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.
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 analysable 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.
Key Topics Covered:
Top Learning Objectives
- Overcome the natural uncertainty of the failure process
- Learn what data is relevant and how to verify that it is accurate enough for predictive performance
- Know how to transform the historical database from a black hole into which work order data is lost to analytic scrutiny into a knowledge resource for optimizing specific maintenance tactics
- Apply reliability theory to practice in such a way as to achieve verified optimal performance from day to day maintenance decisions
- Use RCM knowledge skills when transmitting field observations made during the execution of maintenance so as to continually improve the current maintenance plan
- Construct an analyzable data set for predictive modeling
- Transform information from your maintenance information management EAM/ CMM database
- 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 the new experience and deepening understanding of the asset's condition and age-based failure mode behaviour.
- Perform Reliability Analysis and build practical CBM decision models
- Assess proposed projects with life cycle cost RAM analysis
For more information about this training visit https://www.researchandmarkets.com/r/pbt3x2