Reliability of mission-based k-out-of-n systems with exponential phase durations and component lifetimes

This paper focuses on the reliability analysis of [Formula: see text]-out-of-[Formula: see text] systems which are designed to perform a given mission consisting of several distinct phases. As the phases of the mission change, the reliability characteristics of the system and the system structure vary accordingly. The phase transition probabilities, phase durations, and component failure rates are dependent on the number of operating components. Moreover, the system is allowed to have cold, warm, or hot standby components. We propose computationally tractable methods to compute the system reliability, mean time to failure, and long-run availability for such a general system structure. We also provide a numerical analysis to show the applicability and the empirical computational complexity of the proposed methods.

Reliability Analysis of Redundant Dynamic Positioning Control System With Human Factor Involved

Deepwater oil and gas explorations bring more safety and reliability problems for the dynamically positioned vessels. With the demands for the safety of vessel crew and onboard device increasing, the single control architecture of dynamic positioning (DP) system can not guarantee the long-time faultless operation for deeper waters, which calls for much more reliable control architectures, such as the Class 2 and Class 3 system, which can tolerate a single failure of system according to International Maritime Organization’s (IMO) DP classification. The reliability analysis of the main control station of DP Class 3 system is proposed from a general technical prospective. The fault transitions of the triple-redundant DP control system are modeled by Markov process. The effects of variation in component failure rates on the system reliability are investigated. Considering the DP operation involved a human-machine system, the DP operator factors are taken into account, and the human operation error failures together with technical failures are incorporated to the Markov process to predict the reliability of the DP control system.

Nested Multistate Design for Maximizing Probabilistic Performance in Persistent Observation Campaigns

The paper presents a nested multistate methodology for the design of mechanical systems (e.g., a fleet of vehicles) involved in extended campaigns of persistent surveillance. It uses multidisciplinary systems analysis and behavioral-Markov modeling to account for stochastic metrics such as reliability and availability across multiple levels of system performance. The effects of probabilistic failure states at the vehicle level are propagated to mission operations at the campaign level by nesting various layers of Markov and estimated-Markov models. A key attribute is that the designer can then quantify the impact of physical changes in the vehicle, even those physical changes not related to component failure rates, on the predicted chance of maintaining campaign operations above a particular success threshold. The methodology is demonstrated on the design of an unmanned aircraft for an ice surveillance mission requiring omnipresence over Antarctica. Probabilistic results are verified with Monte Carlo analysis and show that even aircraft design parameters not directly related to component failure rates have a significant impact on the number of aircraft lost and missions aborted over the course of the campaign.

Design of Long-Endurance Systems With Inherent Robustness to Partial Failures During Operations

This article presents an integrated multistate method for the early-phase design of inherently robust systems; namely, those capable, as a prima facie quality, of maintaining adequate performance in the face of probabilistic system events or failures. The methodology merges integrated multidisciplinary analysis techniques for system design with behavioral-Markov analysis methods used to define probabilistic metrics such as reliability and availability. The result is a multistate approach that concurrently manipulates design variables and component failure rates to better identify key features for an inherently robust system. This methodology is demonstrated on the design of a long-endurance unmanned aerial vehicle for a three-month ice surveillance mission over Antarctica. The vehicle is designed using the multistate methodology and then compared to a baseline design created for the best performance under nominal conditions. Results demonstrated an improvement of 10–11% in system availability over this period with minimal impacts on cost or performance.

Probability of failure on demand of safety systems: impact of partial test distribution

In accordance with the IEC 61508 functional safety standard, safety-related systems operating in a low demand mode need to be proof tested to reveal any ‘dangerous undetected failures’. Proof tests may be full (i.e. complete) or partial (i.e. incomplete), depending on their ability to detect all the system failures or only a part of them. Following a partial test, some failures may then be left latent until the full test, whereas after a full test (and overhaul), the system is restored to an as-good-as-new condition. A partial-test policy is defined by the efficiency of the partial tests, and the number and distribution (periodic or non-periodic) of the partial tests in the full test time interval. Non-approximate equations are introduced for probability of failure on demand (PFD) assessment of a Moo N architecture (i.e. k-out-of- n: G) systems subject to partial and full tests. Partial tests may occur at different time instants (periodic or not) until the full test. The time-dependent, average, and maximum system unavailability (PFD(t), PFDavg, and PFDmax) are investigated, and the impact of the partial test distribution on average and maximum system unavailability are analysed, according to system architecture, component failure rates, and partial test efficiency.