{"id":4375,"date":"2015-03-11T13:55:01","date_gmt":"2015-03-11T18:55:01","guid":{"rendered":"http:\/\/www.livingreliability.com\/en\/?p=4375"},"modified":"2025-11-06T05:28:54","modified_gmt":"2025-11-06T10:28:54","slug":"objectivity-in-condition-based-maintenance-decisions","status":"publish","type":"post","link":"https:\/\/www.livingreliability.com\/en\/posts\/objectivity-in-condition-based-maintenance-decisions\/","title":{"rendered":"Objectivity in condition based maintenance decisions"},"content":{"rendered":"

I am interested in knowing more on your decision tool around CM (Condition Monitoring) prognosis and when to intervene in corrective maintenance.\u00a0 I want to look at different ways of removing subjective decisions and use more of an objective method given the complexity of information surrounding critical productive equipment.\u00a0 \u00a0As we have many mines around the world, I wanted to see if there was a tool that can be easily used to making decisions around maintenance intervention.<\/em><\/p>\n

You raise precisely the right themes, namely: 1. “objectivity” in the context of \u00a02. “complex” information” used in 3. “making decisions around maintenance intervention”. Let’s take them in sequence. Objective decision making flows from the analysis of information having two obvious yet critical characteristics: accuracy and relevancy. Obviously, we require accurate information on our asset’s observed\u00a0failure behavior if we wish to use that information to develop a practical decision procedure. The EAM was\u00a0supposed<\/em> to fill the accuracy\u00a0requirement. Yet many reliability analysts assert\u00a0that work order data lacks consistency, accuracy, and completeness. Accuracy\u00a0requires that the EAM provide the\u00a0types<\/em> of\u00a0data that a\u00a0Reliability Analysis (RA) needs for using an evidence based decision tool.<\/p>\n

A decision tool is a method for building a decision model<\/em>. A decision model\u00a0is a rule or algorithm for making recurring decisions. It is inconceivable that a maintenance analyst would\u00a0decide each\u00a0maintenance action by invoking first principles. He\u00a0does not have the time to\u00a0eyeball every CM graph, \u00a0perform a calculation, and arrive at\u00a0a considered decision on whether to intervene. Therefore\u00a0a model\u00a0will be\u00a0used to support or automate the\u00a0decision process. A model can invoke varying degrees of\u00a0objectivity from “gut feel” to full statistical rigor. The vital question, however, is this: “Does the model result\u00a0in decisions that, over the long run, support in a verifiable way the reliability, availability, and cost objectives set for an\u00a0asset?<\/p>\n

Begin by defining a CM decision. A condition based maintenance (CBM) decision invariably boils down to a recurring choice among three possibilities:<\/p>\n

    \n
  1. Intervene immediately and perform maintenance on a specific part or component.<\/li>\n
  2. Schedule, within a specified time, an intrusive inspection or specified maintenance on a component or part.<\/li>\n
  3. Defer the decision until the next CM observation.<\/li>\n<\/ol>\n

    Going one step further, we need to determine whether our decision process\u00a0<\/em>is optima<\/em>l? Does the process result in the best desirable balance between proactive and reactive maintenance so as to maximize some \u00a0objective, typically profitability.<\/p>\n

    Our initial axiom stated that the information used for constructing our\u00a0decision model must be\u00a0accurate and relevant[1<\/a>]<\/sup>. Accurate data in the EAM database depends on the technician’s:<\/p>\n