Relationships between documentation and maintenance activities

2002 ◽  
Author(s):  
G. Visaggio
Keyword(s):  
Maintenance Activities

424. Heat Stress During Fuel Tank Entry and Maintenance Activities on U.S. Air Force and Commercial Aircraft

1999 ◽  
Author(s):  
G. Kinnes ◽  
P. Jensen ◽  
K. Mead ◽  
D. Watkins ◽  
L. Smith ◽  
...  
Keyword(s):  
Heat Stress ◽  
Air Force ◽  
Fuel Tank ◽  
Commercial Aircraft ◽  
Maintenance Activities

Assessing Pruning Knowledge towards Effective Tree Maintenance: A case study of four Local Authorities in Malaysia

2020 ◽  
Vol 5 (13) ◽  
pp. 223
Author(s):  
Norainiratna Badrulhisham ◽  
Noriah Othman
Keyword(s):  
Publishing House ◽  
Knowledge Level ◽  
Local Authorities ◽  
The Public ◽  
Risk Of Injury ◽  
International Publishing ◽  
Maintenance Activities ◽  
Tree Pruning ◽  
Open Access Article

Pruning is one of the most crucial tree maintenance activities which give an impact on the tree's health and structure. Besides, improper pruning will contribute to the risk of injury to property and the public. This study aims to assess pruning knowledge among four Local authorities in Malaysia. Results found that 69.3 percent of tree pruning workers have a Good pruning knowledge level. However, Topping, pruning types and pruning cut dimension shows the lowest mean percentage of the correct answer. The findings also show that there is a significant positive relationship between pruning knowledge and education level and frequency attending pruning courses.Keywords: Tree pruning; knowledge; sustainable practices; urban treeseISSN: 2398-4287 © 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.DOI: https://doi.org/10.21834/e-bpj.v5i13.2054 

scholarly journals STANDARDISING MAINTENANCE JOBS TO IMPROVE GROUPING DECISION MAKING

2021 ◽  
Vol 1 ◽  
pp. 2701-2710
Author(s):  
Julie Krogh Agergaard ◽  
Kristoffer Vandrup Sigsgaard ◽  
Niels Henrik Mortensen ◽  
Jingrui Ge ◽  
Kasper Barslund Hansen ◽  
...  
Keyword(s):  
Decision Making ◽  
Tacit Knowledge ◽  
True Variance ◽  
Complex Decision ◽  
Maintenance Decision ◽  
Maintenance Activities

AbstractMaintenance decision making is an important part of managing the costs, effectiveness and risk of maintenance. One way to improve maintenance efficiency without affecting the risk picture is to group maintenance jobs. Literature includes many examples of algorithms for the grouping of maintenance activities. However, the data is not always available, and with increasing plant complexity comes increasingly complex decision requirements, making it difficult to leave the decision making up to algorithms.This paper suggests a framework for the standardisation of maintenance data as an aid for maintenance experts to make decisions on maintenance grouping. The standardisation improves the basis for decisions, giving an overview of true variance within the available data. The goal of the framework is to make it simpler to apply tacit knowledge and make right decisions.Applying the framework in a case study showed that groups can be identified and reconfigured and potential savings easily estimated when maintenance jobs are standardised. The case study enabled an estimated 7%-9% saved on the number of hours spent on the investigated jobs.

Predicting Pavement Roughness Using Deep Learning Algorithms

2021 ◽  
pp. 036119812110237
Author(s):  
Qingwen Zhou ◽  
Egemen Okte ◽  
Imad L. Al-Qadi
Keyword(s):  
Neural Network ◽  
Service Life ◽  
Pavement Performance ◽  
Series Data ◽  
Statistical Regression ◽  
Network Algorithm ◽  
Pavement Roughness ◽  
Impacting Factors ◽  
Neural Network Algorithm ◽  
Maintenance Activities

Transportation agencies should measure pavement performance to appropriately strategize road preservation, maintenance, and rehabilitation activities. The international roughness index (IRI), which is a means to quantify pavement roughness, is a primary performance indicator. Many attempts have been made to correlate pavement roughness to other pavement performance parameters. Most existing correlations, however, are based on traditional statistical regression, which requires a hypothesis for the data. In this study, a novel approach was developed to predict asphalt concrete (AC) pavement IRI, utilizing datasets extracted from the Long-Term Pavement Performance (LTPP) database. IRI prediction is categorized by two models: (i) IRI progression over the pavement’s service life without maintenance/rehabilitation and (ii) the drop in IRI after maintenance. The first model utilizes the recurrent neural network algorithm, which deals with time-series data. Therefore, historical traffic data, environmental information, and distress (rutting, fatigue cracking, and transverse cracking) measurements were extracted from the LTPP database. A long short-term memory network was used to solve the vanishing gradient problem. Finally, an optimal model was achieved by setting the sequence length to 2 years. The second model utilizes an artificial neural network algorithm to correlate the impacting factors to the IRI value after maintenance. The impacting factors include maintenance activities; initial (new construction), milled, and overlaid AC thicknesses; as well as IRI value before maintenance activities. Combining the two models allows for the prediction of IRI values over AC pavement’s service life.

scholarly journals Machine learning for anomaly detection and process phase classification to improve safety and maintenance activities

2020 ◽  
Vol 56 ◽  
pp. 117-132 ◽  
Author(s):  
Elena Quatrini ◽  
Francesco Costantino ◽  
Giulio Di Gravio ◽  
Riccardo Patriarca
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Phase Classification ◽  
Maintenance Activities

scholarly journals Dataset of metaheuristics for the flow shop scheduling problem with maintenance activities integrated

Data in Brief ◽  
2021 ◽  
Vol 36 ◽  
pp. 106985 ◽  
Author(s):  
Antonella Branda ◽  
Davide Castellano ◽  
Guido Guizzi ◽  
Valentina Popolo
Keyword(s):  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Maintenance Activities
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