Phased-mission Systems Research Articles

Reliability modeling of multi-state phased mission systems with random phase durations and dynamic combined phases

Random phase durations and dynamic combined phases challenge the application of existing reliability models in the reliability analysis of multistate-phased mission systems (MS-PMSs). To this end, this paper presents a new reliability modeling method for multi-state phased mission systems with random phase durations and dynamic combined phases. Initially, a multi-state multi-valued decision diagram-based (MMDD-based) reliability modeling method is created to efficiently map random phase durations and the dynamic combined phase nature of MS-PMSs into the reliability model. To solve the MMDD-based reliability model, a path probability evaluation method is subsequently constructed with the assistance of the Markov regenerative function. The effectiveness and the superior performance of the proposed MMDD-based reliability model and its solving algorithm are validated by the application to the reliability modeling and analysis of an attitude and orbit control system with multiple modes. Overall, this paper provides the reliability sector with a new reliability model and its solving algorithm to enhance the reliability and safety of multi-state phased mission systems.

An integrated method of extended STPA and BN for safety assessment of man-machine phased-mission system

Man-Machine Phased-Mission System (MMPMS) usually demands the cooperation of operators with different responsibilities and machines to accomplish multi-phase missions. Its machine configuration and human organization structure may change across phases, and phase dependencies of machine failures and human errors may exist. In current studies, the safety of man-machine system is usually analyzed qualitatively by System Theoretic Process Analysis (STPA) and assessed quantitatively by the integration of STPA with Bayesian Networks (BN). These studies only focus on single-phase systems and conduct single-phase BN while cannot address the features of MMPMS. In this paper, a qualitative analysis and quantitative assessment method for phase dependencies is proposed and integrated into the method that combines STPA and BN. Firstly, four types of phase dependencies in MMPMS are identified. Secondly, new mapping rules for phase dependencies are proposed to integrate single-phase BN into a multi-phase BN. Thirdly, the quantitative assessment method for phase dependencies considering the effects of human organization structure changes are proposed to quantify the parameters of multi-phase BN. Fourthly, the safety of MMPMS can be assessed through multi-phase BN. Finally, an Unmanned Aerial Vehicle system with three-phase missions is presented as a case study to demonstrate the effectiveness of the proposed method.

Efficient reliability analysis of generalized [formula omitted]-out-of-[formula omitted] phased-mission systems

A k-out-of-n phased-mission system (PMS) is a PMS where the system structure is k-out-of-n: G in each phase. This paper investigates k-out-of-n PMSs with phase-K-out-of-N requirement, where the entire mission is successful if at least K out of the N phases achieve success. Such system is referred to as a generalized k-out-of-n PMS (k/n-GPMS). The k/n-GPMSs are prevalent in applications such as satellites, unmanned aerial vehicles (UAVs), wireless sensor networks and so on. In this paper, a novel method based on multi-valued decision diagram (MDD) is proposed to analyze the reliability of k/n-GPMSs, where the number of available components n, the required number of components k, and the components failure behaviors in different phases may vary. Distinguishing from the traditional phase-by-phase MDD generation method, the proposed method considers the behavior of all phases simultaneously and generates only one MDD model in a top-down manner. To illustrate the application of the proposed method, the reliability and the sensitivity of a four UAVs system which conducts supplies delivery mission is analyzed. The complexity analysis is performed. The correctness and efficiency are verified and demonstrated by several case studies. The proposed method is also compared with Monte Carlo simulation method.

Resilience-oriented adaptive predictive maintenance optimization for continuous process manufacturing systems considering mission profile variation

Continuous process manufacturing systems (CPMSs) are typical phased mission systems that require high standards of operational stability, reliability, and safety. With the variation of production mission profiles, CPMSs are required to run in diverse modes or conditions in different phases; therefore, to meet these standards, maintenance decisions applied to CPMSs should be adapted to such variations. Considering that the concept of “resilience” provides a systematic solution to evaluate system adaptability via “disruption absorption” and “recoverability,” this paper proposes a CPMS resilience evaluation model and utilizes it as guidance for the optimization of CPMS predictive maintenance (PdM). The proposed method consists of the following steps: (1) applying a customized Seasonal Trend Decomposition model to predict the future trend of production mission profile variations, (2) assessing the production mission accomplishment capability of CPMS based on a Gamma process model of equipment performance degradation, (3) using disruption response ratio to evaluate CPMS resilience based on mission accomplishment capability, and (4) proposing a Simulated Annealing Q-Learning algorithm for adaptive PdM optimization, which keeps resilience above a threshold level while minimizing maintenance costs. The applicability and effectiveness of the proposed method are validated by an industrial case study of a nuclear fuel rod shielding component CPMS.

