Analysis of Floodwater Level and Periodic Maintenance of Celeng River, Bantul

In order to improve the operation and maintenance (O&M) of offshore wind turbines, a new Petri net (PN)-based offshore wind turbine maintenance model is developed in this paper to simulate the O&M activities in an offshore wind farm. With the aid of the PN model developed, three new potential wind turbine maintenance strategies are studied. They are (1) carrying out periodic maintenance of the wind turbine components at different frequencies according to their specific reliability features; (2) conducting a full inspection of the entire wind turbine system following a major repair; and (3) equipping the wind turbine with a condition monitoring system (CMS) that has powerful fault detection capability. From the research results, it is found that periodic maintenance is essential, but in order to ensure that the turbine is operated economically, this maintenance needs to be carried out at an optimal frequency. Conducting a full inspection of the entire wind turbine system following a major repair enables efficient utilisation of the maintenance resources. If periodic maintenance is performed infrequently, this measure leads to less unexpected shutdowns, lower downtime, and lower maintenance costs. It has been shown that to install the wind turbine with a CMS is helpful to relieve the burden of periodic maintenance. Moreover, the higher the quality of the CMS, the more the downtime and maintenance costs can be reduced. However, the cost of the CMS needs to be considered, as a high cost may make the operation of the offshore wind turbine uneconomical.

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On a periodic maintenance problem

Operations Research Letters ◽

10.1016/0167-6377(83)90044-5 ◽

1983 ◽

Vol 2 (2) ◽

pp. 90-93 ◽

Cited By ~ 43

Author(s):

W.D Wei ◽

C.L Liu

Keyword(s):

Periodic Maintenance ◽

Maintenance Problem

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Automatic Boundary Extraction for Photovoltaic Plants Using the Deep Learning U-Net Model

Applied Sciences ◽

10.3390/app11146524 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6524

Author(s):

Andrés Pérez-González ◽

Álvaro Jaramillo-Duque ◽

Juan Bernardo Cano-Quintero

Keyword(s):

Research Work ◽

Solar Photovoltaic ◽

Boundary Extraction ◽

Pv Systems ◽

Periodic Maintenance ◽

Planning Algorithm ◽

Extraction Processes ◽

Inspection And Maintenance ◽

Photovoltaic Plants ◽

Path Planning Algorithm

Nowadays, the world is in a transition towards renewable energy solar being one of the most promising sources used today. However, Solar Photovoltaic (PV) systems present great challenges for their proper performance such as dirt and environmental conditions that may reduce the output energy of the PV plants. For this reason, inspection and periodic maintenance are essential to extend useful life. The use of unmanned aerial vehicles (UAV) for inspection and maintenance of PV plants favor a timely diagnosis. UAV path planning algorithm over a PV facility is required to better perform this task. Therefore, it is necessary to explore how to extract the boundary of PV facilities with some techniques. This research work focuses on an automatic boundary extraction method of PV plants from imagery using a deep neural network model with a U-net structure. The results obtained were evaluated by comparing them with other reported works. Additionally, to achieve the boundary extraction processes, the standard metrics Intersection over Union (IoU) and the Dice Coefficient (DC) were considered to make a better conclusion among all methods. The experimental results evaluated on the Amir dataset show that the proposed approach can significantly improve the boundary and segmentation performance in the test stage up to 90.42% and 91.42% as calculated by IoU and DC metrics, respectively. Furthermore, the training period was faster. Consequently, it is envisaged that the proposed U-Net model will be an advantage in remote sensing image segmentation.

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Mathematical Modeling and Discrete Firefly Algorithm to Optimize Scheduling Problem with Release Date, Sequence-Dependent Setup Time, and Periodic Maintenance

Mathematical Problems in Engineering ◽

10.1155/2019/8028759 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 3

Author(s):

Mustafa A. Qamhan ◽

Ammar A. Qamhan ◽

Ibrahim M. Al-Harkan ◽

Yousef A. Alotaibi

Keyword(s):

Firefly Algorithm ◽

Programming Model ◽

Setup Time ◽

Mixed Integer ◽

Release Date ◽

Sequence Dependent ◽

Sequence Dependent Setup Time ◽

Periodic Maintenance ◽

Discrete Firefly Algorithm ◽

Sequence Dependent Setup

An evolutionary discrete firefly algorithm (EDFA) is presented herein to solve a real-world manufacturing system problem of scheduling a set of jobs on a single machine subject to nonzero release date, sequence-dependent setup time, and periodic maintenance with the objective of minimizing the maximum completion time “makespan.” To evaluate the performance of the proposed EDFA, a new mixed-integer linear programming model is also proposed for small-sized instances. Furthermore, the parameters of the EDFA are regulated using full factorial analysis. Finally, numerical experiments are performed to demonstrate the efficiency and capability of the EDFA in solving the abovementioned problem.

