Phased-mission Systems Research Articles

A Bayesian network for reliability assessment of man-machine phased-mission system considering the phase dependencies of human cognitive error

Existing researches on the reliability assessment of phased-mission systems (PMSs) focus mainly on the phase dependencies of the machine state. However, with regard to the man-machine PMS (MMPMS), it also has non-negligible phase dependencies of human cognitive error. For example, the error of omission in previous phase may result in an identical error if the operator experiences a similar working scenario in a subsequent phase. To address the phase dependencies of human cognitive error, a novel method for the reliability assessment of MMPMS is proposed. First, the phase dependencies of human cognitive error are analyzed and categorized into four types based on the framework of situation awareness. A decision tree is then developed to quantify the dependence level. Second, the Bayesian network (BN) is used to construct the system reliability model for each phase from the perspective of mental model. Subsequently, the phase dependencies of machine state and of human cognitive error are mapped to BN to integrate constructed single-phase models as a multi-phase system reliability model. Third, the reliability of MMPMS can be assessed based on the conditional probabilities of all the nodes. Finally, the proposed method is exemplified with a multi-phase reconnaissance mission of an unmanned aerial vehicle.

Formal Analysis of Repairable Phased-Mission Systems With Common Cause Failures

Although several studies have been devoted to the reliability analysis of phased-mission systems (PMSs) considering the influence of common cause failures (CCFs), statistical correlation and repairable behavior still pose challenges to the analysis. In this article, a hierarchical formal model is proposed for the reliability analysis of repairable PMSs with CCFs. The low-level model is based on continuous time Markov chains and multiple beta factor theory used to construct the failure and repair behaviors of different missions under the effect of CCFs. The upper-level model realizes phase transition based on the Erlang distribution. The model can be implemented in PRISM, a tool that supports probabilistic model checking technology, and can be automatically verified by the properties (termed as reliability and availability in this article) defined by continuous stochastic logic. The proposed model introduces the benefits of probabilistic model checking into the analysis of PMSs for the first time. The formal hierarchical model is demonstrated by an example of a field programmable gate array system based on different design modes. Then, the influence of CCFs on the reliability of three different mission phase combinations are discussed. Our model can help researchers verify system properties for dynamic, complex, and continuous tasks in the initial design phase, thereby optimizing the design pattern or task arrangement.

Qualitative Analysis of Commercial Services in MEC as Phased-Mission Systems

Currently, mobile edge computing (MEC) is one of the most popular techniques used to respond to real-time services from a wide range of mobile terminals. Compared with single-phase systems, commercial services in MEC can be modeled as phased-mission systems (PMS) and are much more complex, because of the dependencies across the phases. Over the past decade, researchers have proposed a set of new algorithms based on BDD for fault tree analysis of a wide range of PMS with various mission requirements and failure behaviors. The analysis to be performed on a fault tree can be either qualitative or quantitative. For the quantitative fault tree analysis of PMS by means of BDD, much work has been conducted. However, for the qualitative fault tree analysis of PMS by means of BDD, no much related work can be found. In this paper, we have presented some efficient methods to calculate the MCS encoding by a PMS BDD. Firstly, three kinds of redundancy relations-inclusive relation, internal-implication relation, and external-implication relation-within the cut set are identified, which prevent the cut set from being minimal cut set. Then, three BDD operations, IncRed, InImpRed, and ExImpRed, are developed, respectively, for the elimination of these redundancy relations. Using some proper combinations of these operations, MCS can be calculated correctly. As an illustration, some experimental results on a benchmark MEC system are given.

Reliability assessment of engine electronic controllers based on Bayesian deep learning and cloud computing

The reliability of an Engine Electronic Controller (EEC) attracts attention, which has a critical impact on aircraft engine safety. Reliability assessment is an important part of the design phase. However, the complex composition of EEC and the characteristic of the Phased-Mission System (PMS) lead to the difficulty of assessment. This paper puts forward an advanced approach, considering the complex products and uncertain mission profiles to evaluate the Mean Time Between Failures (MTBF) in the design phase. The failure mechanisms of complex components are deduced by Bayesian Deep Learning (BDL) intelligent algorithm. And copious samples of reliability simulation are solved by cloud computing technology. Based on the result of BDL and cloud computing, simulations are conducted with the Physics of Failure (PoF) theory and Failure Behavior Model (FBM). This reliability assessment approach can evaluate MTBF of electronic products without reference to physical tests. Finally, an EEC is applied to verify the effectiveness and accuracy of the method.

