scholarly journals Combinatorial Bayesian Dynamic Linear Models of Bridge Monitored Data and Reliability Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Xueping Fan ◽  
Yuefei Liu
Keyword(s):  
Linear Models ◽  
Historical Data ◽  
Polynomial Function ◽  
Reliability Prediction ◽  
Structural Components ◽  
Time Data ◽  
Dynamic Linear Models ◽  
Monitoring Mechanism ◽  
Real Time Data ◽  
Order Polynomial

Considering the uncertainties and randomness of the mass structural health monitored data, the objectives of this paper are to present (a) a procedure for effective incorporation of the monitored data for the reliability prediction of structural components or structures, (b) one transforming method of Bayesian dynamic linear models (BDLMs) based on 1-order polynomial function, (c) model monitoring mechanism used to look for possible abnormal data based on BDLMs, (d) combinatorial Bayesian dynamic linear models based on the multiple BDLMs and their corresponding weights of prediction precision, and (e) an effective way of taking advantage of combinatorial Bayesian dynamic linear models to incorporate the historical data and real-time data in structural time-variant reliability prediction. Finally, a numerical example is provided to illustrate the application and feasibility of the proposed procedures and concepts.

scholarly journals An LSTM-Based Method Considering History and Real-Time Data for Passenger Flow Prediction

2020 ◽  
Vol 10 (11) ◽  
pp. 3788 ◽  
Author(s):  
Qi Ouyang ◽  
Yongbo Lv ◽  
Jihui Ma ◽  
Jing Li
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Short Term Memory ◽  
Historical Data ◽  
Time Interval ◽  
Information Coding ◽  
Time Data ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Real Time Data

With the development of big data and deep learning, bus passenger flow prediction considering real-time data becomes possible. Real-time traffic flow prediction helps to grasp real-time passenger flow dynamics, provide early warning for a sudden passenger flow and data support for real-time bus plan changes, and improve the stability of urban transportation systems. To solve the problem of passenger flow prediction considering real-time data, this paper proposes a novel passenger flow prediction network model based on long short-term memory (LSTM) networks. The model includes four parts: feature extraction based on Xgboost model, information coding based on historical data, information coding based on real-time data, and decoding based on a multi-layer neural network. In the feature extraction part, the data dimension is increased by fusing bus data and points of interest to improve the number of parameters and model accuracy. In the historical information coding part, we use the date as the index in the LSTM structure to encode historical data and provide relevant information for prediction; in the real-time data coding part, the daily half-hour time interval is used as the index to encode real-time data and provide real-time prediction information; in the decoding part, the passenger flow data for the next two 30 min interval outputs by decoding all the information. To our best knowledge, it is the first time to real-time information has been taken into consideration in passenger flow prediction based on LSTM. The proposed model can achieve better accuracy compared to the LSTM and other baseline methods.

Development of Failure Prediction Models for Subsea Blowout Preventers Using Data Analytics and AI

2021 ◽  
Author(s):  
Rodrigo Chamusca Machado ◽  
Fabbio Leite ◽  
Cristiano Xavier ◽  
Alberto Albuquerque ◽  
Samuel Lima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Analytics ◽  
Historical Data ◽  
Learning Algorithms ◽  
Failure Prediction ◽  
Machine Learning Algorithms ◽  
Time Data ◽  
Real Time Data ◽  
Operational Data

Objectives/Scope This paper presents how a brazilian Drilling Contractor and a startup built a partnership to optimize the maintenance window of subsea blowout preventers (BOPs) using condition-based maintenance (CBM). It showcases examples of insights about the operational conditions of its components, which were obtained by applying machine learning techniques in real time and historic, structured or unstructured, data. Methods, Procedures, Process From unstructured and structured historical data, which are generated daily from BOP operations, a knowledge bank was built and used to develop normal functioning models. This has been possible even without real-time data, as it has been tested with large sets of operational data collected from event log text files. Software retrieves the data from Event Loggers and creates structured database, comprising analog variables, warnings, alarms and system information. Using machine learning algorithms, the historical data is then used to develop normal behavior modeling for the target components. Thereby, it is possible to use the event logger or real time data to identify abnormal operation moments and detect failure patterns. Critical situations are immediately transmitted to the RTOC (Real-time Operations Center) and management team, while less critical alerts are recorded in the system for further investigation. Results, Observations, Conclusions During the implementation period, Drilling Contractor was able to identify a BOP failure using the detection algorithms and used 100% of the information generated by the system and reports to efficiently plan for equipment maintenance. The system has also been intensively used for incident investigation, helping to identify root causes through data analytics and retro-feeding the machine learning algorithms for future automated failure predictions. This development is expected to significantly reduce the risk of BOP retrieval during the operation for corrective maintenance, increased staff efficiency in maintenance activities, reducing the risk of downtime and improving the scope of maintenance during operational windows, and finally reduction in the cost of spare parts replacementduring maintenance without impact on operational safety. Novel/Additive Information For the near future, the plan is to integrate the system with the Computerized Maintenance Management System (CMMS), checking for historical maintenance, overdue maintenance, certifications, at the same place and time that we are getting real-time operational data and insights. Using real-time data as input, we expect to expand the failure prediction application for other BOP parts (such as regulators, shuttle valves, SPMs (Submounted Plate valves), etc) and increase the applicability for other critical equipment on the rig.

