•The paper presents a reliability-based learning function for adaptive Kriging surrogate models.•The modulating effect of the scatting geometry of random samples is considered.•The use of ...low-discrepancy samples and truncated sampling regions initiates efficient active-learning results.•Case studies have shown the proposed method has engineering applications.
Structural reliability analysis is typically evaluated based on a multivariate function that describes underlying failure mechanisms of a structural system. It is necessary for a surrogate model to mimic the true performance function as the brute-force Monte-Carlo simulation is computationally intensive for rare failure probabilities. To this end, the paper presents an effective active-learning based Kriging method for structural reliability analysis. The reliability-based expected improvement function (REIF) is first derived based on the folded-normal distribution. To account for the modulating effect of the joint probability density function of input random variables on the scattering geometry of candidate samples, an improvement of the REIF active-learning function, i.e., the REIF2 is further presented. Then, the low-discrepancy samples and adaptively truncated sampling regions are combined together to initiate efficient active-learning iterations. The truncated sampling region is directly related to a structural failure probability result, rather than subjectively fixed by an analyst. Numerical validity of the proposed active-learning functions in conjunction with adaptively truncated sampling region and low-discrepancy samples is demonstrated by several structural reliability examples in the literature.
•Autonomous ships will probably be monitored by humans from an onshore control center.•Task analysis of operators for collision avoidance was performed.•Interactions between operators and system for ...collision avoidance must the considered.•Humans can be an ultimate safety barrier for successful collision avoidance.•Risk assessment of autonomous ships need to consider the human-system interaction.
Numerous research and industry initiatives have increasingly aimed at developing maritime surface autonomous ships (MASS). Among the motivations for the use of MASS is the potential increase in safety when compared to traditional manned ships – particularly regarding human error. However, in spite of having less human intervention, MASS will rely on humans working on an onshore control center for their operation. There have been great advances in investigating the technical aspects of MASS operation, such as collision avoidance algorithms and detection sensors; nevertheless, possible human tasks and their deriving failures have rarely been addressed. This paper thus explores how humans can be a key factor for successful collision avoidance in future MASS operations. It presents a task analysis for collision avoidance through Hierarchical Task Analysis and making use of a cognitive model for categorizing the tasks. The failures in accomplishing these tasks are further analyzed, and human failure events are identified. The results provide valuable information for the design stage of the system; which must acknowledge the operators’ tasks to ensure a safe voyage. The conclusions of this paper are also a starting point for the implementation of a Human Reliability Analysis for this operation.
To improve the handling of micro-arc oxidation (MAO) process in this paper, the analysis of factors influencing the properties and operating characteristics of MAO-coatings and their classification ...on the basis of methods of reliability and quality theory (cause-effect diagram and the relationship diagram) were done. The main factors influencing the MAO process were revealed and recommendations on the use of obtained results in theoretical and experimental studies were done.
Abstract
Aiming at the problems of poor accuracy and low reliability in equipment evaluation and decision results of examination and repair (E&R) for the traditional power system (P-S) state, an E&R ...technology of intelligent substation (INSU) equipment state based on SNN is proposed. Firstly, based on the operational status of devices and systems in the intelligent substation (INSU), the E&R risks of devices and systems in INSU are evaluated through analysis of device availability. Then, by analyzing traditional CNN and improved SNN networks, a P-S state E&R model for power equipment detection based on SNN is proposed. Finally, a simulation experiment was conducted to compare and analyze the proposed SNN-based INSU device state E&R model with other methods. The results show that the proposed method has a lower system risk in the E&R process and is superior to other comparison methods.
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