Achieving Operational Efficiencies from a Centralized Alarm Management System

2021 ◽  
Author(s):  
Mahmoud AbdulHameed Al Mahmoud ◽  
Joseph Sylvester Pius David ◽  
Askar Jaffer
Keyword(s):  
Real Time ◽  
Management System ◽  
Integrated Control ◽  
Information Management System ◽  
Machine Learning Algorithms ◽  
Time Data ◽  
Alarm Management ◽  
Alarm Systems ◽  
Real Time Data ◽  
The Times

Abstract Alarm Management Systems ("AMS") have been adopted in the oil & gas industry where several benefits were realized. Such as improved panel operator effectiveness, maintaining higher levels of plant uptime and integrity, reducing the number of abnormal situation. Which ultimately leads to higher asset productivity. Several OPCO have multiple operational assets/sites that are geographically diverse. Where each asset might have a different Integrated Control System ("ICS") due to the time and availability of technology at the time of commissioning. Requiring diverse locally implemented AMS. A unified CAMS thus reduces time and effort to develop, deploy, and maintain alarm systems and is an essential toolkit for enhanced safe operation of the plant. Some sites have multiple plants wuth common pocess control section. The process control enginners needs to visit individual plants access DCS alalrms. By carryinhour corporate alarm management, engibbers at their office PCs have the access to the DCS alarms. Implementing CAMS requires the presence of a robust data presence infrastructure in place. Notably a centralized plant information management system, where several real time data points with regards to alarms and operator inputs can be captured. A CAMS unifies the approach of how alarm management is conducted in the company. Where a CAMS system generates a set of standard and custom templates that highlight the performance of each operating asset/shift/panel operator. Providing insights into the performance of each asset, efficiency of each operational shift and response of the panel operators. That when addressed, will lead to an overall performance and production of the operational asset. With this alarm management data, it can be further enhanced through data analytics to identify areas where operational efficiencies can be achieved. Additionally, the CAMS reduces the times and effort to deploy an alarm management system for any future operating asset expansions. CAMS coupled with real time data and Machine learning algorithms, past behaviours of the plant can be correlated, which can then be utilised for future predictions on alarms. This would further enhance our data driven decision-making, and would reduce the dependence on personal driven decisions. It can be concluded, that the CAMS is worthy investment for operating companies that have geographical/ICS diverse operational assets.

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.

Design and Implementation of a Loom Information Management System

2015 ◽  
Vol 719-720 ◽  
pp. 707-711
Author(s):  
Hui Li ◽  
Jian Xia Zheng ◽  
Tao Xu
Keyword(s):  
Information Management ◽  
Control Strategy ◽  
Management System ◽  
Information Management System ◽  
System Structure ◽  
Time Data ◽  
Design And Implementation ◽  
Real Time Data ◽  
And Control ◽  
Information Networking

In order to solve the problems revealed in loom devices deployed in textile enterprises, such as excessive varieties, differences caused by different produced times, technical inconsistence and over-complicated equipments, the paper puts forward the system structure and control strategy in the loom management system which solves the information networking problems for different structured loom equipments, achieves real-time data collection and detection on-site through which clients could get the latest updated weaving information, the system can be used as a reference for similar applications.

scholarly journals Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk

2021 ◽  
Vol 9 ◽  
Author(s):  
Apeksha Shah ◽  
Swati Ahirrao ◽  
Sharnil Pandya ◽  
Ketan Kotecha ◽  
Suresh Rathod
Keyword(s):  
Cardiac Arrest ◽  
Real Time ◽  
Cox Regression ◽  
Risk Classification ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Time Data ◽  
Extreme Gradient Boosting ◽  
Real Time Data

Cardiovascular disease (CVD) is considered to be one of the most epidemic diseases in the world today. Predicting CVDs, such as cardiac arrest, is a difficult task in the area of healthcare. The healthcare industry has a vast collection of datasets for analysis and prediction purposes. Somehow, the predictions made on these publicly available datasets may be erroneous. To make the prediction accurate, real-time data need to be collected. This study collected real-time data using sensors and stored it on a cloud computing platform, such as Google Firebase. The acquired data is then classified using six machine-learning algorithms: Artificial Neural Network (ANN), Random Forest Classifier (RFC), Gradient Boost Extreme Gradient Boosting (XGBoost) classifier, Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT). Furthermore, we have presented two novel gender-based risk classification and age-wise risk classification approach in the undertaken study. The presented approaches have used Kaplan-Meier and Cox regression survival analysis methodologies for risk detection and classification. The presented approaches also assist health experts in identifying the risk probability risk and the 10-year risk score prediction. The proposed system is an economical alternative to the existing system due to its low cost. The outcome obtained shows an enhanced level of performance with an overall accuracy of 98% using DT on our collected dataset for cardiac risk prediction. We also introduced two risk classification models for gender- and age-wise people to detect their survival probability. The outcome of the proposed model shows accurate probability in both classes.

Vista Angkasa Apartment Management System

2019 ◽  
Vol 1 (2) ◽  
pp. 76-84
Author(s):  
Devi Priya Thevaraju ◽  
Zalmiyah Zakaria
Keyword(s):  
Real Time ◽  
Programming Language ◽  
Management System ◽  
Time Data ◽  
The Real ◽  
Real Time Data ◽  
Management Staff

Vista Angkasa Apartment is one of the oldest apartments in the Bangsar South area, with a total of 8 blocks and consisting of more than 1000 units of houses. Currently, all the transaction related to the maintenance of each apartment is recorded manually. It is very difficult to manage all the data manually and if some information is required urgently then to obtain it also is very difficult. It will not only take a lot of time, but it also increases the chances of errors. Therefore, to solve the issues faced, an apartment management system that allows management staff to view the apartment’s data and tenant’s data as well as maintenance requests, notices and complaints has been developed. It also increases the efficiency and the effectiveness of Vista Angkasa Apartment Management by eliminating the current manual system. Compilation of data can be done easily with just a click of mouse. The methodology that has been applied to develop the system will be Agile with the PHP programming language. The developed system has successfully tested with the real time data at the Vista Angkasa Apartment. Based on the obtained results, the Vista Angkasa Apartment Management System solved the current issues with the management office and tenants.

Research on the Application of Generated Controlling System Base on OPC Rules

2014 ◽  
Vol 926-930 ◽  
pp. 1400-1403
Author(s):  
Ning Li
Keyword(s):  
Real Time ◽  
Control Function ◽  
Integrated Control ◽  
Control Program ◽  
Data Access ◽  
Time Data ◽  
Controlling System ◽  
Real Time Data ◽  
Data Acquisition Program ◽  
Program Data

The technology of real-time data access and the data management of real-time data technology in generated controlling system base on OPC rules is analyzed in this study. Aiming at the plant of certain generated controlling system, a set of software is developed based on OPC technology, which includes OPC client program, data acquisition program, data preprocessing program, advanced control program based on soft-sensing technology, real-time fault diagnosis expert system. The software has laid the good foundation for making full use of the greatly integrated control function of DCS. The result shows that the OPC rules can get well used in the generated controlling system.

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