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Damage Assessment of Structures - An Air Force Office of Scientific Research Structural Mechanics Perspective

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.347.69 ◽

2007 ◽

Vol 347 ◽

pp. 69-74 ◽

Cited By ~ 6

Author(s):

Victor Giurgiutiu

Keyword(s):

Structural Health Monitoring ◽

Air Force ◽

Health Monitoring ◽

Damage Assessment ◽

Scientific Research ◽

Structural Mechanics ◽

Structural Health ◽

Aging Aircraft ◽

Periodic Maintenance ◽

Implementation And Dissemination

This paper presents the perspective of the Structural Mechanics program of the Air Force Office of Scientific Research on the damage assessment of structures. It is found that damage assessment of structures plays a very important role in assuring the safety and operational readiness of Air Force fleet. The current fleet has many aging aircraft, which poses a considerable challenge for the operators and maintainers. The nondestructive evaluation technology is rather mature and able to detect damage with considerable reliability during the periodic maintenance inspections. The emerging structural health monitoring methodology has great potential, because it will use on-board damage detection sensors and systems, will be able to offer on-demand structural health bulletins. Considerable fundamental and applied research is still needed to enable the development, implementation, and dissemination of structural health monitoring technology.

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Development of a Novel Methodology for Remaining Useful Life Prediction of Industrial Slurry Pumps in the Absence of Run to Failure Data

Sensors ◽

10.3390/s21248420 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8420

Author(s):

Muhammad Mohsin Khan ◽

Peter W. Tse ◽

Amy J.C. Trappey

Keyword(s):

Life Prediction ◽

Prediction Models ◽

Remaining Useful Life ◽

Condition Based Maintenance ◽

Vibration Signals ◽

The Core ◽

Failure Data ◽

Periodic Maintenance ◽

Useful Life ◽

Nonlinear Autoregressive

Smart remaining useful life (RUL) prognosis methods for condition-based maintenance (CBM) of engineering equipment are getting high popularity nowadays. Current RUL prediction models in the literature are developed with an ideal database, i.e., a combination of a huge “run to failure” and “run to prior failure” data. However, in real-world, run to failure data for rotary machines is difficult to exist since periodic maintenance is continuously practiced to the running machines in industry, to save any production downtime. In such a situation, the maintenance staff only have run to prior failure data of an in operation machine for implementing CBM. In this study, a unique strategy for the RUL prediction of two identical and in-process slurry pumps, having only real-time run to prior failure data, is proposed. The obtained vibration signals from slurry pumps were utilized for generating degradation trends while a hybrid nonlinear autoregressive (NAR)-LSTM-BiLSTM model was developed for RUL prediction. The core of the developed strategy was the usage of the NAR prediction results as the “path to be followed” for the designed LSTM-BiLSTM model. The proposed methodology was also applied on publically available NASA’s C-MAPSS dataset for validating its applicability, and in return, satisfactory results were achieved.

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Analysis of Factors that Affect Employee Work Accidents at PT. Windu Blambangan Sejati (WBS) Banyuwangi Regency 2018

JOURNAL FOR QUALITY IN PUBLIC HEALTH ◽

10.30994/jqph.v3i2.77 ◽

2020 ◽

Vol 3 (2) ◽

pp. 300-308

Author(s):

Ruri Firmansyah ◽

Sismulyanto Sismulyanto ◽

Nurwijayanti Nurwijayanti

Keyword(s):

Occupational Safety ◽

Simple Random Sampling ◽

P Value ◽

Cross Sectional ◽

Work Related ◽

Safety And Health ◽

Workplace Accidents ◽

Periodic Maintenance ◽

Research Population ◽

Work Accidents

Workplaces always have various hazard factors that can affect the health of the workforce or can cause work-related disorders or illnesses. Accidents, the essence is an unexpected event and certainly not expected by anyone. These unexpected events, obviously not a form of intentional and not planned in advance. Accidents arise as a result of managing potential hazards and risks to low occupational safety and health. The purpose of the study was to analyze the factors that influence employee work accidents at PT. Windu Blambangan Sejati (WBS) Banyuwangi Regency. The research design used was cross sectional. The research population is all employees of PT. Windu Blambangan Sejati (WBS) Banyuwangi Regency and a large sample of 81 people using simple random sampling. Data collection using questionnaire sheets was then analyzed using logistic regression tests. The results showed that there was an influence of skills on workplace accidents (p-value = 0.027 <0.05). There is an influence of experience on workplace accidents (p-value = 0.043 <0.05). There is an influence of discipline on workplace accidents (p-value = 0.040 <0.05). There is an influence of Technical factors (availability of equipment) on workplace accidents (p-value = 0.003 <0.05). There is no effect of lay out on work accidents (p-value = 0.420> 0.05). There is no effect of lighting on work accidents (p-value = 0.944> 0.05). There is no noise effect on work accidents (p-value = 0.084> 0.05). Based on these studies, company management pays more attention to these three factors, namely human, technical and environmental by conducting periodic and periodic maintenance

