Services

Reliability Engineering & Asset Management

System Reliability, Availability, and Maintainability (RAM) Analysis

Kevcomp has developed a comprehensive method to identify the top contributors for Risk (Loss Production Opportunity Cost + Cost Of Maintenance + Environmental and Health effects) vs (un)reliability and Risk vs (un)availability for manufacturing sites using the reliability, availability, maintainability (RAM) process. 

Kevcomp models complex manufacturing systems and processes using reliability block diagrams (RBDs).  The RAM computations are obtained from Monte Carlo simulations for analysis to repairable systems and exact computations for analysis of non-repairable systems.

Kevcomp analyzes the RBD data and predicts the reliability and availability of the entire plant, process units, and equipment classes, along with predicted maintenance costs and loss profit opportunity costs.  This data forms the basis in the identification of equipment that are the top contributors that risk refinery profits. 

How Do We Do This?

The RAM process can be performed for an existing facility or to check the design future (Green Field) facticity.  Information about the equipment is required to make a comprehensive RBD model.  This data consists of the mean time between failures (MTBF), maintenance repair time and costs, and loss profits from equipment failure.  

The steps involved for an existing facility are: 

      • Kevcomp develops a list of all the manufacturing site’s equipment.  This is typically a download from the sites Computerized Maintenance Management System (CMMS).     
      • Kevcomp facilitates Data Validation Workshops with personnel from Operations, Maintenance, Inspection, and Engineering.  The purpose of the workshop is to validate the gathered information, discuss industry averages for missing data and fill in missing data with subjective data from knowledgeable individuals.
      • Input equipment data into modeling software and build reliability block diagram model.  
      • Report the RAM top 10/20/30 spreadsheets, tables, and graphs and RBD output results of plant, process unit, equipment class, etc. reliability and availability.

For a green field facility, The MTBF and the maintenance information is gathered from industrial equipment databases such as The Offshore and Onshore Reliability Data (OREDA), Lambda Predict, RealiabilityAnalyticsToolkit, SilSafe Data, GE Digital Solutions, Original Equipment Manufacturers (OEM),  and Equipment specific Subject Mater Experts.

Case Study

A major independent oil refiner experienced equipment failures soon after a major planned maintenance outage, referred to as a turnaround.  These failures were resource intensive in terms or personnel, maintenance dollars and caused unit outages that resulted in large loss of profits.

The refiner realized that they had the opportunity to address the equipment on the Turnaround, but it was not identified as a concern during the Turnaround planning process.

Kevcomp was asked to perform a RAM analysis of the entire refinery.  The RAM process predict future failures and identifies those items that have a higher probability of lost profits and maintenance repair costs.

An RBD model of the entire refinery was developed using the refineries P&ID’s to develop the layout of the RBD.  The model contained 5500 pieces of Fixed, Rotating, Instrumentation and Electrical equipment.  A Monte Carlo Simulation of was run to converge on the refinery’s reliability, availability and maintainability data.

The results was the identification of equipment that are the top contributors that put refinery’s profits at risk.  The refinery chose to do a Failure Modes and Effects Analysis (FMEA) of the top 30 items.

During their following turnaround, the refinery was able to reduce their projected profit loss by 28% .

Cost Perato by Unit
Cost Perato by Unit
Cost Category Pie Chart
Cost Category Pie Chart
Cost Ranking by Equipment
Cost Ranking by Equipment
Unreliability vs Risk
Unreliability vs Risk