{"id":6180,"date":"2016-03-24T20:03:57","date_gmt":"2016-03-25T01:03:57","guid":{"rendered":"http:\/\/www.livingreliability.com\/en\/?p=6180"},"modified":"2025-11-08T17:08:32","modified_gmt":"2025-11-08T22:08:32","slug":"take-the-eam-data-health-check","status":"publish","type":"post","link":"https:\/\/www.livingreliability.com\/en\/posts\/take-the-eam-data-health-check\/","title":{"rendered":"Take the EAM data health check"},"content":{"rendered":"\n
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If biological health depends on the quality of food we ingest a maintenance department will thrive on a diet of “good” data that it will convert to optimal decisions.
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Data analysis covers a vital role in maintenance management. Yet most managers doubt the analytic capability of historical data stored in their EAM work order systems. This article will assist the maintenance engineer in assessing and then improving<\/em> the effectiveness of his organization’s maintenance data procedures.<\/p>\n\n\n\n

Three types of data feed the maintenance decision process:<\/p>\n\n\n\n

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  1. Age data:<\/strong> Age data consists of\u00a0 records of failure mode instances, their working ages at the time of renewal, and whether maintenance was provoked by a) failure, b) potential failure, or c) a decision to renew a part or component that did not fail. The third of these failure mode ending event types is called a “suspension”.<\/li>\n\n\n\n
  2. Condition monitoring data:<\/strong> Internal variables such as vibration, oil analysis, temperature, and other measurements are selected to track the deterioration of a part or failure mode. External variables monitor the stresses on an asset that eventually lead to a failure or a “potential failure”. External and internal monitored variables constitute valid data sources for predictive maintenance.<\/li>\n\n\n\n
  3. Business data:<\/strong> While age and condition monitoring data can support a decision based on probability alone, only business data, specifically, the cost associated with a failure relative to the cost of its mitigation, will add the ability to attain an optimizing objective. Optimized maintenance procedures will accomplish, in a verifiable way, stated enterprise objectives such as maximum availability, minimum cost, maximum profitability, or a desired mix of objectives.<\/li>\n<\/ol>\n\n\n\n

    Current technology enables the acquisition of precise condition<\/em> and business<\/em> data. On the other hand, deficient age<\/em> data in the EAM captured via the work order remains the final obstacle to optimal maintenance decision making. We offer here a simple method to verify the quality of your EAM data with regard to its ability to support maintenance decision optimization. The analysis will often identify systemic causes<\/em> of inadequate data. The steps are:<\/p>\n\n\n\n

    A. Extract a sample of age data<\/a> covering a period of 3 or 4 or more years. The format of the data should be as follows:<\/h3>\n\n\n