Data-driven process planning for shipbuilding

2017 ◽  
Vol 32 (1) ◽  
pp. 122-130 ◽  
Author(s):  
Jinsong Bao ◽  
Xiaohu Zheng ◽  
Jianguo Zhang ◽  
Xia Ji ◽  
Jie Zhang
Keyword(s):  
Health Management ◽  
Model Identification ◽  
Production Performance ◽  
Data Driven ◽  
Complex Process ◽  
Data Driven Approach ◽  
Plan Change ◽  
Major Factors ◽  
Relevant Factors

AbstractErection planning in shipbuilding is a highly complex process. When a process change happens for some reason, it is often difficult to identify how many factors are affected and estimate how sensitive these factors can be. To optimize the planning and replanning of the shipbuilding plan for the best production performance, a data-driven approach for shipbuilding erection planning is proposed, which is composed of an erection plan model, identification of major factors related to the erection plan, and a data-driven algorithm to apply shipbuilding operation data for creating plans and forecasting, for plan adjustment, future availabilities of shipyard resources including machines, equipment, and man power. Through data clustering, the relevant factors are identified as a result of plan change, and critical equipment health management is carried out through data-driven anomaly detection. A case study is implemented, and the result shows that the proposed data-driven method is able to reschedule the shipbuilding plans smoothly.

A Cognitive Data-Driven Single-Well Modeling Workflow for Reservoir Deliverability Predictions – Expanding the Wireline Formation Tester Application Envelope

2021 ◽  
Author(s):  
Abdul Bari ◽  
Mohammad Rasheed Khan ◽  
M. Sohaib Tanveer ◽  
Muhammad Hammad ◽  
Asad Mumtaz Adhami ◽  
...  
Keyword(s):  
Reservoir Characterization ◽  
Production Performance ◽  
Data Driven ◽  
Analytical Models ◽  
Production Model ◽  
Reservoir Evaluation ◽  
Fluid Properties ◽  
Petrophysical Model ◽  
Single Well ◽  
Data Driven Approach

Abstract In today's dynamically challenging E&P industry, exploration activities demand for out-of-the-box measures to make the most out of the data available at hand. Instead of relying on time consuming and cost-intensive deliverability testing, there is a strong push to extract maximum possible information from time- and cost-efficient wireline formation testers in combination with other openhole logs to get critical reservoir insight. Consequently, driving efficiency in the appraisal process by reducing redundant expenditures linked with reservoir evaluation. Employing a data-driven approach, this paper addresses the need to build single-well analytical models that combines knowledge of core data, petrophysical evaluation and reservoir fluid properties. Resultantly, predictive analysis using cognitive processes to determine multilayer productivity for an exploratory well is achieved. Single Well Predictive Modeling (SWPM) workflow is developed for this case which utilizes plethora of formation evaluation information which traditionally resides across siloed disciplines. A tailor-made workflow has been implemented which goes beyond the conventional formation tester deliverables while incorporating PVT and numerical simulation methodologies. Stage one involved reservoir characterization utilizing Interval Pressure Transient Testing (IPTT) done through the mini-DST operation on wireline formation tester. Stage two concerns the use of analytical modeling to yield exact solution to an approximate problem whose end-product is an estimate of the Absolute Open Flow Potential (AOFP). Stage three involves utilizing fluid properties from downhole fluid samples and integrating with core, OH logs, and IPTT answer products to yield a calibrated SWPM model, which includes development of a 1D petrophysical model. Additionally, this stage produces a 3D simulation model to yield a reservoir production performance deliverable which considers variable rock typing through neural network analysis. Ultimately, stage four combines the preceding analysis to develop a wellbore production model which aids in optimizing completion strategies. The application of this data-driven and cognitive technique has helped the operator in evaluating the potential of the reservoir early-on to aid in the decision-making process for further investments. An exhaustive workflow is in place that can be adopted for informed reservoir deliverability modeling in case of early well-life evaluations.

scholarly journals Data-driven Application of PHM to Asset Strategies

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Sarah Lukens ◽  
Matt Markham
Keyword(s):  
Data Analytics ◽  
Failure Modes ◽  
Health Management ◽  
Data Driven ◽  
Work Processes ◽  
Industrial Companies ◽  
Use Of Data ◽  
Reliability Centered Maintenance ◽  
Daunting Challenge

There are many benefits from implementing a prognostics and health management (PHM) initiative in an industrial facility, such as realizing potentials from reducing unplanned downtime and increased asset efficiency. Many industrial companies would like to take advantage of PHM technologies and algorithms to meet their business objectives, but identifying how to get started can be a daunting challenge. The classical approach is to begin with a Reliability Centered Maintenance (RCM) program supported by failure modes and effects analysis (FMEA) where all possible failure modes, their risks, and mitigating actions are evaluated in the context of asset function. In this framework, application of PHM technologies is viewed as a maintenance strategy effective at mitigating certain failure modes in specific cases that are both feasible and costeffective. However, there are many challenges and limitations to traditional RCM where data-driven analytics embedded in these work processes can help overcome and/or automate. On the other hand, the use of data-driven approaches introduces new challenges surrounding available data, data quality, and identifying numerical methods that are scalable across large datasets. In this paper, we present a case study applied to historical maintenance data for identifying and prioritizing where to start a PHM initiative, and discuss the work processes and various challenges encountered when embedding data analytics in classical reliability approaches.

