AI Solutions for Asset and Maintenance Master Data Management
Is your CMMS or EAM data complete and accurate? If you are like most asset-intensive companies, probably not.
NRX AssetHub provides asset-intensive companies with a powerful Data Quality Report to analyze data and quickly and easily identifies the gaps and inaccuracies in their CMMS data. It also reports on non-compliances and operational integrity issues. Our customers rely on our solution to highlight asset and maintenance data inadequacies, and provide easy to follow recommendations for creating high-quality data.
- To make sure all maintainable assets are correctly set up in their asset management systems including EAM/CMMS
- To capture asset information efficiently in the field during walkdowns
- To extract asset information from vendor documentation, P&IDs and other drawings
- To classify equipment correctly
- To recommend failure codes
- To extract spare parts information from purchase orders, invoices, vendor documentation, SPIR forms and other sources and recommend BOMs for equipment
- To standardize descriptions
- To extract task lists and work instructions from vendor documentation and other information sources
- To structure work order information for effective reliability analysis
At Hubhead our utilization of AI Powered Solutions allow us to reduce the cost of building and sustaining master data by 90%.
Capture Asset Information Efficiently During Walkdowns
Users can walk down plants and storerooms quickly, taking pictures of name plates, tags and parts information. AI can find and extract the relevant information from the pictures. An intuitive efficient UI is provided for reviewing and approving the information before it is automatically standardized and formatted to user specific standards for upload into your EAM/CMMS and other asset management systems.
Classification of Equipment
Correctly classifying equipment is essential to running an effective asset reliability program. It is also necessary to implement reliability centered maintenance (RCM) and for failure modes and effects analysis (FMEA) to deliver good results. AI can suggest equipment classifications based on equipment description, nameplate information, vendor documentation, and other sources of information. An intuitive efficient UI is provided for reviewing and approving the information before it is automatically standardized and formatted to user specific standards for upload into your EAM/CMMS and other asset management systems.
Configuring and Recommending Failure Codes
For each equipment classification, your EAM/CMMS and other asset management systems should be configured to have a reasonable number of failure codes associated with each equipment type. This is a best practice to ensure usable reliability information is captured by maintenance workers when completing work orders. AI can recommend appropriate failure code lists for all of your equipment types for approval before it’s used to configure your EAM/CMMS solution. This can be tailored easily to individual company standards. Of course, AI agents can also recommend failure codes to workers completing work orders and also structure all of the work order completion information for effective reliability analysis.
Extracting Information from P&IDs and Other Drawings
Engineers frequently need to extract information from P&IDs and other drawings for a number of purposes:
- Building asset registers and confirming maintainable items
- Risk based analysis of pipelines and vessels
- Estimating construction costs
- Building Digital Twins
Doing this work manually can be time consuming, expensive and error prone. AI can do most of the heavy lifting to save users time and reduce error rates. An efficient intuitive UI is provided to quickly QA the results and do any fine tuning. The results can automatically be standardized and formatted for loading into other solutions including EAM/CMMS solutions, reliability analysis solutions or construction estimating software.
Preparing Task Lists and Work Instructions
The amount of effort required to create good task lists and work instructions for EAM/CMMS systems was often prohibitive for asset intensive organizations with thousands of assets. Organizations tried to simplify the process by having simple reusable lists that could be applied against wide groups of equipment. But often the results were too simple to be useful to planners and maintenance workers or resulted in lots of unnecessary or low value preventive work orders. Our AI solution can extract detailed useful information efficiently from vendor documentation and other information sources. An intuitive simple UI allows users to review the results and make corrections and do fine tuning. The information can automatically be standardized and formatted for loading into EAM/CMMS and other asset management systems.