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PRODUKTIVITAS ALAT BERAT DAN BIAYA PELAKSANAAN PEKERJAAN PADA PROYEK PEMELIHARAAN BERKALA RUAS JALAN: SUNGAI LAKUM – PASAR KAMIS (KERTAK HANYAR)

INFO-TEKNIK ◽

10.20527/infotek.v21i1.8967 ◽

2020 ◽

Vol 21 (1) ◽

pp. 103

Author(s):

Aulia Isramaulana ◽

Rizka Norjanah

Keyword(s):

Theoretical Analysis ◽

Data Processing ◽

Field Data ◽

Unit Price ◽

Base Layer ◽

Class A ◽

Heavy Equipment ◽

Periodic Maintenance ◽

Class B ◽

Aggregate Base

On the project Periodic Maintenance of Road Sungai Lakum Pasar Kamis is a project implemented using heavy equipment. By using heavy equipment, will facilitate and accelerate the course of the project. The tools used must be considered maintenance, because it is very influential on the production tools and costs incurred for the use of the tool. This study aims to analyze the use of heavy equipment in the periodic Maintenance Project of road Sungai Lakum Pasar Kamis by using theoretical analysis based on field data. Analyzing the calculation is the productivity and cost of the equipment on the scope of work that is the work of the land, widening work and road shoulder work, grinding pavement work, and asphalt work, using Excel program as a tool in data processing so that the achievement of the use of heavy equipment available in finish the job. Based on the calculation obtained the price of theoretical analysis unit based on field data for the work of preferred stockpiles from the source of Rp. 310.994.714,2; Class B aggregate base layer Rp. 76.127.086,04; Aggregate base layer with hill excavation Rp. 59.705.050,39; Class A base aggregate layer Rp. 204.703.029,9; Asphalt lining resap coat liquid Rp. 3.038.436,28; Lataston coating foundation Rp. 306.997.682,6. Where the calculation of unit price based on field data is smaller than the unit price of the bid data.

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A HYBRID MODEL FOR THE REVERSE ENGINEERING OF THE MILAN CATHEDRAL. CHALLENGES AND LESSON LEARNT

10.4995/arqueologica9.2021.12138 ◽

2021 ◽

Author(s):

Franco Spettu ◽

Simone Teruggi ◽

Francesco Canali ◽

Cristiana Achille ◽

Francesco Fassi

Keyword(s):

Reverse Engineering ◽

Point Cloud ◽

Learning Algorithm ◽

A Priori ◽

Point Clouds ◽

Logical Structure ◽

Cloud Data ◽

Survey Techniques ◽

Periodic Maintenance ◽

Maintenance And Inspection

Cultural Heritage (CH) 3D digitisation is getting increasing attention and importance. Advanced survey techniques provide as output a 3D point cloud, wholly and accurately describing even the most complex architectural geometry with a priori established accuracy. These 3D point models are generally used as the base for the realisation of 2D technical drawings and 3D advanced representations. During the last 12 years, the 3DSurveyGroup (3DSG, Politecnico di Milano) conduced an omni-comprehensive, multi-technique survey, obtaining the full point cloud of Milan Cathedral, from which were produced the 2D technical drawings and the 3D model of the Main Spire used by the Veneranda Fabbrica del Duomo di Milano (VF) to plan its periodic maintenance and inspection activities on the Cathedral. Using the survey product directly to plan VF activities would help to skip a long-lasting, uneconomical and manual process of 2D and 3D technical elaboration extraction. In order to do so, the unstructured point cloud data must be enriched with semantics, providing a hierarchical structure that can communicate with a powerful, flexible information system able to effectively manage both point clouds and 3D geometries as hybrid models. For this purpose, the point cloud was segmented using a machine-learning algorithm with multi-level multi-resolution (MLMR) approach in order to obtain a manageable, reliable and repeatable dataset. This reverse engineering process allowed to identify directly on the point cloud the main architectonic elements that are then re-organised in a logical structure inserted inside the informative system built inside the 3DExperience environment, developed by Dassault Systémes.

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