scholarly journals Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study

2020 ◽  
pp. 221-251
Author(s):  
Stefan Varga ◽  
Joel Brynielsson ◽  
Andreas Horndahl ◽  
Magnus Rosell
Keyword(s):  
Text Analysis ◽  
Ecological Validity ◽  
Data Driven ◽  
Intelligence Analysis ◽  
Prototype Tool ◽  
Analysis Task ◽  
Private Citizens ◽  
Data Driven Approach ◽  
Psychological Operations

Abstract With the availability of an abundance of data through the Internet, the premises to solve some intelligence analysis tasks have changed for the better. The study presented herein sets out to examine whether and how a data-driven approach can contribute to solve intelligence tasks. During a full day observational study, an ordinary military intelligence unit was divided into two uniform teams. Each team was independently asked to solve the same realistic intelligence analysis task. Both teams were allowed to use their ordinary set of tools, but in addition one team was also given access to a novel text analysis prototype tool specifically designed to support data-driven intelligence analysis of social media data. The results, obtained from the case study with a high ecological validity, suggest that the prototype tool provided valuable insights by bringing forth information from a more diverse set of sources, specifically from private citizens that would not have been easily discovered otherwise. Also, regardless of its objective contribution, the capabilities and the usage of the tool were embraced and subjectively perceived as useful by all involved analysts.

scholarly journals A Data-Driven Approach to Extend Failure Analysis: A Framework Development and a Case Study on a Hydroelectric Power Plant

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6400
Author(s):  
Sara Antomarioni ◽  
Marjorie Maria Bellinello ◽  
Maurizio Bevilacqua ◽  
Filippo Emanuele Ciarapica ◽  
Renan Favarão da Silva ◽  
...  
Keyword(s):  
Power Plant ◽  
Failure Analysis ◽  
Power Plants ◽  
Failure Modes ◽  
Real Life ◽  
Hydroelectric Power Plant ◽  
Data Driven ◽  
Hydroelectric Power ◽  
Data Driven Approach

Power plants are required to supply the electric demand efficiently, and appropriate failure analysis is necessary for ensuring their reliability. This paper proposes a framework to extend the failure analysis: indeed, the outcomes traditionally carried out through techniques such as the Failure Mode and Effects Analysis (FMEA) are elaborated through data-driven methods. In detail, the Association Rule Mining (ARM) is applied in order to define the relationships among failure modes and related characteristics that are likely to occur concurrently. The Social Network Analysis (SNA) is then used to represent and analyze these relationships. The main novelty of this work is represented by support in the maintenance management process based not only on the traditional failure analysis but also on a data-driven approach. Moreover, the visual representation of the results provides valuable support in terms of comprehension of the context to implement appropriate actions. The proposed approach is applied to the case study of a hydroelectric power plant, using real-life data.

scholarly journals A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants: A Hospital Case Study

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 827 ◽  
Author(s):  
Serafín Alonso ◽  
Antonio Morán ◽  
Miguel Prada ◽  
Perfecto Reguera ◽  
Juan Fuertes ◽  
...  
Keyword(s):  
Energy Demand ◽  
Data Driven ◽  
Coefficient Of Performance ◽  
Key Factors ◽  
Whole Plant ◽  
Data Driven Approach ◽  
Energy Efficiency Indicator ◽  
Cooling Loads ◽  
Aggregation Operations

Large buildings cause more than 20% of the global energy consumption in advanced countries. In buildings such as hospitals, cooling loads represent an important percentage of the overall energy demand (up to 44%) due to the intensive use of heating, ventilation and air conditioning (HVAC) systems among other key factors, so their study should be considered. In this paper, we propose a data-driven analysis for improving the efficiency in multiple-chiller plants. Coefficient of performance (COP) is used as energy efficiency indicator. Data analysis, based on aggregation operations, filtering and data projection, allows us to obtain knowledge from chillers and the whole plant, in order to define and tune management rules. The plant manager software (PMS) that implements those rules establishes when a chiller should be staged up/down and which chiller should be started/stopped according different efficiency criteria. This approach has been applied on the chiller plant at the Hospital of León.

Steps towards incorporating heterogeneities into program theory: A case study of a data-driven approach

2016 ◽  
Vol 58 ◽  
pp. 88-97 ◽  
Author(s):  
Sanjeev Sridharan ◽  
Bobby Jones ◽  
Barry Caudill ◽  
April Nakaima
Keyword(s):  
Program Theory ◽  
Data Driven ◽  
Data Driven Approach
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