Helicopter maintenance error analysis: Beyond the third order of the HFACS-ME

In this paper a statistical analysis of a sample of 58 helicopter maintenance-induced safety occurrences is conducted to study helicopter accidents and incidents’ survivability and the severity distribution of such occurrences. Analysis is also carried out to identify helicopter main and sub-systems mostly exposed to maintenance errors and to determine various types of such errors. Expected inherent relations between rotorcraft components affected and types of associated maintenance errors are investigated. Human factors-based triggers of these accidents and severe incidents are explored. The concept of Specific Failures (SFs) that immediately precede each of such occurrences is introduced for more detailed representation of the last breached individual and organizational safety barriers. Root causes of these safety occurrences were then sought utilizing the Human Factors Analysis and Classification System-Maintenance Extension (HFACS-ME) taxonomy with a refined focus on its third order categories’ list. The rotorcraft characteristics influencing individuals and organizational behaviours within Maintenance, Repair and Overhaul organizations (MROs) are discussed in the light of the root cause investigation results.

Relevance to industry

The study of human reliability within helicopter maintenance industry is waited to emphasise the understanding of causes and propagation mechanisms of maintainers' errors and their consequences on the overall aviation safety. Previous works often investigated maintenance errors and their roles in promoting aviation accidents of fixed-wing aircraft; this research is investigating the case of rotorcraft.     

基于传动误差数据的齿轮误差检测方法与系统

齿轮传动误差影响齿轮传动精度和平稳性,传动误差与齿距和齿廓偏差等齿轮误差数据有直接关系.利用单面啮合测试和虚拟仪器原理,构建了传动误差检测系统,研究了传动误差与齿距与齿廓偏差数据的关系,找到一种将齿距与齿廓偏差计算出来的方法.经过试验分析,验证了本方法和系统的准确性.     

Applying Dynamic Training‐Subset Selection Methods Using Genetic Programming for Forecasting Implied Volatility

Volatility is a key variable in option pricing, trading, and hedging strategies. The purpose of this article is to improve the accuracy of forecasting implied volatility using an extension of genetic programming (GP) by means of dynamic training‐subset selection methods. These methods manipulate the training data in order to improve the out‐of‐sample patterns fitting. When applied with the static subset selection method using a single training data sample, GP could generate forecasting models, which are not adapted to some out‐of‐sample fitness cases. In order to improve the predictive accuracy of generated GP patterns, dynamic subset selection methods are introduced to the GP algorithm allowing a regular change of the training sample during evolution. Four dynamic training‐subset selection methods are proposed based on random, sequential, or adaptive subset selection. The latest approach uses an adaptive subset weight measuring the sample difficulty according to the fitness cases' errors. Using real data from S&P500 index options, these techniques are compared with the static subset selection method. Based on mean squared error total and percentage of non‐fitted observations, results show that the dynamic approach improves the forecasting performance of the generated GP models, especially those obtained from the adaptive‐random training‐subset selection method applied to the whole set of training samples.     

A sparse direction-of-arrival estimation algorithm for MIMO radar in the presence of gain-phase errors

In this paper, the problem of direction-of-arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar with gain-phase errors is addressed, by using a sparse DOA estimation algorithm with fourth-order cumulants (FOC) based error matrix estimation. Useful cumulants are designed and extracted to estimate the gain and the phase errors in the transmit array and the receive array, thus a reliable error matrix is obtained. Then the proposed algorithm reduces the gain-phase error matrix to a low dimensional one. Finally, with the updated gain-phase error matrix, the FOC-based reweighted sparse representation framework is introduced to achieve accurate DOA estimation. Thanks to the fourth-order cumulants based gain-phase error matrix estimation, and the reweighted sparse representation framework, the proposed algorithm performs well for both white and colored Gaussian noises, and provides higher angular resolution and better angle estimation performance than reduced-dimension MUSIC (RD-MUSIC), adaptive sparse representation (adaptive-SR) and ESPRIT-based algorithms. Simulation results verify the effectiveness and advantages of the proposed method.     

Fault accommodation for uncertain linear systems with measurement errors

This paper investigates the observer based fault accommodation (FA) problem for linear systems with state perturbations and measurement errors. A novel intermediate estimator is proposed to estimate the state and the faults simultaneously, where the effect of state perturbations and measurement errors can be effectively eliminated by fully exploiting the design freedom of some specified parameters. Based on the estimation, a fault tolerant control (FTC) scheme is synthesized. It is proved that the states of the resulting closed loop system are uniformly ultimately bounded with an explicit bound. Simulation examples verify the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.     

无线上行视频流的跨层容错传输方法

如何通过资源受限的移动通信终端提升无线上行视频流的抗误性能是亟待解决的重要问题。通过不同通信层次的联合调度,提出了一种跨层容错传输方案。移动通信终端的网络层代理首先利用容错包调度为视频流的延时约束帧集合提供重要性分类,随后该终端的链路层代理利用无线链路单元的优先级调度实现选择性重传。在调度延时与传输带宽限制下,跨层容错传输能够将突发错误转移到延时约束帧集合的低优先级视频数据中,从而在突发易错传输环境中实现了无线链路单元粒度的渐进式传输和平稳退化。     

Optimal Average Case Estimation in Hilbert Norms

In contrast to the worst case approach, the average case setting provides less conservative insight into the quality of estimation algorithms. In this paper we consider two local average case error measures of algorithms based on noisy information, in Hilbert norms in the problem element and information spaces. We define the optimal algorithm and provide formulas for its two local errors, which explicitly exhibit the influence of factors such as information, information (measurement) errors, norms in the considered spaces, a subset where approximations are allowed, and “unmodeled dynamics.” Based on the error expression, we formulate in algebraic language the problem of selecting the optimal approximating subspace. The solution is given along with the specific formula for the error, which depends on the eigenvalues of a certain matrix defined by information and norms under consideration. Date received: November 25, 1999. Date revised: May 30, 2000.     

The grammar of action and reversal errors in children's printing.

36 left-handed and 36 right-handed kindergarten children were asked to print after seeing a series of reversible letters and numbers presented individually on slides or flash cards. Regardless of handedness, both groups of Ss found the left–right orientation cues of the same letters and numbers difficult to recall. This challenges a proposal expressed in the "grammar of action" that claims that certain reversal errors in children's printing result from the inappropriate application of the horizontal motor rule, because although left- and right-handed children use this rule, they do so in different ways and therefore should have produced different errors. Another sample of 86 right-handed children from nursery school through Grade 1 was given the same printing task. Findings from this 2nd sample provide an additional challenge to the grammar of action by showing that reversals of the letter N, which are also thought to result from a misapplication of motor rules, decrease in frequency along with reversals of other letters and numbers, instead of increasing as predicted, during the developmental period when the motor rules that are said to be responsible for reversals of this particular letter become more strongly established. (21 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

On the self-erasing nature of errors of prediction.

82 female undergraduates were assigned to 1 of 4 experimental groups—predict-request, information-request, predict only, and request only—in which requested tasks involved writing a counterattitudinal essay or singing over the telephone. In 3 experiments, Ss overpredicted the degree to which their behavior would be socially desirable and these errors of prediction proved to be self-erasing. Having mispredicted a given behavior, Ss were likely to have these predictions confirmed in later behavior, indicating that prediction of a behavioral sequence evokes a specific cognitive representation of that sequence which is subsequently accessed. Results demonstrate the strong effects on behavior of engaging in prebehavioral cognitive work. (27 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)