Optimal allocation of multi-state elements in a sliding window system with phased missions

Many real-world engineering systems such as aerospace systems, intelligent transportation systems and high-performance computing systems are designed to complete missions in multiple phases. These types of systems are known as phased-mission systems. Inspired by an industrial heating system, this research proposes a generalized linear sliding window system with phased missions. The proposed system consists of N nodes with M multi-state elements that are subject to degradation. The linear sliding window system fails if the cumulative performance of any r consecutive nodes is less than the pre-determined demand in any phase. The degradation process of each element is modeled by a continuous-time Markov chain. A novel reliability evaluation algorithm is proposed for the linear sliding window system with phased missions by extending the universal generating function technique. Furthermore, the optimal element allocation strategy is determined using the particle swarm optimization. The effectiveness of the proposed algorithm is confirmed by a set of numerical experiments.

An Accelerated Simulation Approach for Multistate System Mission Reliability and Success Probability under Complex Mission

The mission reliability and success probability estimation of multistate systems under complex mission conditions are studied. The reliability and success probability of multistate phased mission systems (MS-PMS) is difficult to use analytic modeling and solving. An estimation approach for mission reliability and success probability based on Monte Carlo simulation is established. By introducing accelerated sampling methods such as forced transition and failure biasing, the sampling efficiency of small-probability events is improved while ensuring unbiasedness. The ship’s propulsion and power systems are used as applications, and the effectiveness of the method is verified by a numerical example. Under complex missions, such as missions with different mission time and their combinations, and phased-missions, the proposed method is superior in small-probability event sampling than the crude simulation method. The calculation example also studies the influence of mission factors or system reliability and maintainability factors on system availability and mission success probability, and analyzes the relationship between different mission types and system availability and success probability.

Reconfigurable Framework for Environmentally Adaptive Resilience in Hybrid Space Systems

Due to ongoing innovations in both sensor technology and spacecraft autonomy, onboard space processing continues to be outpaced by the escalating computational demands required for next-generation missions. Commercial-off-the-shelf, hybrid system-on-chips, combining fixed-logic CPUs with reconfigurable-logic FPGAs, present numerous architectural advantages that address onboard computing challenges. However, commercial devices are highly susceptible to space radiation and require dependable computing strategies to mitigate radiation-induced single-event effects. Depending upon the mission, the dynamics of the near-Earth space-radiation environment expose spacecraft to radiation fluxes that can vary by several orders of magnitude. By adopting an adaptive approach to dependable computing, spacecraft computers can reconfigure system resources to efficiently accommodate changing environmental conditions to maximize system performance while satisfying availability constraints throughout the mission. In this article, we propose Hybrid, Adaptive, Reconfigurable Fault Tolerance (HARFT), a reconfigurable framework for environmentally adaptive resilience in hybrid space systems. Furthermore, we describe a methodology to model adaptive systems, represented as phased-mission systems using Markov chains, subject to the near-Earth space-radiation environment, using a combination of orbital perturbation, geomagnetic field, and single-event effect rate prediction tools. We apply this methodology to evaluate the HARFT architecture using various static and adaptive strategies for several orbital case studies and demonstrate the achievable performability gains.

A Combinatorial Modeling Method for Mission Reliability of Phased-Mission System With Phase Backups

In many real-world applications, phased-mission systems (PMSs) often have time redundancy in the form of that mission task failed in one phase can be re-executed in a backup phase with change of its configurations. In this case, a new kind of dependence among phase tasks is involved: the success or failure of a phase task may result in a different configuration of its backup phases, and the original task in the backup phase may be either cooperated with or replaced by the failed task. It is essential to consider such redundancy as a more precise result for system mission reliability evaluation is required. This article proposes a combinatorial method to model the mission reliability of PMS with such phase backups. A compressed event tree containing only failure branches is constructed, each branch describing a mission failure cause as an execution sequence of a set of dependent phase tasks. Each phase task is supported by a kind of phase configuration, whose failure causes are modeled by a fault tree illustrating the logic relations of component malfunctions. A binary decision diagram-based method is applied on this combinatorial model to calculate the mission failure probability, and always has a better performance than other alternative solutions. Examples are analyzed for illustration, and the results show the effectiveness of the proposed approach.

