Reliability modeling for balanced systems considering mission abort policies

For safety-critical systems such as aircrafts and submarines, mission abort is commonly deployed to enhance system survivability at the cost of reducing mission success probability. In addition to mission abort, protective device can also mitigate the failure risk of safety-critical systems by reducing the magnitude of external shocks. Considering the effect of protective device on system failure behavior, this article proposes a condition-based mission abort policy where a mission is terminated and rescue procedure starts immediately if the state of system is worse than a control limit. Based on the developed mission abort policy, mission reliability and system survivability are evaluated to analyze the risk of mission failure and system failure. The optimal mission abort threshold balancing the tradeoff between mission reliability and system survivability is investigated. Furthermore, the joint optimization of mission abort and protective device selection policies is explored by simultaneously optimizing the defensive factor and abort threshold. A numerical example on a hydraulic system is presented to illustrate the applicability of the proposedpolicies.

Phased-mission systems are widely utilized in aerospace, defense fields, and production processes. These systems are characterized by missions consisting of multiple consecutive phases, each with distinct objectives and operating conditions. Due to complex operational environments and internal degradation, such systems face high failure risks, potentially re- sulting in irreversible economic losses and safety hazards. Therefore, designing a rational mission abort policy during mission execution to balance mission reliability and system survivability is of significant practical importance. This paper addresses the specific charac- teristics of phased-mission systems and proposes a mission abort policy based on the degra- dation process. A multi-phase Wiener process with phase-specific parameters is employed to depict system degradation behavior in different phases. A dynamic decision-making frame- work is developed to determine whether to continue or abort the mission by minimizing the expected total cost. A dynamic programing algorithm is used to find the optimal mission abort policy. The structural properties of the optimal policy are examined. The mission success probability is derived recursively using a matrix method. The practical value of the proposed model is validated through numerical analysis and comparative studies.

Optimal mission abort policy for systems subject to random shocks based on virtual age process

The balanced system often performs key missions in many fields, such as aerospace, new energy storage, and the military. However, the failure of the system can cause major losses. Therefore, the mission needs to be aborted when the risk of system failure is higher. In this paper, the optimal mission abort policy for a k‐out‐of‐n: F balanced system is studied. The system with a restricted mechanism consists of m sectors and there are n components in each sector, in which the components fail due to two situations: internal failure or external shocks. If the number of working components in m sectors is the same, the system is balanced. If one component fails, the system will force down or resume a component with a probability to keep balance. The model of the mentioned system is established, and the Markov process imbedding approach is used for deriving the reliability indices. Two optimization models are established according to two competitive abort criteria. Finally, an unmanned aerial vehicle performing a mission is discussed to illustrate the proposed mission abort policy.

Multi-attempt missions with multiple rescue options

Optimal loading of repairable system with perfect product storage

Influence of storage on mission success probability of m-out-of-n standby systems with reusable elements

Optimal mission abort and selective replacement policies for multi-state systems

The saddlepoint approximation (SA) can directly estimate the probability distribution of linear performance function in non-normal variables space. Based on the property of SA, three SA based methods are developed for the structural system reliability analysis. The first method is SA based reliability bounds theory (RBT), in which SA is employed to estimate failure probability and equivalent normal reliability index for each failure mode firstly, and then RBT is employed to obtain the upper and the lower bounds of system failure probability. The second method is SA based Nataf approximation, in which SA is used to estimate the probability density function (PDF) and cumulative distribution function (CDF) for the approximately linearized performance function of each failure mode. After the PDF of each failure mode and the correlation coefficients among approximately linearized performance functions are estimated, Nataf distribution is employed to approximate the joint PDF of multiple structural system performance functions, and then the system failure probability can be estimated directly by numerical simulation using the joint PDF. The third method is SA based line sampling (LS). The standardization transformation is needed to eliminate the dimensions of variables firstly in this case. Then LS method can express the system failure probability as an arithmetic average of a set of failure probabilities of the linear performance functions, and the probabilities of the linear performance functions can be estimated by the SA in the non-normal variables space. By comparing basic concepts, implementations and results of illustrations, the following conclusions can be drawn: (1) The first method can only obtain the bounds of system failure probability and it is only acceptable for the linear limit state function; (2) the second method can give the estimation of system failure probability, and its error mostly results from the approximation of Nataf distribution for the joint PDF of the structural system performance functions and the linearization of the performance functions; (3) the SA based LS method can obtain the estimator of system failure probability, which converges to the actual value along with the increase of sample size. The SA based LS method considers the influence of nonlinearity of limit state function on the failure probability, and it is acceptable for the structural system both with a single failure mode and with multiple failure modes, therefore it has the widest applicability.

