• Like

Estimating Failure Parameters in Reliability Analysis

  • 862 views
Uploaded on

How important is it to use Failure Parameters in Reliability Analysis and RAMS for major projects

How important is it to use Failure Parameters in Reliability Analysis and RAMS for major projects

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
862
On Slideshare
0
From Embeds
0
Number of Embeds
1

Actions

Shares
Downloads
22
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Empowering Maintenance & Reliability Decision Makers Estimating Failure Parameters Mick Drew – ARMS Reliability Engineers How important is it to use Failure Parameters in Reliability Analysis and RAMS for major projects? Answer: There are different types of Failure Parameters that can be used. There are failure parameters that are used to assess the reliability of a component or system, and there are failure parameters used to predict reliability of components or systems. Assessment: One of the most common parameters used to assess reliability is MTBF. Comparing the MTBF is an indicator of the time between failures and can also be used to calculate system Availability. The Mean Time Between Failures is calculated by:- Total Operating hours/No of Failures The MTBF can be used at equipment level or at System level. The inverse of the MTBF is the Failure rate. For new projects failure rates can be determined from existing plant, similar plant or there may be published data availability from various industry bodies. OREDA is often referred to for offshore oil industry, and the GADS database for more than 6,500 electric generating units. Other published data is available from Reliability Analysis Centre such as the NPRD handbook for non electronic parts. Of course the best data is from your own plant or similar plant. In the following I describe the analysis of failure data for four cases. © 2009 ARMS Reliability Engineers
  • 2. Empowering Maintenance & Reliability Decision Makers Case 1 Up Down 15 30 45 60 In case a system has four failures over the first 70 days of operation. The timeline is shown above. From the event log the following data is assembled and the MTBF is calculated from 44 operating days divided by 4 failures gives an mtbf of 11 days. Similarly the MTTR the plant is 6.5 days. Perform In service TTF Maintenance Duration 0 7 7 4 11 10 21 5 26 12 38 7 45 15 60 10 Total 44 26 Mean 11 6.5 A maintenance analysis finds the first failure was due to severe aging mechanism, whereby it was decided to perform a PM task every 15 days. The resulting timeline is shown in (Case 2) and the timeline shows the first failure eliminated. © 2009 ARMS Reliability Engineers
  • 3. Empowering Maintenance & Reliability Decision Makers Case 2 Up Down 15 30 45 60 In this case the MTBF has increased from 11 days to 18 days. A dramatic improvement. Or is it? When we include the planned outages in the calculation as shown in Case 3, the Mean Time Between Outages has reduced to only 6.7 days. So whilst the failures have reduced the downtime has actually increased. Case 3 Up Mtbo=47/7=6.7days Down 15 30 45 60 75 In Perform service TTF Maintenance Duration 0 15 15 2 PM 17 4 21 5 Repair 26 4 30 2 PM 32 6 38 7 Repair 45 0 45 2 PM 47 13 60 10 Repair+PM 70 5 75 2 PM Total 47 30 Mean 6.7 4.3 © 2009 ARMS Reliability Engineers
  • 4. Empowering Maintenance & Reliability Decision Makers Investigation work by the Engineering group finds a way to eliminate the Regular PM’S and design out two of the failures, but the repair time is now greatly increased. But the MTBF increases significantly to 18 days. Case 4. Up Mtbf=36/2=18days Down 15 30 45 60 75 In Perform service TTF Maintenance Downtime 0 21 21 24 45 15 60 26 Total 36 50 Mean 18 25.0 © 2009 ARMS Reliability Engineers
  • 5. Empowering Maintenance & Reliability Decision Makers Discussion of Results Comparison on the MTBF could indicate that the MTBF of case 4 means that Case 4 is the best case. Case 1 Case 2 Case 3 Case 4 MTBF (Days) 11 16 6.7 18 This is clearly not the case if one was at all concerned about the amount of downtime. In order to consider the downtime it is necessary to factor in the MTTR and calculate Availability for each case which is shown below. Availability = MTBF/(MTBF+MTTR) Case 1 Case 2 Case 3 Case 4 MTBF 11 16 6.7 18 Failure Rate 0.09 0.06 0.15 0.06 MTTR 6.5 7.3 4.3 25 Availability 62.9% 68.6% 61.0% 41.9% From this of course we see that Case 1 is actually better than Case 3 and Case 4 and Case 2 is invalid because the planned downtime was not taken into account. Conclusions • If you are concerned about number of unplanned outages- MTBF can be used as a guide. • If you are concerned about number of outages- MTBO can be used as a guide. • If you are concerned about minimising Downtime then Availability can be used as a guide. • It is very important to consider both planned and unplanned outages when assessing and comparing systems. • The use of a fixed time maintenance regime without consideration of operating time is far too conservative. • Major projects may start off with maintenance outage data from vendors but the actual outage times must be determined through maintainability studies that take into account logistics delays, sparing levels, diagnostic times. • What about Predicting performance? For the answer to that question we need to go to Reliability Parameters. © 2009 ARMS Reliability Engineers
  • 6. Empowering Maintenance & Reliability Decision Makers © 2009 ARMS Reliability Engineers