A modular model for reliability analysis model of PMS with multiple K/N subsystems and mixed shocks

AbstractIn this article, a modular model is proposed for the reliability modeling of phased mission systems (PMSs) with complicated system behaviors. By the modular method, the system is divided into several levels: system, subsystem, and components. At the component level, the shock effect, including self‐degradation, additional wear and damage caused by shocks, is considered and the component reliability is evaluated. Then, the reliability modeling method of the K/N system consisting of multiple K/N subsystems is proposed, by a mathematics reasoning method. The correctness of the proposed method is also verified by a Monte Carlo (MC) simulation procedure. At last, the modular method is applied at the system level, and the reliability of a practical engineering case, the attitude and orbit control system (AOCS), is evaluated for illustration. Meanwhile, the parameter sensitivity analysis is also carried out for implementation.

Reliability Analysis for Phased-Mission System of Supply Chain based on BDD

Reliability Analysis for Phased-Mission System of Supply Chain based on BDD

Reliability evaluation of phased-mission systems exposed to random shocks

The paper aims to examine the reliability analysis problem of phased-mission systems that execute multiple consecutive non-overlapping mission phases in a random shock environment. Each phase is carried out by several subsystems which consist of non-repairable components connected in different topology configurations. The random shocks, modeled by a Poisson process, significantly affect the failure rate of components within the dynamic subsystems. The system configuration, success criteria, and component failure behavior may differ from phase to phase. The paper presents technical methodologies to evaluate the mission success probabilities of subsystems in various configurations and proposes a modularization method to compute the reliability of phased-mission systems by combining the mission success probabilities of all subsystems. Finally, a numerical example is provided to illustrate the proposed model and method.

Optimizing dynamic performance of phased-mission systems with a common bus and warm standby elements

Optimizing dynamic performance of phased-mission systems with a common bus and warm standby elements

Reliability modeling and optimization of K/N phased mission system with backup missions and global redundancy strategy

AbstractThe mission success probability (MSP) is a critical indicator for phased mission systems (PMSs). In the modern aerospace industry, redundancy techniques, including component/phase redundancy, are commonly seen to increase the MSP of the whole system. These component/phase redundancies make the reliability analysis more complex. Meanwhile, one or more components are required for normal working for different subsystems, called the K/N structure. In this article, a Markov‐process method is proposed for PMS with K/N subsystems and different redundancy strategies. Then, a universal system optimization model is proposed to optimize system structure and redundancy strategies for all subsystems at the same time. Then, an improved genetic algorithm (GA) is used to resolve the optimization problem. At last, a propulsion system is used as an engineering case, showing the proposed binary decision diagram‐based method.

Phase reduction for efficient reliability analysis of dynamic k-out-of-n phased mission systems

Phase reduction for efficient reliability analysis of dynamic k-out-of-n phased mission systems

Correction to “A copula‐based reliability model for phased mission systems with dependent components”

Correction to “A copula‐based reliability model for phased mission systems with dependent components”

Mission success probability optimizing of phased mission system balancing the phase backup and system risk: A novel GERT mechanism

Mission success probability optimizing of phased mission system balancing the phase backup and system risk: A novel GERT mechanism

Performance evaluation of non-repairable cyclic phased-mission systems using evidential reasoning rule and multi-valued decision diagrams

Phased-mission systems (PMS) often perform different missions that have multiple consecutive, non-overlapping phases. Many recent studies have focused on the reliability analysis for PMS. However, not much attention has been paid to the PMS performance evaluation and the phase cycle characteristic of PMS. In this paper, a performance evaluation method of non-repairable cyclic phased-mission systems (CPMS) is proposed. Considering the uncertainty caused by the change of phase duration and component performance degradation, the average duration of each phase and the fuzzy state probability vector of each component are obtained by the evidential reasoning rule (ER rule). Then, the multi-valued decision diagrams (MDD) manipulation rules suitable for cyclic phase conditions are proposed, and the component state composition path generation method based on cycle-end component states is developed. On the above basis, MDD models for CPMS (CPMS-MDD) in different states are established, and the system performance is evaluated by the continuous time Markov chains (CTMC). A phase duration adjustment strategy is further proposed based on the evaluation results. The effectiveness and correctness of the proposed method are verified by case studies on performance evaluation of satellite turntable system. Comparative study shows the concise and efficient of the proposed method.

Reliability analysis of repairable multistate phased mission systems with Markov approach based on states

PurposeThe purpose of this study is to find the reliability of the three-component three-phased mission system, which can be repaired by considering the exponential distribution for repair and failure rates in the transitions between the phases based on states with Markov approach. Also, multilevel-phased mission systems are calculated based on states for partially working states.Design/methodology/approachThe reliabilities of the repairable two-level and three-level three-component three-phased mission systems based on states are calculated with the Markov approach. The structure functions are obtained for each phase of the systems, and differential equations are created by the failure and repair of each working state component. These equations are solved using Laplace method.FindingsReliability values of two-level and three-level three-component three-phased systems with different failure, repair, and time intervals are calculated and compared. The intermediate states that multilevel systems handle differently from two-level systems provide a better investigation of the systems. So, these repairable systems offer transparent information in complex systems like transportation and energy, ensuring appropriate timing and cost for repair operations.Originality/valueThis study is original in terms of calculating the reliability of the repairable phased mission system based on the states using Markov method. It is also important in calculating the reliability of the repairable multilevel phased mission system based on states and making reliability comparisons according to different repair and failure rates, equal and different time intervals.