Optimal attempt scheduling and aborting in heterogenous system performing asynchronous multi-attempt mission

Masonry infills are usually treated as nonstructural elements in buildings, and their interaction with the bounding frame is often ignored in analysis and design of reinforced concrete structures. The main aim of this study is to develop a seismic fragility curves showing the probability of exceeding a damage limit state for a given structure type subjected to a seismic excitation. For the purpose of this study, three distinct buildings namely, seven-story, eleven-story and sixteen-story, with typical floor plan were proposed as the case study. Each building cases are explicitly modeled as a bare frame and HCB infilled model with varying percentage of infill configurations. All building models under the case study were analyzed using Seismo-Struct software to assess seismic vulnerabilities. Non-linear dynamic time history and pushover analysis were employed to generate fragility curves. 30 generated artificial accelerograms were employed in the nonlinear dynamic time history analysis. Accordingly, for developing a fragility curve, nonlinear dynamic analyses of 30 building models for each case are conducted and the maximum roof displacement (ID) for each ground motion is recorded. Results of the study showed that bare frame has a highest probability of failure and building models with a larger percentage of infill configurations have lesser failure probability than slightly infilled building models. Basically these infills have significant contribution in arresting large lateral deflections and results in lower and most tolerable story displacements under excited earthquake motion and eventually reducing the structure’s probability of failure at life safety and collapse prevention limit states

When a unit should provide reliability at mission time, the mission success probability or failure probability becomes an important constraint of planing inspection policies. From this viewpoint, this paper considers the optimization problems of periodic and sequential inspection policies with mission failure probabilities. In this paper, the mission time is also considered as a renewal point for inspection polices, but the failure cost occurred at mission time is higher than the total cost of inspection and failure detection. We give an upper limit of the failure probability at mission time, i.e., mission failure probability, to minimize the total expected cost of inspection policies. In addition, the extended models of inspection policies for an mission interval, a constant renewal time and the random mission times are given directly.

Introduction and Background Most engineering structures contain multiple failure modes or components in which the nonperformance of any of the events can lead to system failure. In addition, the different failure modes may be correlated due to common random variables between the events. In many cases the events are assumed independent and system failure is evaluated using unions and intersections of the events. However, neglecting correlation between events may lead to grossly over predicting the probability of failure. An upper and lower bound can be determined for the probability of system failure using first- and second-order techniques. While there is some contradiction in the literature, a complete treatment of first-order bounds is made in this research. The first- order bounds are identified for events with both positive and negative correlation. In practice the second-order bounds typically use a first-order approximation to the limit-state for each event and the intersection of the two events is estimated by the correlation between the events. For nonlinear limit-states, the bounds may not actually include the true solution based on the error of the event probabilities of failure or the predicted intersection of the events. Several numerical examples are used to evaluate the accuracy and effectiveness of

Chapter 4 - Review on mechanical system reliability models

Online testing vehicle is required for 3D TSV pre-bond and post-bond testing due to high probability of TSV failures. It has become a challenge to deal with large sets of generated testing data with limited probing when transmitting the data out. In this paper, a lossless compressive-sensing based testing vehicle is developed for online testing of TSVs. By exploring sparsity of the testing data under constraint of failure bound of TSV, sparse-representation based encoding can be deployed by XOR and AND network on chip to deal with large volume of testing data. Experimental results (with benchmarks) have shown that 89.70% pre-bond data compression rate can be achieved under 0.5% probability of failures; and 88.18% post-bond data compression rate can be achieved with 5% probability of failures.