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Modelling Condition-Based Maintenance Using Predictive Analytics
15 januari 2021 van 2:00 pm tot 3:00 pm
Predictive analytics techniques, such as Monte Carlo simulation, are becoming increasingly important tools in the selection and optimisation of maintenance strategies. The presentation will cover:
- Monte Carlo Simulation – What is it and how can we use it?
- Weibull Analysis – Talk about the bathtub curve the parameters of bathtub with reference to the failure rate, shape and location on the curve.
- Data Sources – Discuss historical failure and maintenance data from CMMS, data quality, library data
- Maintenance Optimisation Case Study – This will cover the practical application of reliability modelling on a critical asset (nutsche filter) from a speciality chemicals plant. It will discuss the approach and how we developed an optimised maintenance strategy at low cost and minimum business risk. Results of the case study will be seen and comparison between run to failure, current practice and optimisation will be seen. The resulting performance predictions will be demonstrated
- Predictive Failure libraries – The development of predictive failure model libraries are becoming more popular within organsiations looking to accelerate their continuous improvement project, with the ‘build it once use many times’ approach.
- Demonstrate how these tools can work together to help justify a change from time-based to condition based maintenance
- How to optimise a proposed condition-based task.
- How predictive analytics can support the low cost and low risk model within any industry