Multivalued decision diagram-based common cause failure analysis in phased-mission systems

Due to a shock or shared root cause, multiple system components may fail at the same time contributing significantly to the entire system failure. Such dependent component failures are referred to as common cause failures (CCFs). A rich body of research has been conducted for addressing effects of CCFs in reliability analysis of diverse types of systems. This paper first adapts multiple-valued decision diagrams (MDDs) for analyzing reliability of a non-repairable phased-mission system (PMS) subject to CCFs caused by external shocks. An explicit method and two implicit methods based on MDDs are proposed and compared. While the three MDD-based methods differ in terms of space and computation complexities, examples show that all these methods offer low computational complexity while addressing dynamics and dependencies caused by CCFs and phased operations.

Series phased-mission systems with heterogeneous warm standby components

In many industrial and aerospace systems, multiple and non-overlapping phases of operations must be performed consecutively, and each phase is performed by a different component. The mission succeeds only when each component successfully accomplishes the subtask assigned to its phase. Such systems are referred to as series phased-mission systems (SPMSs). To enhance the reliability of each phase and thus the entire mission, 1-out-of-N warm standby configuration is implemented for each phase component, with one element working and online when the phase is active and the rest of elements waiting in the standby mode. Different elements may have different failure behaviors and performances. An event transition-based method is proposed to evaluate the mission success probability (MSP) of the considered SPMS with heterogeneous warm standby components. The optimal element loading problem is then solved, which finds the load level of each element to maximize the MSP. The element sensitivity analysis is also performed to rank importance of each element by its impact on the MSP. Interactions between sensitivity analysis results and element activation sequences within each component are further studied through examples.

Reliability Analysis of Phased Mission Systems When Components Can Be Swapped Upon Failure

Abstract This paper proposes a new strategy to improve the reliability of phased mission systems (PMS), namely, by swapping of components. In the proposed strategy, when a component fails, it can be swapped by another one in the system which is still functioning. We consider both the options to swap components at any time and for swaps to be possible only at phase transitions. This paper also discusses the strategy of swapping components according to structure importance. The structure importance is used to measure the importance level of the components in contributing to system reliability. Then, when a component with high importance fails, it is swapped by another component with lower importance from the system which has not yet failed. The survival signature methodology is implemented to assess the reliability of PMS when there is a possibility of components swapping. In addition, we consider the cost effectiveness of component swapping through two models (time independent and time dependent) of penalty costs for PMS.

Ocena niezawodności naprawialnego systemu z misjami okresowymi za pomocą symulacji Monte Carlo w oparciu o modułowy model drzewa niezdatności z bramkami SEQ

Phased-mission system (PMS) is the system subject to multiple, consecutive and non-overlapping tasks. Much more complicated problems will be confronted when the PMS is repairable since the repairable system could perform the multi-phases mission with more diversity requirements. Besides, various maintenance strategies will directly influence the reliability analysis procedure. Most researches investigate those repairable PMSs that carry out the multi-phases mission with deterministic phase durations, and the mission fails once the system switches from up to down. In this case, one common maintenance strategy is that failed components are repairable as long as the system keeps in up state. However, many practical systems (e.g., construction machinery, agricultural machinery) may be involved in such multi-phases mission, which has uncertain phase durations but limited by a maximum mission time, within which failed components can be unconditional repaired, and the system can be restored from down state. Comparing with the former type of repairable PMS, the latter will also concern phase durations dependence, and both the system and components included have the state bidirectional transition. This paper makes new contributions to the reliability assessment of repairable PMSs by proposing a novel SEFT-MC method. Two types of repairable PMS mentioned above are considered. In our method, a specific sequence-enforcing fault tree (SEFT) is proposed to correctly depict failure logical relationships between the system and components included. In order to transfer the graphical fault tree (no matter its size and complexity) into a modular reliability model used in Monte Carlo (MC) simulation, an improved linear algebra representation (I-LAR) approach is introduced. Finally, a numerical example including two cases corresponding to the two types of repairable PMS is presented to validate the proposed method.

