Asset Breakdown Structure and Failure Codes for Optimizing Equipment Performance Data Quality

Volume 5

In the era where enormous amount of data is being collected, ISO 14224 provides the guideline on what data needs to be collected. In Volume 4, we covered the benefits of implementing Enterprise Asset Management. In this article, we provides a guideline for Asset Breakdown Structure and Failure Codes for optimizing equipment performance data quality.

Asset Hierarchy

With a structured approach meaningful data can be collected and analyzed to drive decisions. A typical asset standard should include at a minimum.

  • A taxonomy for the naming of all levels of the asset hierarchy, ranging from the Business level, down to the maintainable item.
  • A list of standard equipment classes and types needs to be defined
  • A list of required information for each asset by class and type. 
  • An asset numbering system.

The ISO 14224 taxonomy defines different levels at which data needs to be collected.

Figure 1: Asset Breakdown Structure

Levels 1 through 4 represent a high-level categorization that relates to Facility, plants and their sections. These levels are regardless of the equipment units involved.

Figure 2: Equipment Breakdown Structure

The Maintainable Item (MI) is the smallest component/ part that is repaired or replaced in the Equipment. The Failure Mode (FM) is one or more ways in which each MI can fail.

This way one can easily monitor:

  • Mean Time to Fail (MTTF) and Mean Time to Repair (MTTR) per Failure Mode (FM).
  • Failure Distribution per FM
  • Fixed vs Context data (Based on meta data: e.g. Product Medium, MI material, Temperature, Load)
  • Equipment and Maintainable Item (MI) Value & Age (Life Cycle Cost Analysis – LCCA)

Failure Codes

In order to improve failure data collection, the failure code library is to be developed and linked to the specific asset classes/sub-classes. The figure below illustrates the Failure Code hierarchy and information to be collected.

Figure 3: Failure hierarchy

Reliability Trends

With the asset hierarchy and relevant failure data collected, trends can be established across asset classes, similar processes, etc.  Artificial data analysis and data mining can be easily deployed to analyze trends:

Figure 4: Analyzing failure data to analyze trends

I have been closely working with Accrete Solutions and Efftronics Systems Pvt. Ltd. on Enterprise Asset Management. Accrete Solutions are leaders in SAP EAM implementations and have a long track record of success in the space, uniquely combining SAP EAM capabilities with a real world IoT device connectivity platform. Accrete has teamed up with Efftronics to offer a complete End-To-End Smart SAP EAM IoT enabled solution to customers.

Let us connect so I could assist in scheduling a presentation to showcase the implementation the IoT Platform which is managing 6+ million devices connected to 200+ command centers generating 22+ million records per day. Smart EAM IoT Platform can offer you measurable improvements in Asset Performance, Regulatory Compliance, Proactive Maintenance and increased Customer Satisfaction.

About Baljit Singh

Baljit Singh has been fortunate to be part of teams that have brought innovation and game changer product & services to the market. He has experience in building successful product and services companies. He is currently an advisor to companies for developing their innovative journey. He is a motivational speaker and gives talks on how to cultivate habits and change lifestyle to drive innovation. Baljit is currently driving cross border partnerships.

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For any further information, please contact Baljit Singh at [email protected]