Projects
Projects: Projects for Investigator |
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| Reference Number | NIA_NGN_407 | |
| Title | Asset Data Intelligence | |
| Status | Completed | |
| Energy Categories | Fossil Fuels: Oil Gas and Coal(Oil and Gas, Refining, transport and storage of oil and gas) 100%; | |
| Research Types | Applied Research and Development 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 20%; ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 80%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Project Contact No email address given Northern Gas Networks |
|
| Award Type | Network Innovation Allowance | |
| Funding Source | Ofgem | |
| Start Date | 01 August 2022 | |
| End Date | 31 January 2023 | |
| Duration | ENA months | |
| Total Grant Value | £205,906 | |
| Industrial Sectors | Energy | |
| Region | Yorkshire & Humberside | |
| Programme | Network Innovation Allowance | |
| Investigators | Principal Investigator | Project Contact , Northern Gas Networks (100.000%) |
| Industrial Collaborator | Project Contact , Northern Gas Networks (0.000%) |
|
| Web Site | https://smarter.energynetworks.org/projects/NIA_NGN_407 |
|
| Objectives | Technical Method: - Provision of SAP Data Intelligence production tenant in SAP Business Technology Platform Connection of SAP Data Intelligence to NGNs existing SAP HANA Cloud Deployment of pre-configured Data Intelligence pipelines to identify anomalies in equipment records in SAP Plant Maintenance and configuration of SAP HANA Cloud to store the results using proprietary data processing algorithm Deployment of a Data Intelligence pipeline to surface the results of up to 3 rule-based validations in SAP S/4 HANA Amend pre-configured Data Intelligence pipelines as follows: restrict agreed data scope (medium pressure distribution mains, medium pressure valves) and model data to allow comparison of attributes across both asset classes and within the same class. Create enhancement in SAP so that the transactions for Equipment change and display (IE02/IE03) allow users to see if the Asset Data Intelligence pipelines have flagged the equipment record displayed in SAP as an outlier. This is facilitated through integrating the results (stored in SAP HANA Cloud) with SAP S/4 HANA via a service in SAP Business Technology Platform. Outlier report: A visual interface in either an SAP UI5 app or SAP Analytics Cloud report to surface the results of the data intelligence pipelines to end-users Measurement Quality Statement: - The following Quality Metrics will be monitored… Cost Control Delivery to pre-agreed timescales Defect Resolution Failure Rate Data Quality Statement: - The following Data Quality Metrics will be monitored… Accuracy Completeness Consistency Integrity Timeliness Uniqueness Validity The AI will assess data quality of NGN mains pipe data. To prove the feasibility of using artificial intelligence in automatically detecting, classifying and prescribing non-conformities in large asset data sets. | |
| Abstract | Asset Data Intelligence works by processing Equipment data stored in SAP S/4 HANA and associated Characteristic data. The aim is to identify data outliers and quality issues using artificial intelligence (AI), and optionally to incorporate rule-based validation. The results of the data processing are then exposed to business-users either directly in the data maintenance process in SAP S/4 HANA, or after the fact in the form of business intelligence reports. | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 01/11/23 | |
