scholarly journals Performance Data Analytics for Contact Cooled Rotary Screw Air Compressors

2020 ◽  
Vol 8 (6) ◽  
pp. 3597-3602
Keyword(s):  
Operations Management ◽  
Preventive Maintenance ◽  
Data Analytics ◽  
Historical Data ◽  
Control Limit ◽  
System Availability ◽  
Mean Values ◽  
Performance Deterioration ◽  
Air Compressors ◽  
Rotary Screw

The operations management of air compressors gets transformed from the traditional reactive approach to the proactive data analytics based one. While large volume of data is analyzed, it is important to identify the parameters that cause performance deterioration. This helps in the preventive maintenance and increases the system availability to a great extent. In this research, we propose an approach to identify the causal parameters and test them in a practical customer environment. We have analyzed the data pertaining to Contact Cooled Rotary Screw Air Compressors from a customer's site. The system is controlled by XE-90M/145M controller. The controller continuously monitors 85 parameters and values collected every minute and uploaded to the cloud every 15 minutes. We analyzed the data for all the parameters, identified the upper control limit (UCL), lower control limit (LCL) and mean values. Using the historical data of drip of compressors, we have analyzed the data and classified the parameters into two categories, parameters which causes the performance deterioration and the ones that goes abnormal as a consequence of performance deterioration. Also, the study has discovered that the causal parameters show performance deterioration symptoms during a window of one to three hours before the actual drip happens. This has helped the customer to proactively do the corrective actions in the window and avoid drip of the compressor

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.

Big Data Analytics in Aquaculture Using Hive and Hadoop Platform

2018 ◽  
pp. 29-35
Author(s):  
P. Venkateswara Rao ◽  
A. Ramamohan Reddy ◽  
V. Sucharita
Keyword(s):  
Big Data ◽  
Data Management ◽  
Single Machine ◽  
Data Analytics ◽  
Historical Data ◽  
Big Data Analytics ◽  
Huge Amount ◽  
Hadoop Platform ◽  
Different Sources

In the field of Aquaculture with the help of digital advancements huge amount of data is constantly produced for which the data of the aquaculture has entered in the big data world. The requirement for data management and analytics model is increased as the development progresses. Therefore, all the data cannot be stored on single machine. There is need for solution that stores and analyzes huge amounts of data which is nothing but Big Data. In this chapter a framework is developed that provides a solution for shrimp disease by using historical data based on Hive and Hadoop. The data regarding shrimps is acquired from different sources like aquaculture websites, various reports of laboratory etc. The noise is removed after the collection of data from various sources. Data is to be uploaded on HDFS after normalization is done and is to be put in a file that supports Hive. Finally classified data will be located in particular place. Based on the features extracted from aquaculture data, HiveQL can be used to analyze shrimp diseases symptoms.

Optimal decisions for operations management of BDAR: A military industrial logistics data analytics perspective

2019 ◽  
Vol 137 ◽  
pp. 106100
Author(s):  
Xiong Li ◽  
Xiaodong Zhao ◽  
Wei Pu ◽  
Ping Chen ◽  
Fang Liu ◽  
...  
Keyword(s):  
Operations Management ◽  
Data Analytics ◽  
Optimal Decisions

scholarly journals Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

2020 ◽  
Vol 17 (19) ◽  
pp. 7080 ◽  
Author(s):  
Abdelrahman E. E. Eltoukhy ◽  
Ibrahim Abdelfadeel Shaban ◽  
Felix T. S. Chan ◽  
Mohammad A. M. Abdel-Aal
Keyword(s):  
Data Analytics ◽  
Historical Data ◽  
External Factors ◽  
Number Of Patients ◽  
Future Prediction ◽  
The Government ◽  
Using Data ◽  
Novel Coronavirus ◽  
Improved Accuracy ◽  
Nonlinear Autoregressive

The outbreak of the 2019 novel coronavirus disease (COVID-19) has adversely affected many countries in the world. The unexpected large number of COVID-19 cases has disrupted the healthcare system in many countries and resulted in a shortage of bed spaces in the hospitals. Consequently, predicting the number of COVID-19 cases is imperative for governments to take appropriate actions. The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. In the literature, most of the existing prediction methods focus only on the historical data and overlook most of the external factors. Hence, the number of COVID-19 cases is inaccurately predicted. Therefore, the main objective of this study is to simultaneously consider historical data and the external factors. This can be accomplished by adopting data analytics, which include developing a nonlinear autoregressive exogenous input (NARX) neural network-based algorithm. The viability and superiority of the developed algorithm are demonstrated by conducting experiments using data collected for top five affected countries in each continent. The results show an improved accuracy when compared with existing methods. Moreover, the experiments are extended to make future prediction for the number of patients afflicted with COVID-19 during the period from August 2020 until September 2020. By using such predictions, both the government and people in the affected countries can take appropriate measures to resume pre-epidemic activities.

