RAM-int includes four modules: Data Regression, System Analysis, Redundancy Optimization and Spare Parts Optimization.
- Data Regression: uses existing failure data to easily build models based on Weibull distribution, which predict when and why equipment items will fail
- System Analysis: simulates the RAM performance for the whole process, makes informed design decisions and selects the best Preventive Maintenance/Inspection interval
- Redundancy Optimization: optimizes the system redundancy subject to various objectives and constraints
- Spare Parts Optimization: finds the optimal spare parts management strategy by determining the type of spare parts needed, purchase timing, and quantity.
RAM-int is user-friendly and is designed so that chemical engineers, mechanical engineers and RAM engineers can easily model industrial processes such as those found in the chemical, petrochemical, refining and gas processing industries.
Its interactive interface provides flexible ways for the user to import historical data from EAM, CMMS, Microsoft Excel or other data sources to build a reliability database which can predict equipment failures. The software automatically sets-up a Reliability Block Diagram (RBD) which allows the user to understand how the reliability of individual equipment items affects the reliability of a whole processing system and so, make informed decisions on the approach to RAM strategy.
Uniquely, RAM-int contains comprehensive process integration technology which gives the user new insights in evaluating investment decisions as part of an overall RAM strategy. As a result, the user is able to make informed, cost-effective decisions on maintenance strategy, preventive or corrective maintenance, inspection intervals, redundancy and maintenance resourcing. For ease of understanding, (wherever possible) the software presents the results graphically. Detailed results are presented for different interests, such as management decision support information, process throughput and cost information, equipment and system reliability/availability information, planned and unplanned maintenance and condition-based maintenance information and maintenance resource usage information.
Module 1: Data Regression
Instructional videos for the remaining modules will appear in the near future.