Maintenance Model of Digital Avionics

Raza, Ahmed

doi:10.3390/aerospace5020038

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…The effectiveness of complex technical systems is heavily reliant on diagnostic quality. Suffice it to point out, for example, that an error in diagnosing the condition of some aviation systems can lead to significant economic losses and tragic consequences [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty Quantification of Imperfect Diagnostics

Ulansky

Raza

2023

Aerospace

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The operable state of a system is maintained during operation, which requires knowledge of the system’s state. Technical diagnostics, as a process of accurately obtaining information about the system state, becomes a crucial stage in the life cycle of any system. The study deals with the relevant problem of uncertainty quantification of imperfect diagnostics. We considered the most general case when the object of diagnostics, the diagnostic tool, and the human operator can each be in one of the many states. The concept of a diagnostic error is introduced, in which the object of diagnostics is in one of many states but is erroneously identified as being in any other state. We derived the generalized formulas for the probability of a diagnostic error, the probability of correct diagnosis, and the total probability of a diagnostic error. The proposed generalized formulas make it possible to determine the probabilistic indicators of diagnosis uncertainty for any structures of diagnostics systems and any types of failures of the diagnostic tool and human operator. We demonstrated the theoretical material by computing the probabilistic indicators of diagnosis uncertainty for an aircraft VHF communication system and fatigue cracks in the aircraft wings.

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Section: Introductionmentioning

confidence: 99%

“…Analytical models are significantly simpler and less expensive to use. (3). The use of the well-known F1 score measure is also impractical to employ for assessing diagnostic trustworthiness for the following reasons.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification of Imperfect Diagnostics

Ulansky

Raza

2023

Aerospace

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“…As is well known (Raza, 2018;Raza & Ulansky, 2020), any two or three-level maintenance options must check the operability of the dismantled LRU or LRM. We illustrate this statement by the diagram presented in Figure 1 for two-level maintenance, including O-and I-level.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Health Monitoring Trustworthiness of Avionics Systems

Ulansky

Machalin

Terentyeva

2021

IJPHM

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The article provides a methodology for assessing the trustworthiness of health monitoring the dismounted avionics systems with automated test equipment (ATE). The indicators include the probabilities of false-positive, false-negative, true-positive, and true-negative. For the first time, we introduced into consideration the instability of the source of stimulus signal (SSS), the random and systematic component of the measuring channel error, and the reliability characteristics of the systems themselves. We consider a specific case of an exponential distribution of permanent failures and intermittent faults and derive formulas for calculating the trustworthiness indicators. Numerical calculations illustrate how the probabilities of correct and incorrect decisions depend on accuracy parameters. We show that the probabilities of false-positive and false-negative increase much faster than the probabilities of true-positive and true-negative decrease when the standard deviation of stimulus signal increases. For a Very High-Frequency Omni-Directional Range (VOR) receiver, we demonstrate that even with a zero random error generated by the source of the stimulus signal, the probabilities of false-positive and false-negative are different from zero.

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Likelihood‐based inference for a frailty‐copula model based on competing risks failure time data

Wang

Emura

Lo³

et al. 2020

Quality & Reliability Eng

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A competing risks phenomenon arises in industrial life tests, where multiple types of failure determine the working duration of a unit. To model dependence among marginal failure times, copula models and frailty models have been developed for competing risks failure time data. In this paper, we propose a frailty‐copula model, which is a hybrid model including both a frailty term (for heterogeneity among units) and a copula function (for dependence between failure times). We focus on models that are useful to investigate the reliability of marginal failure times that are Weibull distributed. Furthermore, we develop likelihood‐based inference methods based on competing risks data, including accelerated failure time models. We also develop a model‐diagnostic procedure to assess the adequacy of the proposed model to a given dataset. Simulations are conducted to demonstrate the operational performance of the proposed methods, and a real dataset is analyzed for illustration. We make an R package “gammaGumbel” such that users can apply the suggested statistical methods to their data.

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Maintenance Model of Digital Avionics

Cited by 5 publications

References 24 publications

Uncertainty Quantification of Imperfect Diagnostics

Uncertainty Quantification of Imperfect Diagnostics

Assessment of Health Monitoring Trustworthiness of Avionics Systems

Likelihood‐based inference for a frailty‐copula model based on competing risks failure time data

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