are most important to the process of interest. We demonstrate the utility of these advances by applying the combination of Ehrenfest mean field theory with the GQME (MF-GQME) to models of the Fenna-Matthews-Olson (FMO) complex and the light harvesting complex II (LHCII), with 7 and 14 states, respectively. This allows us to show that the MF-GQME is able to accurately capture all the relevant dynamical time scales in LHCII: the initial nonequilibrium population transfer on the femtosecond time scale, the steady state-type trapping on the picosecond time scale, and the long time population relaxation. Remarkably, all of these physical effects spanning tens of picoseconds can be encoded in a memory kernel that decays only after ∼65 fs.

Dynamically encircling exceptional points in a three-mode waveguide system

Dynamically encircling exceptional points (EPs) in non-Hermitian systems has attracted considerable attention recently, but all previous studies focused on two-state systems, and the dynamics in more complex multi-state systems is yet to be investigated. Here we consider a three-mode non-Hermitian waveguide system possessing two EPs, and study the dynamical encircling of each single EP and both EPs, the latter of which is equivalent to the dynamical encircling of a third-order EP that has a cube-root behavior of eigenvalue perturbations. We find that the dynamics depends on the location of the starting point of the loop, instead of the order of the EP encircled. Compared with two-state systems, the dynamical processes in multi-state systems exhibit more non-adiabatic transitions owing to the more complex topological structures of energy surfaces. Our findings enrich the understanding of the physics of multi-state non-Hermitian systems and may lead to the design of new wave manipulation schemes.

Zastosowanie sieci bayesowskiej do analizy niezawodności złożonych systemów wielostanowych w warunkach niepewności

Reliability analysis of complex multi-state system has uncertainty, which is caused by complex structures, limited test samples, and insufficient reliability data. By introducing fuzzy mathematics and grey system theory into the Bayesian network, the model of the grey fuzzy Bayesian network is built, and the reliability analysis method of complex uncertainty multi-state system with the non-deterministic membership function and the interval characteristic quantity is proposed in this paper. Using the trapezoidal membership function with fuzzy support radius variable to describe the fault state of the component, it can effectively avoid the influence of human subjective factors on the selection of the membership function and solve the problem that the fault states of the system and its components are difficult to define accurately. And the conditional probability table containing interval grey numbers is constructed to effectively express the uncertain fault logic relationship between the system and its components. Moreover, a parameter planning model of the system reliability characteristic quantities is constructed, and the system reliability characteristic quantities are expressed as the form of interval values. Finally, two sets of numerical experiments are conducted and discussed, and the results show that the proposed method is an effective and a promising approach to reliability analysis for complex uncertainty multi-state systems.

Non-volatility using materials with only volatile properties: Vertically integrated magnetoelectric heterostructures and their potential for multi-level-cell devices

Different (1–3) heterostructures, such as BiFeO3-CoFe2O4 and BiFeO3-CuFe2O4 on Pb(Mg1/3Nb2/3)0.74Ti0.26O3 (PMN-26PT), were selected for study as possible materials for magnetoelectric (ME) random access memory. The (1–3) heterostructures were deposited, and multimagnetic states were found under different E-field (E) conditions. Upon removal of E, two possible remnant magnetization states remained stable. If an H-field (H) was also applied, two additional stable remnant magnetization states were found. Our investigations demonstrate (1–3) heterostructures with nonvolatility even though the individual phases/substrates had only volatile properties. This simplifies materials selection for multistate systems based on these heterostructures, averting difficulties with compositional nonuniformity and property repeatability, in particular, with regard to PMN-xPT crystal substrates. With such N≥4 magnetic state systems, a multilevel-cell memory device could readily be built with high ME coupling and numerous accessible magnetic states.

Multi-mission selective maintenance modelling for multistate systems over a finite time horizon

In the life cycle of a long-life structure, multiple maintenances are often required to ensure safety or normal performance. This article developed a multi-mission selective maintenance optimization model for a system with several multistate components over a finite time horizon. The model aims to minimize the total cost by determining the optimal intervention time and the corresponding maintenance option of each component. The system reliability requirement is taken as the constraint. The degradation process of each component is characterized by a homogeneous continuous time Markov chain in this model. Considering the uncertainties that exist during the deterioration–maintenance process, the condition state of each component at any time is described by a state probability vector. In addition, the components in the system are considered to be structurally dependent during the maintenance process, and a directed graph is used to represent the precedence relations among these components. A differential evolution algorithm is utilized to solve this complicated optimization model. A numerical example and an engineering application are presented to illustrate the availability of the proposed model.