Reliability assessment of multi-state phased mission systems with common bus performance sharing considering transmission loss and performance storage

Multi-state phased mission systems (MS-PMSs) with common bus performance sharing widely exist in practical engineering, such as intelligent transmission systems and power supply systems. The challenges in assessing the reliability of these systems come from the dependence among phases and the complexity of multi-state behaviors of components. Besides, the performance transfer between different phases and the performance transfer with transmission loss have never been studied in such systems. To effectively address these problems, this paper proposes a reliability model for MS-PMSs with common bus performance sharing considering transmission loss and performance storage. First, a novel Markov model for multi-state components is established to solve the dependence problem among phases. Then, considering the performance sharing among subsystems within each phase through the common bus and the performance transferring between phases by an energy storage device, an iteration method combined with transmission loss function is adopted to establish the system reliability model, where the performance transmission loss is proportional to transmission distance and transmission performance. Furthermore, a universal generating function (UGF) based method is used for evaluating the system reliability. Finally, an analytical example and a case study of the IEEE-24 bus system are utilized to verify the availability of the proposed method.

Redundancy allocation problem of phased-mission system with non-exponential components and mixed redundancy strategy

The redundancy allocation problem (RAP) has been widely studied, which aims to identify the optimal design to achieve the best system indicators, such as system reliability or availability, under certain constraints. This problem has been studied well in the single phased mission systems. But many practical systems, like the manmade satellites or spacecraft, perform a series of tasks in multiple, consecutive, non-overlapping time durations (phases). For these multi-phased systems, dependencies among the phases need to be fully considered. To address this problem, the RAP of the phased mission systems (PMSs) is studied in this paper. Moreover, in order to improve system reliability as much as possible, the mixed redundancy strategy where both active and cold standby components can be simultaneously used in a subsystem is applied. The non-exponential components, which are more practical, are also considered. The Semi-Markov process (SMP) as well as a numerical approximation method are used to deal with the dynamic non-exponential components. Then, an improved Genetic algorithm (GA) is used to determine the types and quantities of active and cold-standby components in each subsystem to optimize the whole system. At last, the propulsion system in a spacecraft is optimized to illustrate the proposed method.

Reliability analysis of PMS with failure mechanism accumulation rules and a hierarchical method

Phased-mission systems (PMSs) are featured by the variety of system configuration, environment or load condition in different phases which can attribute to the alteration of system behavior. From the view of failure mechanism development, the damage accumulation of each mechanism in different phases should be considered for calculating the system reliability. A hierarchical model based on the binary decision diagram (BDD) is proposed to integrate four types of damage accumulation rules into PMS reliability modeling, including the homogeneous failure mechanisms with phase constant stress or combinational profile and inhomogeneous failure mechanisms which have the same damage effect in one phase or different phases. An extended BDD was combined into the traditional BDD model to integrate three different levels of the failure accumulation, including the failure mechanism level, the phase level and the mission level. As a study case, the reliability of a phased-mission control and drive system was modeled and analyzed. Results showed that the proposed method can effectively describe the changing behavior of a PMS and obtain the system reliability by calculating the model.

A Reliability Synthesis Method for Complicated Phased Mission Systems

Phased mission systems are very common in engineering practices. It has been a challenging task to effectively estimate the reliability of phased mission systems. In the paper, a new reliability synthesis method for phased mission systems is proposed for addressing the challenge. Based on the existed reliability block diagram, the hierarchical synthesis framework of the reliability can be built. The reliability synthesis algorithms for the same level and hierarchical level are respectively presented. Component reliability is the input of the reliability synthesis as the lowest level of the framework, which can be obtained by data-driven method or physics of failure method. The reliability estimation method for three lifetime distribution types is given. The effectiveness of the proposed method is testified and demonstrated by a complicated phased mission system.