Stuck Pipe Early Detection on Extended Reach Wells Using Ensemble Method of Machine Learning

2021 ◽  
Author(s):  
Rushad Ravilievich Rakhimov ◽  
Oleg Valerievich Zhdaneev ◽  
Konstantin Nikolaevich Frolov ◽  
Maxim Pavlovich Babich
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Historical Data ◽  
Learning Algorithm ◽  
Ensemble Method ◽  
Machine Learning Algorithm ◽  
Time Data ◽  
Machine Learning Model ◽  
Real Time Data ◽  
The Ensemble Method

Abstract The ultimate objective of this paper is to describe the experience of using a machine learning model prepared by the ensemble method to prevent stuck pipe events during well construction process on extended reach wells. The tasks performed include collecting, analyzing and cleaning historical data, selecting and preparing a machine learning model, testing it on real-time data by means of desktop application. The idea is to display the solution at the rig floor, allowing Driller to quickly take actions for prevention of stuck pipe event. Historical data mining and analysis were performed using software for remote monitoring. Preparation, labelling and cleaning of historical and real-time data were executed using programmable scripts and big data techniques. The machine learning algorithm was developed using the ensemble method, which allows to combine several models to improve the final result. On the field of interest, the most common type of stuck pipe are solids induced pack offs. They occur due to insufficient hole cleaning from drilled cuttings and wellbore collapse due to rocks instability. Stuck pipe prevention on extended reach drilling (ERD) wells requires holistic approach meanwhile final role is assigned to the driller. Due to continuously exceeding ERD envelope and increased workloads on both personnel and drilling equipment, the effectiveness of preventing accidents is deteriorating. This leads to severe consequences: Bottom Hole Assembly lost in hole, the necessity to re-drill the bore and eventually to increased Non-Productive Time (NPT). Developed application based on ensemble machine learning algorithm shows prediction accuracy above 94%. Reacting on alarms, driller can quickly take measures to prevent downhole accidents during well construction of ERD wells.

Real Time Data Integration Based on the GIS Pipeline Infrastructure for the Management of the Gas and Power Business

2006 ◽  
Author(s):  
Fernando Jose´ de Carvalho Salcedo ◽  
Ronaldo Jose´ Seixas de Carvalho
Keyword(s):  
Data Integration ◽  
Real Time ◽  
Performance Indicators ◽  
Historical Data ◽  
Geographical Information ◽  
Main Process ◽  
Business Unit ◽  
Time Data ◽  
The Real ◽  
Real Time Data

The Strategic Data and Information Management (GEDI, as per Portuguese initials) in PETROBRAS (Brazilian oil company - Gas & Power Business Unit), has as its main process to turn available the most correct and updated information to the related user, using the adequate means to access and capture of data, coming from a variety of sources, in order to add strategic value to business. The SCADA system (Supervisory Control And Data Acquisition) integrates the facilities of thermo electrical plant and pipeline with the field, including operational stations, measurements and energy deliveries. The Geographical Information Systems (GIS) allows the use of maps to visualize the geopolitics aspects, gas pipeline infrastructure and satellite images. The historical data systems has as its requirements, the interface among many SCADA systems, through the tracking of historical data, common process variables real time data (flow, pressure, temperature, etc.) and KPI’s visualization (typical performance indicators of energy systems such as unavailability, generation efficiency, distribution, etc.). Based on the business systemic vision, the Real-Time Enterprise Architecture (real time data integration and performance indicators based on the GIS software platform ) was developed for PETROBRAS, Gas & Power Business Unit (GPBU) enterprise scenario. The present work has its focus in the real time visualization of integrated data, coming from gas pipelines and thermo electrical plants GIS infra-structure, guaranteeing the integrity, the audit trail of information and a proactive vision for the GPBU management.

Real-Time Data-Based Fault Diagnosis System

2011 ◽  
Vol 189-193 ◽  
pp. 2621-2624
Author(s):  
Hai Wang
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Historical Data ◽  
Prototype System ◽  
Time Data ◽  
Diagnosis System ◽  
Real Time Analysis ◽  
Real Time Data ◽  
Diagnosis Method ◽  
Fault Diagnosis System

Mining from the equipment/technology process historical data can find diagnosis knowledge. Real-time analysis and evaluation on the status of equipment can be realized based on its current state parameters and historical information, which detect the potential fault rapidly and protect equipment to avoid failures further. This paper did an in-depth study in real-time data-based fault diagnosis system, built a real-time data integration platform and accomplished intelligent diagnosis method by ANFIS networks with the mined historical data. The production process was diagnosed and evaluated online with this diagnosis method. Combined with actual production system, its prototype system was developed.

scholarly journals NMSTREAM: A SCALABLE EVENT-DRIVEN ETL FRAMEWORK FOR PROCESSING HETEROGENEOUS STREAMING DATA

2018 ◽  
Vol IV-4 ◽  
pp. 243-246 ◽  
Author(s):  
F. Xiao ◽  
C. Li ◽  
Z. Wu ◽  
Y. Wu
Keyword(s):  
Data Streams ◽  
Historical Data ◽  
Streaming Data ◽  
Data Warehouses ◽  
Time Data ◽  
Web Based ◽  
Effective Manner ◽  
Real Time Data ◽  
Event Driven ◽  
Data Analytic

<p><strong>Abstract.</strong> ETL (Extraction-Transform-Load) tools, traditionally developed to operate offline on historical data for feeding Data-warehouses need to be enhanced to deal with continuously increased streaming data and be executed at network level during data streams acquisition. In this paper, a scalable and web-based ETL system called NMStream was presented. NMStream is based on event-driven architecture and designed for integrating distributed and heterogeneous streaming data by integrating the Apache Flume and Cassandra DB system, and the ETL processes were conducted through the Flume agent object. NMStream can be used for feeding traditional/real-time data-warehouses or data analytic tools in a stable and effective manner.</p>

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data
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