Expensive critical equipment generates overwhelming volumes of data. Maintenance personnel try repeatedly, with little success, to interpret that data so as to predict when and where failure will next occur. Yet the maintenance engineer lacks systematic techniques with which to reduce vast amounts of data to clear confident maintenance decisions. Although maintenance technology vendors have long promised this capability so far none have delivered it.
The solution does in fact exist. Merely an incomplete understanding of the nature of data has impeded resolution of a central problem. The fundamental problem in maintenance is uncertainty in the prediction of failure. As a result of misunderstanding of the key role of maintenance data, vital failure mode instance attributes have eluded capture by conventional enterprise asset management (EAM) procedures.
The course “Achieving Reliability from Data” will impart to each trainee a comprehensive framework for acquiring data of adequate analytical quality. And then to extract the decisions from that data. And to verify that the decision is optimal. Specifically course participants will acquire vital skills with which to:
- Overcome the natural uncertainty of the failure process.
- Ensure the “right” data is captured on the work order, that it is consistent, and of analytical accuracy.
- Transform information from the EAM in combination with condition data into practical, model driven, unambiguous, optimal day-to-day maintenance decisions.
- Use the RCM methodology to configure a defensible initial maintenance plan.
- Update the RCM knowledge base in a living process so that the maintenance plan continuously reflects new experience and deepening understanding of the asset’s condition and age based failure mode behavior.
- Perform Reliability Analysis and build practical CBM decision models
- Assess proposed projects with life cycle cost RAM analysis
The three day “Achieving reliability from data” training course addresses the subject of uncertainty in maintenance thereby empowering trainees to make decisions based on statistical confidence. The course imparts a thorough treatment of RCM methodology with which to establish an initial maintenance plan. However the course goes further by extending RCM methods to daily work order related activity. The “living” RCM process accomplishes two important goals that have been neglected in current maintenance practice. The first goal is to ensure dynamic update of the RCM knowledge base. Experience of new failure modes and effects evolve, as does operating context. A living RCM process keeps the knowledge synchronized with reality. Secondly course participants will learn how to ensure that analyzable work order data is transferred accurately and completely from the field or shop to the EAM. Finally the course will provide the trainee with Reliability Analysis tools and skills with which to transform data into practical decision models that they will verify as having improved reliability, availability, and profitability within their enterprise.
Each participant will gain permanent access to a thorough set of slides and supporting text as well as educational versions of software that they will have used in the course exercises.
The course is delivered in three modules over three days:
Module 1 RCM
Module 2 Living RCM
Module 3 Reliability analysis and decision modeling
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