Aircraft Maintenance Process Knowledge Modeling Method

Shiftwork disrupts the sleep-wake cycle, leading to sleepiness, fatigue, and performance impairment, with implications for occupational health and safety. Aircraft maintenance crews work a 24-hour shift rotation and sustain flight punctuality rate of job stress.If an error occurs during the aircraft maintenance process, this error may become a potential risk factor for flight safety.This paper focuses on optimal work shift scheduling to reduce the fatigue of aircraft maintenance crews. We model fatigue as a dynamic system, and the objective is to find the optimal shift schedules that minimize the maximum fatigue values. Various constraints such as holidays, company and government regulations are included in our model. This optimization problem is formulated as a mixed-integer program, in which the shift assignments are described by 0-1 variables. We take a sample aircraft maintenance crews schedule to demonstrate the proposed methods.

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Process knowledge modeling

Conceptual Modeling — ER '96 - Lecture Notes in Computer Science ◽

10.1007/bfb0019917 ◽

1996 ◽

pp. 89-104

Author(s):

Pierre Maret ◽

Jean-Marie Pinon

Keyword(s):

Knowledge Modeling ◽

Process Knowledge

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A Knowledge Modeling Method for Computer Graphics Design & Production Based on Ontology

MATEC Web of Conferences ◽

10.1051/matecconf/201710002041 ◽

2017 ◽

Vol 100 ◽

pp. 02041

Author(s):

Tong Chen ◽

Beibei Fan ◽

Dongping Chen ◽

Cong Li

Keyword(s):

Computer Graphics ◽

Modeling Method ◽

Knowledge Modeling ◽

Design Production ◽

Graphics Design

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Novel Causal Network Modeling Method Integrating Process Knowledge with Modified Transfer Entropy: A Case Study of Complex Chemical Processes

Industrial & Engineering Chemistry Research ◽

10.1021/acs.iecr.7b02700 ◽

2017 ◽

Vol 56 (48) ◽

pp. 14282-14289 ◽

Cited By ~ 6

Author(s):

Qun-Xiong Zhu ◽

Qian-Qian Meng ◽

Ping-Jiang Wang ◽

Yan-Lin He

Keyword(s):

Network Modeling ◽

Chemical Processes ◽

Modeling Method ◽

Transfer Entropy ◽

Causal Network ◽

Process Knowledge ◽

Complex Chemical ◽

Integrating Process

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Online Model-Based Remaining-Useful-Life Prognostics for Aircraft Cooling Units Using Time-Warping Degradation Clustering

Aerospace ◽

10.3390/aerospace8060168 ◽

2021 ◽

Vol 8 (6) ◽

pp. 168

Author(s):

Mihaela Mitici ◽

Ingeborg de Pater

Keyword(s):

Particle Filtering ◽

Remaining Useful Life ◽

Aircraft Maintenance ◽

Time Warping ◽

Model Based ◽

Degradation Models ◽

Useful Life ◽

Dynamic Time ◽

Specific Degradation ◽

Maintenance Process

Remaining-useful-life prognostics for aircraft components are central for efficient and robust aircraft maintenance. In this paper, we propose an end-to-end approach to obtain online, model-based remaining-useful-life prognostics by learning from clusters of components with similar degradation trends. Time-series degradation measurements are first clustered using dynamic time-warping. For each cluster, a degradation model and a corresponding failure threshold are proposed. These cluster-specific degradation models, together with a particle filtering algorithm, are further used to obtain online remaining-useful-life prognostics. As a case study, we consider the operational data of several cooling units originating from a fleet of aircraft. The cooling units are clustered based on their degradation trends and remaining-useful-life prognostics are obtained in an online manner. In general, this approach provides support for intelligent aircraft maintenance where the analysis of cluster-specific component degradation models is integrated into the predictive maintenance process.

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