Reducing Unneeded Clinical Variation in Sepsis and Heart Failure Care to Improve Outcomes and Reduce Cost: A Collaborative Engagement with Hospitalists in a MultiState System.

To (1) measure hospitalist care for sepsis and heart failure patients using online simulated patients, (2) improve quality and reduce cost through customized feedback, and (3) compare patient-level outcomes between project participants and nonparticipants. We conducted a prospective, quasi-controlled cohort study of hospitalists in eight hospitals matched with comparator hospitalists in six nonparticipating hospitals across the AdventHealth system. We provided measurement and feedback to participants using Clinical Performance and Value (CPV) vignettes to measure and track quality improvement. We then compared length of stay (LOS) and cost results between the two groups. 107 providers participated in the study. Over two years, participants improved CPV scores by nearly 8% (P < .001), with improvements in utilization of the three-hour sepsis bundle (46.0% vs 57.7%; P = .034) and ordering essential medical treatment elements for heart failure (58.2% vs 72.1%; P = .038). In study year one, average LOS observed/expected (O/E) rates dropped by 8% for participants, compared to 2.5% in the comparator group, equating to an additional 570 hospital days saved among project participants. In study year two, cost O/E rates improved from 1.16 to 0.98 for participants versus 1.14 to 1.01 in the comparator group. Based on these improvements, we calculated total cost savings of $6.2 million among study participants, with $3.8 million linked to system-wide improvements and an additional $2.4 million in savings attributable to this project. CPV case simulation-based measurement and feedback helped drive improvements in evidence-based care that translated into lower costs and LOS, above-and-beyond other improvements at AdventHealth.

Reliability analysis of multi‐state series systems with performance sharing mechanism under epistemic uncertainty

AbstractFor real engineering systems, it is sometimes difficult to obtain sufficient data to estimate the precise values of some parameters in reliability analysis. This kind of uncertainty is called epistemic uncertainty. Because of the epistemic uncertainty, traditional universal generating function (UGF) technique is not appropriate to analyze the reliability of systems with performance sharing mechanism under epistemic uncertainty. This paper proposes a belief UGF (BUGF)‐based method to evaluate the reliability of multi‐state series systems with performance sharing mechanism under epistemic uncertainty. The proposed BUGF‐based reliability analysis method is validated by an illustrative example and compared with the interval UGF (IUGF)‐based methods with interval arithmetic or affine arithmetic. The illustrative example shows that the proposed BUGF‐based method is more efficient than the IUGF‐based methods in the reliability analysis of multi‐state systems (MSSs) with performance sharing mechanism under epistemic uncertainty.

Reliability analysis of semi-Markov systems with restriction on transition times

In this paper, a competing risk model with a restriction on transition times for semi-Markov multi-state repairable systems is proposed. The states of semi-Markov systems can be divided into three categories: a normal working subset, a defective working subset and a breakdown subset which contains an absorbing state where the system cannot escape once entering it. If the number of transitions between the normal and defective working subsets exceeds a given value, the system will be abandoned due to the high maintenance costs. The theory of aggregated stochastic process is employed to obtain the closed-form formulas of the probabilities of competing risks, distributions of survival times and point-wise availabilities. An integer nonlinear programming is presented which enables the system to satisfy the prescribed performance requirements and control its economic cost. A corresponding algorithm for solving the integer nonlinear programming is provided to find the optimal number of transition times. Two numerical examples including Markov process and semi-Markov process are conducted to illustrate the obtained results and proposed methods finally.

Polarization and depolarization of monomial ideals with application to multi-state system reliability

We study the family of depolarizations of a squarefree monomial ideal $I$, i.e. all monomial ideals whose polarization is $I$. We describe a method to find all depolarizations of $I$ and study some of the properties they share and some they do not share. We then apply polarization and depolarization tools to study the reliability of multi-state coherent systems via binary systems and vice versa.