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.

The Bus Dispatching Optimal Control System Based on Real-Time Data Acquisition

2012 ◽  
Vol 195-196 ◽  
pp. 1008-1016 ◽  
Author(s):  
Jian Li
Keyword(s):  
Optimal Control ◽  
Control System ◽  
Data Acquisition ◽  
Real Time ◽  
Time Interval ◽  
Time Data ◽  
Software Module ◽  
Optimal Control System ◽  
Departure Time ◽  
Real Time Data

In this paper, main software module in the subject The Bus Dispatching Optimal Control System Based on Real-time Data Acquisition has been designed. By gradually changing the departure time interval, the system uses SCM to simulate the system running status in various time periods so as to determine the optimal departure time interval, and has established the integrated and optimal scheduling model through the research and development of four functions i.e. data acquisition, remote network monitoring, C/S/S architecture, network access. This article will focus on the design of the main module of software part in the system.

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.

scholarly journals A Real-Time Data Analysis Platform for Short-Term Water Consumption Forecasting with Machine Learning

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 682-694
Author(s):  
Aida Boudhaouia ◽  
Patrice Wira
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Real Time ◽  
Water Consumption ◽  
Short Term Memory ◽  
Back Propagation ◽  
Time Data ◽  
Short Term ◽  
Real Time Data ◽  
Analysis Platform

This article presents a real-time data analysis platform to forecast water consumption with Machine-Learning (ML) techniques. The strategy fully relies on a web-oriented architecture to ensure better management and optimized monitoring of water consumption. This monitoring is carried out through a communicating system for collecting data in the form of unevenly spaced time series. The platform is completed by learning capabilities to analyze and forecast water consumption. The analysis consists of checking the data integrity and inconsistency, in looking for missing data, and in detecting abnormal consumption. Forecasting is based on the Long Short-Term Memory (LSTM) and the Back-Propagation Neural Network (BPNN). After evaluation, results show that the ML approaches can predict water consumption without having prior knowledge about the data and the users. The LSTM approach, by being able to grab the long-term dependencies between time steps of water consumption, allows the prediction of the amount of consumed water in the next hour with an error of some liters and the instants of the 5 next consumed liters in some milliseconds.

scholarly journals Study on real-time prediction model of railway passenger flow based on big data technology

2022 ◽  
Vol 355 ◽  
pp. 02025
Author(s):  
Yiyi Yin ◽  
Yong Zhang ◽  
Zhengzheng Wei ◽  
Xiang Zhao
Keyword(s):  
Big Data ◽  
Prediction Model ◽  
Real Time ◽  
Time Data ◽  
Message Queue ◽  
Passenger Flow ◽  
Time Prediction ◽  
Real Time Data ◽  
Railway Passenger ◽  
Big Data Technology

In order to solve the limitation of traditional offline forecasting application scenarios, the author uses a variety of big data open source frameworks and tools to combine with railway real-time data, and proposes a real-time prediction model of railway passenger flow. The model architecture is divided into four levels from bottom to top: data source layer, data transmission layer, prediction calculation layer and application layer. The main components of the model are data flow and prediction flow. Through message queue and ETL, the data process part realizes the synchronization of offline data and real-time data; through the big data technology frameworks such as Spark, Redis and Hive and the GBDT (Gradient Boosting Tree) algorithm, the prediction process partially realizes the real-time passenger flow of the train OD section prediction. The experimental results show that the model proposed by the author has certain practicability and accuracy both in performance and prediction accuracy.

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.

Measurement and Monitoring System With Real Time Data Logging Based on Microcontroller

2018 ◽  
Vol 7 (2) ◽  
pp. 20-40
Author(s):  
Abdelkrim Mohrem ◽  
Boukhemis Chetate ◽  
Houssem Eddine Guia
Keyword(s):  
Real Time ◽  
Time Interval ◽  
Data Logging ◽  
Time Data ◽  
Industrial Facilities ◽  
System A ◽  
Electronic Board ◽  
Real Time Data ◽  
Data Files ◽  
Log File

This article presents a microcontroller-based system for measurement and monitoring voltage of a three-phase electrical system with real-time data logging abilities. The proposed system uses voltage signals and time data as input. The output is an LCD and data files. The system accurately records abnormal voltage variations which have occurred on the system. A PC software is developed to receive and save data in two spreadsheet files through a serial port. Ihe log file contains the measured voltage, which is recorded periodically with a predefined time interval, and the second file contains the type of the fault. The proposed system is first simulated by ISIS-Proteus and then realized and implemented on an electronic board. It is beneficial to make detailed, scientific judgments and analysis for the voltage system to be supplied to a load. Because of the very simple circuit, it finds applications in industrial facilities. It is also useful in applying final circuits for both investigation and monitoring purposes.

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