Mission performance analysis of phased-mission systems with cross-phase competing failures

Phased-mission systems (PMSs) with sophisticated failure modes could behave diverse mission performance levels. The non-negligible competing failures, resulting from complicated components’ failure patterns, have posed a great challenge for the mission performance analysis of PMSs. Even though many methods have been developed to facilitate reliability assessment of PMSs with competing failures in the past few decades, the mission performance analysis of PMSs with competing failures, varying from phase to phase, is still in difficulty. This feature is defined as the cross-phase competing failure, characterized by a two-by-two crossover combination of three or more failure modes in distinct mission phases. In this study, mission performance analysis of PMSs with cross-phase competing failures is conducted. By the divide-and-conquer strategy, the conditional probability of paths, resulting from the phase-dependent effects of components, is computed via an iterative method. Based on the phase equivalent binary decision diagrams, the mission performance analysis of PMSs with cross-phase competing failures is conducted. The probability distribution of mission performance levels, together with the system reliability, is assessed phase-by-phase based on the lifetime distributions of components and mission durations. A distributed multi-sensor multi-target tracking system, along with a set of comparative studies, is given to demonstrate the effectiveness and applicability of the proposed method. The results show that the proposed approach to mission performance analysis of PMSs is more computationally efficient than the existing combinational models.

Reliability analysis of a BWR plant system at startup stage - analysis by the GO-FLOW methodology with consideration of loop structures and phased mission problem -

A reliability analysis of a boiling water reactor (BWR) system at startup stage has been performed. This paper is a useful attempt at a reliability analysis of a phased mission system (PMS) with loop structures in a real engineering system. The system startup schedule has been set based on typical BWR system. During startup, phased mission problem occurs, and loop structures are formed in the system. The solution of Boolean expression for a reliability of loop structure has been explained. Reliability analysis has been performed by the GO-FLOW methodology; a brief explanation of the GO-FLOW has been given. The system has been modeled on a GO-FLOW chart. The loop structured parts have been modeled corresponding to the solution of the Boolean equations. The chart suggests a way to model logical loop relations in a diagram without loop structure. The PMS of a BWR system has been also modeled on the same GO-FLOW chart by using the phased mission operator. An exact analytical approach for loop structured system has been selected and both the logic of loop structures and the logic of PMS have been modeled on the same GO-FLOW chart. This allows PMS with loop structures to be easily solved. The mission success probabilities of the system have been obtained in a single computation by a single GO-FLOW chart. The proposed modeling method for logical loop structures and phased mission problem is useful in evaluating reliability and/or availability of large complex engineering systems.

Reliability analysis of cold-standby phased-mission system based on GO-FLOW methodology and the universal generating function

Reliability analysis of cold-standby phased-mission system based on GO-FLOW methodology and the universal generating function

A copula‐based reliability model for phased mission systems with dependent components

AbstractRecently, researches on the reliability analysis of phased mission systems (PMSs) are very popular. And most of the existing research on PMSs assumed that all the components are statistically independent on each other, which is not meet the engineering reality. The system structure changes from phase to phase in the PMSs, so the dependency among components may also change in different phases. Meanwhile, the well‐known phase dependency is also existing in the system reliability modeling. To model the varying dependency among components and phase dependency at the same time, a copula‐based method is proposed for the reliability analysis of PMS with correlated components. Firstly, the system reliability of multi‐state PMS with dependent components is evaluated by different copula functions. Then, multiple indicators are used to select the optimal copula functions for different phases. And an engineering case is illustrated to show the proposed copula‐based method.

Reliability analysis of dynamic voting phased-mission systems

Reliability analysis of dynamic voting phased-mission systems

Parameteric Estimation for Reliability Function of Phased Mission Systems with Series Network

In this paper, we consider a phased mission system consisting of [Formula: see text] phases (subsystems) wherein the control flows sequentially. The mission is successful if tasks associated with every phase are successful. Density and reliability functions of the lifetime of such a system are worked out when the lifetimes of components of the subsystems follow exponential distribution. It is assumed that components in different subsystems are in series configuration and function independently. The subsystems are assumed to operate independently. A simulation study is conducted to find bias of the estimates of the parameters and the reliability function. The Root Mean Square Error (RMSE) of the estimates is also calculated.