Probability Of Mission Failure Research Articles

Chance-Constrained Multilayered Sampling-Based Path Planning for Temporal Logic-Based Missions

This article introduces a robust and safe path planning algorithm in order to satisfy mission requirements specified in linear temporal logic (LTL). When a path is planned to accomplish a mission, it is possible for a robot to fail to complete the mission or collide with obstacles due to noises and disturbances in the system. Hence, we need to find a robust path against possible disturbances. We introduce a robust path planning algorithm, which maximizes the probability of success in accomplishing a given mission by considering disturbances, while minimizing the moving distance of a robot. The proposed method can guarantee the safety of the planned trajectory by incorporating an LTL formula and chance constraints in a hierarchical manner. A high-level planner generates a discrete plan satisfying the mission requirements specified in LTL. A low-level planner builds a sampling-based rapidly exploring random tree search tree to minimize both the mission failure probability and the moving distance while guaranteeing the probability of collision with obstacles to be below a specified threshold. We have analyzed properties of the proposed algorithm theoretically and validated the robustness and safety of paths generated by the algorithm in simulation and experiments using a quadrotor.

Online decision making and path planning framework for safe operation of unmanned aerial vehicles in urban scenarios

As the potential for deploying low-flying unmanned aerial vehicles (UAVs) in urban spaces increases, ensuring their safe operations is becoming a major concern. Given the uncertainties in their operational environments caused by wind gusts, degraded state of health, and probability of collision with static and dynamic objects, it becomes imperative to develop online decision-making schemes to ensure safe flights. In this paper, we propose an online decision-making framework that takes into account the state of health of the UAV, the environmental conditions, and the obstacle map to assess the probability of mission failure and re-plan accordingly. The online re-planning strategy considers two situations: (1) updating the current trajectory to reduce the probability of collision; and (2) defining a new trajectory to find a new safe landing spot, if continued flight would result in risk values above a pre-specified threshold. The re-planning routine uses the differential evolution optimization method and takes into account the dynamics of the UAV and its components as well as the environmental wind conditions. The new trajectory generation routine combines probabilistic road-maps with B-spline smoothing to ensure a dynamically feasible trajectory. We demonstrate the effectiveness of our approach by running UAV flight simulation experiments in urban scenarios.

Periodic and sequential inspection policies with mission failure probabilities

AbstractWhen a unit should provide reliability at mission time, the mission success probability or failure probability becomes an important constraint to plan inspection policies. From this viewpoint, this paper considers the optimization problems of periodic and sequential inspection policies with mission failure probabilities. In this paper, a mission time is 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, that is, mission failure probability, to minimize the total expected costs and the expected cost rates of inspection policies. In addition, the extended models of inspection policies for an interval mission, possible random times, and operating interval are discussed.

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.

Probabilistic Assessment of the Failure Risk of the Europa Clipper Spacecraft due to Radiations.

The Europa mission approved in 2019 is still in the development phase. It is designed to conduct a detailed reconnaissance of that moon of Jupiter as it could possibly support life as we know it. This article is based on a top-down approach (mission → system → subsystems → components) to model the probability of mission failure. The focus here is on the case where the (uncertain) radiation load exceeds the (uncertain) capacity of critical subsystems of the spacecraft. The model is an illustrative quantification of the uncertainties about (1) the complex external radiation environment in repeated exposures, (2) the effectiveness of the shielding in different zones of the spacecraft, and (3) the components' capacities, by modeling all three as dynamic random variables. A simulation including a sensitivity analysis is used to obtain the failure probability of the whole mission in forty-five revolutions around Jupiter. This article illustrates how probabilistic risk analysis based on engineering models, test results and expert opinions can be used in the early stages of the design of space missions when uncertainties are large. It also describes the optimization of the spacecraft design, taking into account the decisionmakers' risk attitude and the mission resource constraints.

Active Mission Success Estimation through PHM-Informed Probabilistic Modelling


 
 
 Prognostics and Health Management (PHM) techniques have traditionally been used to analyze electrical and mechanical systems, but similar techniques can be adapted for less mechatronically-focused processes such as crewed space missions. By applying failure analysis techniques taken from PHM, the probability of success for missions can be calculated. Extensive work has been conducted to predict space mission failure, but many existing methods do not take full advantage of modern computing power and the potential for real-time calculation of mission failure probabilities. The Active Mission Success Estimation (AMSE) method is developed in this paper to track and calculate the probability of mission failure as the mission progresses, and is intentionally adaptable for shifting mission objectives and parameters. This form of mission modelling takes a broader view of the mission and objectives, and develops statistical probability models of success or failure for multiple possible choice combinations that is used to inform real-time decisions and maximize probability of mission success. A case study of a generalized crewed Mars mission that has turned into a survival scenario is considered where an astronaut has been left behind on the surface and must survive for an extended period of time before undertaking a long-distance journey to a new launch site for rescue and return to Earth. The AMSE method presented here aims to establish real-time probabilistic modeling of decision outcomes during an active mission and can be used to inform mission decisions.
 
