16. Mechanics-Based Maintenance Model

1994 ◽  
pp. 16.1-16.11
Keyword(s):  
Maintenance Model

Meaning maintenance model: The development of theory and research challenges

2016 ◽  
Vol 24 (1) ◽  
pp. 101
Author(s):  
Shijiang ZUO ◽  
Niwen HUANG ◽  
Fang WANG ◽  
Pan CAI
Keyword(s):  
Research Challenges ◽  
Maintenance Model

Restoring meaning: Self-uncertainty increases subjective distance between the past and present self

2020 ◽  
Vol 48 (7) ◽  
pp. 1-9
Author(s):  
Qing Yang ◽  
Oscar Ybarra ◽  
Yufang Zhao ◽  
Xiting Huang
Keyword(s):  
College Students ◽  
Self Esteem ◽  
Appraisal Theory ◽  
Chinese College Students ◽  
The Past ◽  
Subjective Distance ◽  
Maintenance Model

Based on the meaning maintenance model and temporal self-appraisal theory, we conducted 2 experiments with Chinese college students to test how self-uncertainty salience affected the subjective distance between the past and present self. We manipulated uncertainty salience and asked participants to explicitly (Study 1) or implicitly (Study 2) indicate their subjective distance. Participants in both studies increased the subjective distance when uncertainty was made salient. In addition, this effect was moderated by dispositional self-esteem in Study 2, with participants with low self-esteem reporting greater subjective distance than did high self-esteem participants after uncertainty-salience priming. These findings suggest that the process of appraising the past self may help individuals deal with feelings of uncertainty about the present self.

Operation and approximation based on the history of failure modes recorded by SCADA system of Amougdoul Moroccan wind farm using FMECA maintenance model

2021 ◽  
pp. 0309524X2199245
Author(s):  
Kawtar Lamhour ◽  
Abdeslam Tizliouine
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Failure Modes ◽  
Wind Farms ◽  
Scada System ◽  
History Of ◽  
Criticality Analysis ◽  
Maintenance Model ◽  
Reliability And Availability ◽  
Critical Subsystems

The wind industry is trying to find tools to accurately predict and know the reliability and availability of newly installed wind turbines. Failure modes, effects and criticality analysis (FMECA) is a technique used to determine critical subsystems, causes and consequences of wind turbines. FMECA has been widely used by manufacturers of wind turbine assemblies to analyze, evaluate and prioritize potential/known failure modes. However, its actual implementation in wind farms has some limitations. This paper aims to determine the most critical subsystems, causes and consequences of the wind turbines of the Moroccan wind farm of Amougdoul during the years 2010–2019 by applying the maintenance model (FMECA), which is an analysis of failure modes, effects and criticality based on a history of failure modes occurred by the SCADA system and proposing solutions and recommendations.

scholarly journals Dynamic maintenance model for high average-utility pattern mining with deletion operation

2021 ◽  
Author(s):  
Jimmy Ming-Tai Wu ◽  
Qian Teng ◽  
Shahab Tayeb ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Pattern Mining ◽  
Computational Cost ◽  
Practical Applications ◽  
Itemset Mining ◽  
Dynamic Databases ◽  
Speed Up ◽  
Dynamic Maintenance ◽  
Average Utility ◽  
High Utility ◽  
Maintenance Model

AbstractThe high average-utility itemset mining (HAUIM) was established to provide a fair measure instead of genetic high-utility itemset mining (HUIM) for revealing the satisfied and interesting patterns. In practical applications, the database is dynamically changed when insertion/deletion operations are performed on databases. Several works were designed to handle the insertion process but fewer studies focused on processing the deletion process for knowledge maintenance. In this paper, we then develop a PRE-HAUI-DEL algorithm that utilizes the pre-large concept on HAUIM for handling transaction deletion in the dynamic databases. The pre-large concept is served as the buffer on HAUIM that reduces the number of database scans while the database is updated particularly in transaction deletion. Two upper-bound values are also established here to reduce the unpromising candidates early which can speed up the computational cost. From the experimental results, the designed PRE-HAUI-DEL algorithm is well performed compared to the Apriori-like model in terms of runtime, memory, and scalability in dynamic databases.

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