ANOMALY DETECTION IN THE "SMART FIELD" DATA PROCESSING SYSTEMS

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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Geomagnetic anomaly associated with Fukushima earthquake on February 13th, 2021

E3S Web of Conferences ◽

10.1051/e3sconf/202133107012 ◽

2021 ◽

Vol 331 ◽

pp. 07012

Author(s):

Cipta Ramadhani ◽

Bulkis Kanata ◽

Abdullah Zainuddin ◽

Rosmaliati ◽

Teti Zubaidah

Keyword(s):

Data Processing ◽

Geomagnetic Field ◽

Field Data ◽

Low Frequency ◽

Earthquake Precursors ◽

Polarization Ratio ◽

Magnetic Polarization ◽

Central Frequency ◽

Electromagnetic Data ◽

Geomagnetic Anomaly

In this study, we performed research on electromagnetic anomalies related to earthquakes as early signs (precursors) that occurred in Fukushima, Japan on February 13th, 2021. The research focused on the utilization of geomagnetic field data which was derived from the Kakioka (KAK), Kanoya (KNY), and Memambetsu (MMB) observatories, particularly in the ultra-low frequency (ULF) to detect earthquake precursors. The method of electromagnetic data processing was conducted by applying a polarization ratio. In addition, we improved the methodology by splitting the ULF data (which ranged from 0.01-0.1 Hz) into 9 central frequencies and picking up the highest value from each central frequency to get the polarization ratio. The anomaly of magnetic polarization was identified 2-3 weeks before the mainshock in a narrowband frequency in the range of 0.04-0.05 Hz.

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PENGARUH KINERJA, MOTIVASI, DAN KOMPENSASI TERHADAP DISIPLIN KERJA OUSOUCING UNIT ATMRC PT BNI PERSERO TBK KANTOR WILAYAH PALEMBANG

Integritas Jurnal Manajemen Profesional (IJMPRO) ◽

10.35908/ijmpro.v2i2.94 ◽

2021 ◽

Vol 2 (2) ◽

pp. 223-234

Author(s):

Gunawan Putrado ◽

Fakhry Zamzam ◽

Neny Rostiati

Keyword(s):

Data Collection ◽

Data Processing ◽

Research Method ◽

Field Data ◽

Analysis Tool ◽

Regional Office ◽

Field Data Collection ◽

Representative Office

The purpose of this study was to determine the effect, motivation and compensation on work discipline of employees of the Outsourcing Atmrc Unit of PT BNI Persero Tbk Palembang Regional Office partially or simultaneously. This research method uses survey and verification descriptives, field data collection uses questionnaires, interviews and documentation. This research is an associative research, where in this study there are variables that are related and can influence other variables. The population of this study were 62 employees of the BPKP Representative Office Auditor, South Sumatra Province. Data processing using SPSS 25 analysis tool. The results of the study stated that performance had an effect on work discipline, there was an effect of motivation on work discipline, compensation had an effect on work discipline. Performance, Motivation and Compensation affect Discipline simultaneously

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Anomaly Detection from Log Files Using Multidimensional Analysis Model

Digital Technologies and Applications - Lecture Notes in Networks and Systems ◽

10.1007/978-3-030-73882-2_47 ◽

2021 ◽

pp. 515-524

Author(s):

Yassine Azizi ◽

Mostafa Azizi ◽

Mohamed Elboukhari

Keyword(s):

Anomaly Detection ◽

Multidimensional Analysis ◽

Analysis Model ◽

Log Files

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Magnetic field data processing methods of the China Seismo-Electromagnetic Satellite

Earth and Planetary Physics ◽

10.26464/epp2018043 ◽

2018 ◽

Vol 2 (6) ◽

pp. 455-461 ◽

Cited By ~ 8

Author(s):

Bin Zhou ◽

◽

YanYan Yang ◽

YiTeng Zhang ◽

XiaoChen Gou ◽

...

Keyword(s):

Magnetic Field ◽

Data Processing ◽

Field Data ◽

Processing Methods ◽

Magnetic Field Data ◽

Data Processing Methods

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Statistical Methodologies for Reliability Assessment of Gas Turbine Measurements

Volume 2: Turbo Expo 2003 ◽

10.1115/gt2003-38407 ◽

2003 ◽

Cited By ~ 2

Author(s):

M. Pinelli ◽

M. Venturini ◽

M. Burgio

Keyword(s):

Data Processing ◽

Mathematical Models ◽

Gas Turbine ◽

Measurement Errors ◽

Field Data ◽

Reliability Assessment ◽

Field Measurements ◽

Measurement Reliability ◽

Student Distribution ◽

Analysis Models

All measurements, although taken as accurately as possible, are subjected to uncertainty. So the analysis of errors and uncertainty is crucial in all applications since such errors need to be estimated and, when possible, reduced. In particular, when gas turbine mathematical models based on the processing of field measurements (such as the Gas Path Analysis models) are used, the evaluation of measurement reliability is a key point. In fact, it has been demonstrated that these kinds of techniques are sensitive to measurement errors: thus, tools for field data processing to evaluate the presence of the so-called outliers are advisable. In this paper, some statistical methodologies for the assessment of the reliability of the measurements taken on a gas turbine are presented. The methodologies, taken from literature and used for historical measurements, are discussed. Moreover, a new methodology, based on a modified t-Student distribution, is proposed.

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Detection and Classification of Sensor Anomalies in Gas Turbine Field Data

Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy ◽

10.1115/gt2018-75007 ◽

2018 ◽

Cited By ~ 1

Author(s):

Giuseppe Fabio Ceschini ◽

Lucrezia Manservigi ◽

Giovanni Bechini ◽

Mauro Venturini

Keyword(s):

Anomaly Detection ◽

Gas Turbine ◽

Gas Turbines ◽

Field Data ◽

Time Correlation ◽

Detection Algorithm ◽

Anomaly Classification ◽

Monitoring And Diagnostics ◽

Faulty Sensors

Anomaly detection and classification is a key challenge for gas turbine monitoring and diagnostics. To this purpose, a comprehensive approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named DCIDS) was developed by the authors in previous papers. The methodology consists of an Anomaly Detection Algorithm (ADA) and an Anomaly Classification Algorithm (ACA). The ADA identifies anomalies according to three different levels of filtering. Anomalies are subsequently analyzed by the ACA to perform their classification, according to time correlation, magnitude and number of sensors in which an anomaly is contemporarily identified. The performance of the DCIDS approach is assessed in this paper based on a significant amount of field data taken on several Siemens gas turbines in operation. The field data refer to six different physical quantities, i.e. vibration, pressure, temperature, VGV position, lube oil tank level and rotational speed. The analyses carried out in this paper allow the detection and classification of the anomalies and provide some rules of thumb for field operation, with the final aim of identifying time occurrence and magnitude of faulty sensors and measurements.

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