Efficient Mincuts Identification for Phased-Mission Systems

Fault Tree is an important model for reliability and safety assessment. The analysis performed on a fault tree can be either qualitative or quantitative. Both types of analyses may involve identifying minimal cut sets (MCS) or mincuts, each of which is a minimal combination of basic events (component failures) whose occurrence causes the top event (system failure) to occur. Considerable research efforts have been expended in the identification of MCSs for single-phased systems and networks. However, only little work is available for phased-mission systems (PMSs) and the existing method involves a large number of redundant computations in the MCS identification for eliminating redundancies across generated cut sets. This article proposes an MCS identification method based on binary decision diagrams (BDD) generated from the PMS fault tree using the backward ordering. By examining cut sets encoded by the generated BDD model, we identify two kinds of redundancies (inclusion relation-based and intra-implication relation-based) that prevent a cut set from being an MCS. Correspondingly, two BDD operations are developed for eliminating these two kinds of redundancies, contributing to correct and efficient MCS identification. As demonstrated through experiments on a highly-reliable distributed computing infrastructure PMS, the proposed MCS identification method is more efficient than the existing method.

Model-Based Dependability Assessment of Phased-Mission Unmanned Aerial Vehicles

Assessment of non-functional reliability and safety requirements in the early development phases helps to prevent conceptually wrong decisions and, as a consequence, significantly reduces overall development costs. The application of model-based system analysis techniques demonstrates promising results for complex avionics systems, especially software-intensive Unmanned Aerial Vehicles (UAV). Such systems are commonly designed to accomplish a specific mission consisting of multiple mission phases. The concept of phased mission systems enables the specification of individual requirements for different phases. For instance, the reliability requirements or system specifications are different for UAV flights over an agricultural field and a highway. Therefore, modern analytical methods have to distinguish between different mission phases and enable the analysis of phased missions. In this paper, we propose a new model-based method that allows system engineers to assess a conceptional design specification of the UAV concerning the fulfillment of phase-specific requirements. The proposed approach exploits modern probabilistic model checking techniques for the quantification of several dependability metrics. The method supports the systematic analysis of system specifications that contain both structural and behavioral system properties. A case study demonstrates the feasibility of the proposed method.

Improved MDD Algorithm for Mission Reliability Estimation of an Escort Formation

An escort formation is a phased mission system of systems (PMSoS), which is composed of multiple ships with different functions. The configuration of the formation and success criteria for a mission may vary in different phases. Reliability estimation for PMSoS is complicated due to the strong phase dependence of multiple systems. This paper proposes an improved multiple-valued decision diagram (MDD) algorithm to perform reliability estimation of a nonrepairable escort formation. First, a phased fault tree is established to describe the failure mode of an escort formation throughout a mission, which is simplified according to the common failure basic mission (module) (CFBM). Bottom events are sorted based on the CFBM, and the case method is adopted to generate an MDD from the simplified fault tree model. On this basis, the MDD method is adopted to estimate mission reliability. The performance of the improved MDD method is compared with that of a binary decision diagram (BDD) method and a general MDD method. The results show that the improved MDD method can offer lower computational complexity as well as a simpler model construction over the BDD method and general MDD method. A case study of an escort formation PMSoS is analyzed to illustrate the proposed MDD method, and the sensitivity and composite importance measure (CIM) of each system are evaluated.

An extended object-oriented petri net model for mission reliability evaluation of phased-mission system with time redundancy

Phased-mission system (PMS) has been widely used as mission reliability model of many engineering mission-critical systems. In practical, PMS can have time redundancy both inner phase and between phases, i.e. the success of one phase may only require the system to keep operational for a given time interval instead of working throughout the phase. Besides, mission task failed in one phase can be re-executed in a later phase with change of its configurations.Based on the extended object-oriented Petri net (EOOPN), this paper proposes a general modeling procedure for mission reliability evaluation of PMS, which is capable of considering the time redundancy both inner phase and between phases in relatively intuitive way. The proposed approach is performed in system, phase and component levels. The system level depicts all possible executable paths of PMS via the token transition between different EOOPN subnets, including the re-execution arrangement of failed mission tasks. The phase level details the model structure of each EOOPN subnet, which addresses the system state change process and the minimal operational time requirement of a phase. The component level describes components state change during the mission. A discrete-event simulation is applied to the constructed EOOPNs for system mission reliability evaluation, and an example system under different time redundancy assumptions is analyzed to illustrate the effectiveness of this approach.