 

High Reliability Permanent Magnet Brushless Motor Drive for Aircraft Application

Reliability is a fundamental requirement in aircraft safety-critical equipments. Its pursuing involves the adoption of protective design concepts such as fault-tolerant or redundant approaches, aiming to minimize mission failure probabilities. Multi-phase motor drives are gaining a growing interest to this extent, because they permit a boost in torque and power density, allowing the design of very compact high efficiency drives with intrinsic fault-tolerant capabilities. This paper presents a five-phase permanent magnet brushless motor drive developed for an aircraft flap actuator application. The motor is designed to satisfy the load specifications with one or two phases open or with a phase short circuited, while a failure in the rotor position sensors is remedied through a sensorless strategy. Design studies aiming to predict the faulty mode performance in case of different remedial strategies are presented. Experimental tests on the drive prototype are included, which confirm its capability to satisfy the planned degraded modes of operation.

Multiplatform phased mission reliability modelling for mission planning

Autonomous systems are being increasingly used in many areas. A significant example is unmanned aerial vehicles (UAVs), regularly being called upon to perform tasks in the military theatre. Autonomous systems can work alone or be called upon to work collaboratively towards common mission objectives. In this case it will be necessary to ensure that the decisions enable the progression of the platform objectives and also the overall mission objectives. The motivation behind the work presented in this paper is the need to be able to predict the failure probability of missions performed by a number of autonomous systems working together. Such mission prognoses can assist the mission planning process in autonomous systems when conditions change, with reconfiguration taking place if the probability of mission failure becomes unacceptably high. In a multiplatform phased mission a number of platforms perform their own phased mission that contributes to an overall mission objective. Presented in this paper is a methodology for calculating the phase failure probabilities of a multiplatform phased mission. These probabilities are then used to find the total mission failure probability. Prior to the mission the failure probabilities are used to decide if the original mission structure is acceptable. Once underway, failure probabilities, updated as circumstances change, are used to decide whether a mission should continue. Circumstances can change owing to failures on a platform, changing environmental conditions (weather), or the occurrence of unforeseen external events (emerging threats). This diagnostics information should be used to ensure that the updated failure probabilities calculated take into account the most up-to-date system information possible. Since the speed of decision making and the accuracy of the information used are essential, binary decision diagrams (BDDs) are utilized to form the basis of a fast, accurate quantification process.

Risk Assessment of Decommissioning Options Using Bayesian Networks

For complex and costly decommissioning activities, it is beneficial, if not necessary, that all relevant risks are identified and assessed on an overall basis, treating and assessing all risks within the same theoretical framework. Only then may the different options be consistently compared and the risks associated with decommissioning demonstrated and documented to the different parties of interest. The present paper suggests an approach for assessing the risks associated with the decommissioning of offshore facilities. The approach takes basis in a discrete point in time representation of the considered decommissioning options where important phases of the options are represented in terms of event scenarios. Using the possibilities of Bayesian Probabilistic Networks (BPN), the failure probabilities and risk events involved in the modeling of an option may then be analyzed for each phase and added up time-wise over the entire decommissioning process. The principles of BPNs are shortly described, and the proposed approach is illustrated by an example linking the operational and structural risks in connection with a re-float decommissioning option for a concrete offshore platform. It is shown how the sensitivity may be evaluated on the basis of the BPNs, thus providing a valuable framework to first improve the risk model in terms of the representation of important scenarios, then for deciding where to apply additional safety measures most effectively, and last but not least, to demonstrate and document the contributions to the mission failure probability.

Unavailability analysis of periodically tested standby components

General recursive models are developed for the unavailability and the mission-failure probability of standby equipment subject to periodic testing. The analytic approach allows general distributions for the failure times, test durations, and repair times. It accounts for start-up failures, standby failures, and failures during mission, as well as several categories of human error. Explicit expressions are derived for the unavailabilities and approximate optimal test-intervals for constant standby failure rate.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Reliability of Standby Equipment with Periodic Testing

The reliability of standby equipment subjected to periodic testing is studied. Analytic expressions have been derived for reliability measures, such as: mean accumulated operating time to failure, mean number of tests between two consecutive failures, mean time to failure following an emergency start-up, and probability of failing to complete an emergency mission of a specified duration. These results are useful for assessing the reliability of standby equipment such as combustion turbine units of an emergency power supply at a nuclear generating station or hospital. The mean accumulated operating time to failure of the standby equipment depends on the test duration, the starting failure probability, and the mean and variance of the times to failure of an identical equipment in the continuous operation mode. It is considerably less than the mean time to failure of an identical equipment in the continuous operation mode. This is primarily due to starting failures. Bounds of the mean accumulated operating time to failure of standby equipment can be obtained from knowledge of the mean number of tests between two consecutive failures, and test duration. The mission failure probability and the mean time to failure of the standby equipment following an emergency start-up strongly depend on the probability distribution of the times to failure of an identical equipment in continuous operation mode.