Preventive Maintenance of Multi-State System with Phase-Type Failure Time Distribution and Non-Zero Inspection Time

2003 ◽  
Vol 10 (03) ◽  
pp. 323-344 ◽  
Author(s):  
Dongyan Chen ◽  
Yonghuan Cao ◽  
Kishor S. Trivedi ◽  
Yiguang Hong
Keyword(s):  
Preventive Maintenance ◽  
Failure Time ◽  
Regenerative Process ◽  
Inspection Time ◽  
Maintenance Policy ◽  
System Availability ◽  
Failure Time Distribution ◽  
Set Up ◽  
Markov Regenerative Process ◽  
The Mathematical Model

Preventive maintenance is applied to improve the system availability or decrease the operational cost. This paper addresses the optimal preventive maintenance problem for multi-state deteriorating systems, where the system experiences multiple stages of performance degradation before it fails. We consider a general case where the inspection and repair time are generally distributed. The threshold type maintenance policy is employed for preventive minor maintenance and preventive major maintenance, where minor or major maintenance is carried out when the system deterioration stage is found to be larger than certain thresholds. The mathematical model of the system is set up by means of a Markov regenerative process (MRGP). With this formulation, the system steady-state probabilities under consideration are computed.

Big data analytics for proactive industrial decision support

atp magazin ◽  
2016 ◽  
Vol 58 (09) ◽  
pp. 62 ◽  
Author(s):  
Martin Atzmueller ◽  
Benjamin Klöpper ◽  
Hassan Al Mawla ◽  
Benjamin Jäschke ◽  
Martin Hollender ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Historical Data ◽  
Big Data Analytics ◽  
Lessons Learned ◽  
Support Functions ◽  
Big Data Applications ◽  
Big Data Technologies ◽  
Process Plants ◽  
Plant Data

Big data technologies offer new opportunities for analyzing historical data generated by process plants. The development of new types of operator support systems (OSS) which help the plant operators during operations and in dealing with critical situations is one of these possibilities. The project FEE has the objective to develop such support functions based on big data analytics of historical plant data. In this contribution, we share our first insights and lessons learned in the development of big data applications and outline the approaches and tools that we developed in the course of the project.

scholarly journals Optimal Maintenance of a Production-inventory System With Continuous Repair Times and Idle Periods

2020 ◽  
Vol Volume 14 ◽  
Keyword(s):  
Preventive Maintenance ◽  
Average Cost ◽  
Inventory System ◽  
Control Limit ◽  
Raw Material ◽  
Production Unit ◽  
Numerical Examples ◽  
Long Run ◽  
Production Inventory ◽  
Production Inventory System

In this paper two similar models for the maintenance of a production-inventory system are considered. In both models, an input generating installation supplies a buffer with a raw material and a production unit pulls the raw material from the buffer. The installation in the first model and the production unit in the second model deteriorate stochastically over time and the problem of their optimal preventive maintenance is considered. In the first model, it is assumed that the installation, after the completion of its maintenance, remains idle until the buffer is evacuated, while in the second model, it is assumed that the production unit, after the completion of its maintenance, remains idle until the buffer is filled up. The preventive and corrective repair times of the installation in the first model and the preventive and corrective repair times of the production unit in the second model are continuous random variables with known probability density functions. Under a suitable cost structure, semi-Markov decision processes are considered for both models in order to find a policy that minimizes the long-run expected average cost per unit time. A great number of numerical examples provide strong evidence that, for each fixed buffer content, the average-cost optimal policy is of control-limit type in both models, i.e. it prescribes a preventive maintenance of the installation in the first model and a preventive maintenance of the production unit in the second model if and only if their degree of deterioration is greater than or equal to a critical level. Using the usual regenerative argument, the average cost of the optimal control-limit policy is computed exactly in both models. Four numerical examples are also presented in which the preventive and corrective repair times follow the Exponential, the Weibull, the Gamma and the Log-Normal distribution, respectively.

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