Sensitivity analysis of censoring data from component failure analysis and reliability evaluation for the aviation internet of things

Wang, Chao; Guo, Jun; Shen, Anwei

doi:10.1016/j.comcom.2020.04.003

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…As a result, IoT technology has been used in process-related studies in many sectors, with fewer resources and the need to make processes more efficient. Examples of these sectors can be given as aviation [22][23][24][25][26][27], agriculture [28][29][30], public [31][32][33][34], waste management [35], transport [36], health [37][38][39][40][41][42], education [43], military [44], energy [45], financial [46], examples of intelligent systems [47][48][49][50][51][52].…”

Section: Related Workmentioning

confidence: 99%

Internet of Things Supported Airport Boarding System and Evaluation with Fuzzy

Memika¹,

Polat²

2023

Intelligent Automation &Amp; Soft Computing

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The existing systems sustained with the investments made require more automation and digital transformation with the continuous advancement of technology. The aviation industry is a sector that is open to more automation and digital transformation, mainly because of the intense competition and the analysis of a large variety of data. The long duration of operations in current airline processes and some process flows cause customer dissatisfaction and cost increase. In this study, the boarding process, which is one of the operational processes of airline transportation and is open to improvement, was discussed. The classical boarding process has been redesigned using Internet of Things technology a model called Boarding 4.0 was created. With Boarding 4.0, it is aimed to design a process where passengers can take their time before boarding more efficiently. In the study, the sub-processes of the Boarding 4.0 model, other processes that the sub-processes interact with, their activities, and data exchange passenger movements during the activities are explained in detail. Compared to the classical boarding process and Boarding 4.0 with the fuzzy ahp technique, it has been shown that boarding 4.0 is more advantageous and passenger movement times can be reduced during boarding. As a result of the evaluation made with the fuzzy ahp, it was determined that boarding 4.0 is more advantageous than the classical boarding process. In addition, when the total time of the sub-activities in the boarding process is calculated, boarding activities for a passenger take 50 min with the classic boarding process and 20 min with Boarding 4.0. Thus, when Boarding 4.0 is used, the passenger gains 30 min. Furthermore, when the calculation is made concerning the airport's current capacity, two passengers are hosted with the classical boarding process, while five passengers are hosted with Boarding 4.0. This acquisition is significant for airports in terms of efficient use of resources.

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Section: Related Workmentioning

confidence: 99%

Internet of Things Supported Airport Boarding System and Evaluation with Fuzzy

Memika¹,

Polat²

2023

Intelligent Automation &Amp; Soft Computing

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“…The literature on maintenance is vast, including Reliability Engineering [4][5][6][7][8][9][10][11][12], maintenance policies and modelling [13][14][15][16][17][18][19][20], and optimal preventive maintenance intervals [21][22][23][24]. However, fewer papers address the problem of uncertainty in lifetime distribution [25][26][27][28], either by analysing several time-based maintenance policies having uncertainty in the parameters of the lifetime distribution [29] or using right-censored data [30], and adopt the Markovian approach of transition between states (operational, preventive, and corrective maintenance intervention) [31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Optimisation of Maintenance Policies Based on Right-Censored Failure Data Using a Semi-Markovian Approach

Sánchez-Herguedas

Nieto

Rodrigo-Muñoz

et al. 2022

Sensors

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This paper exposes the existing problems for optimal industrial preventive maintenance intervals when decisions are made with right-censored data obtained from a network of sensors or other sources. A methodology based on the use of the z transform and a semi-Markovian approach is presented to solve these problems and obtain a much more consistent mathematical solution. This methodology is applied to a real case study of the maintenance of large marine engines of vessels dedicated to coastal surveillance in Spain to illustrate its usefulness. It is shown that the use of right-censored failure data significantly decreases the value of the optimal preventive interval calculated by the model. In addition, that optimal preventive interval increases as we consider older failure data. In sum, applying the proposed methodology, the maintenance manager can modify the preventive maintenance interval, obtaining a noticeable economic improvement. The results obtained are relevant, regardless of the number of data considered, provided that data are available with a duration of at least 75% of the value of the preventive interval.

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“…Transportation means are essential in every day life and, therefore, it is crucial not only to detect failures but also to analyze their nature and the way to prevent them. Reliability studies associated with aviation, vehicles, rail defects and ships failures have been considered in a number of articles, see for example Zhang and Liu (2002); Zhang et al (2005); Karim (2008); Fang and Das (2005); Ivanov (2009); Decò et al (2012); Kim and Song (2018); Wang et al (2020) or Xiao- Yang et al (2021), just to cite a few. In particular, with the rapid development of the railway industry and high-speed lines, safety and reliability of train systems have attracted increasing attention.…”

Section: Introductionmentioning

confidence: 99%

A bivariate two-state Markov modulated Poisson process for failure modeling

Yera

Lillo

Nielsen

et al. 2021

Reliability Engineering & System Safety

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This dissertation is mainly motivated by the problem of statistical modeling via a specific point process, namely, the (Batch) Markov Modulated Poisson process. Point processes arise in a wide range of situations in physics, biology, engineering, or economics. In general, the occurrence of events is defined depending on the context, but in many areas are defined by the occurrence of an event at a specific time. Sometimes, in order to simplify the models and obtain closed form expressions for the quantities of interest, the exponentiality and/or independence of the inter-event times is assumed. However, the independence and exponentiability assumptions become unrealistic and restrictive in practice, and therefore, there is a need of more realistic models to fit the data.

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Sensitivity analysis of censoring data from component failure analysis and reliability evaluation for the aviation internet of things

Cited by 3 publications

References 19 publications

Internet of Things Supported Airport Boarding System and Evaluation with Fuzzy

Internet of Things Supported Airport Boarding System and Evaluation with Fuzzy

Optimisation of Maintenance Policies Based on Right-Censored Failure Data Using a Semi-Markovian Approach

A bivariate two-state Markov modulated Poisson process for failure modeling

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