deemed to be a repair by PIP if the inspection uncovers a failed
or failing component.
To make matters more complicated, defining failures in
real world environments can sometimes be challenging. If you
are running tests on light bulbs, it’s easy to know when failure
occurs. However, here are a few examples demonstrating the
difficulties in determining what is and what is not a failure:
1. You discover a seal has a one drip per hour leak. Is this a
seal failure? When did it start leaking?
2. During a planned pump inspection, you find the impeller has lost 50 percent of its thickness. Is it a failure? If so,
when did it pass the threshold from acceptable to unacceptable?
3. A pump vibration levels jump from .11-ips to 0.25-ips
from one pump inspection to the next. Management
wants to repair the pump before things get worse. Is this
a failure? When do you say it failed? Can you say it’s 80
percent failed when it was removed?
In summary, we can state that maintenance organizations:
• Have plenty of “dirty” work order data at their disposal.
• Don’t always know when a pump has failed or when it
would have failed if it is removed from service prematurely.
• Must subjectively cull their data to arrive at a usable listing
of repair data.
The Definitions
Now that we better understand the data analysis perspective of
maintenance organizations, let’s talk about the three definitions
of MTBF, MTBR, and MTBPM.
MTBF (Mean Time Between Failures): The mean
number of life units during which all parts of the item perform
within their specified limits, during a particular measurements
interval under stated conditions. When we say “all parts of the
item perform within specified limits,” we mean to say that on
average, no parts fail until the end of the mean life. The following equation is used to determine MTBF:
One thing maintenance folks (and accountants) know for
certain is when they have performed maintenance on a pump.
In addition, they store their maintenance data to the point of
information overload. Ask any maintenance engineer or specialist for pump maintenance data and he or she will present
you with reams of it. The problem is that it’s usually in a form
we call “dirty data.” Dirty data is an aggregate of predictive
maintenance, preventative maintenance, repair and extraneous
data that must be carefully culled before it is usable.
Let’s look at some sample pump data in Table 1.
We have run a hypothetical report of completed work
orders for Pump 101 over a 15-month period. Over that time,
we see there have been 14 completed work orders (a completed
work order is any work order that has been created and closed).
Does this mean we have experienced 14 failures or have completed 14 repairs? Certainly not.
Any experienced maintenance
person can look at Table 1 and
Date
determine which work orders represented real repairs, which ones were 5/26/2004
preventative or predictive maintenance activities, and those that 2
3
were unrelated to the pump, such 4
as the leaking suction valve. (By the 5
way, the lack of details in Table 1 is 6
typical of real-world data. We never 7
have all the details required to make 8
a fully-informed decision on the 9
true nature of maintenance events.) 10
Note that I have highlighted 11
two work orders in green that I
12 7/13/2005
believe represent actual repairs. So,
instead of 14 repairs we really only 13 7/20/2005
have 2 repairs required to return 14 8/1/2005
this pump to operative condition.
MTBF = N/F
where N is the number of machines in the populations and
F are the number of failures in the measurement period.
MTBR (Mean Time Between Repairs): The mean
number of life units between repair activities required to bring
all parts of the item back to within their specified limits, during
a particular measurements interval under stated conditions.
MTBR is similar to MTBF, but uses repair events instead of
failure events. The following equation is used to determine
MTBR:
MTBR = N/R
Completed
Work
Order #
1
Work Order Description
Notes
Replace leaking seal
Repair leaking suction
6/3/2004 valve
6/15/2004 PM – Change oil
9/18/2004 Replace oil leak
9/27/2004 Replace leaking seal
12/14/2004 Leaking gasket
10/29/2004 Bearing running hot
Electric motor making
12/20/2004 noise
4/5/2005 Check vibration
6/15/2005 Replace leaking seal
6/1/2005 PM – Check performance
Total
Cost
0
Repair?
No
Valve replaced
Oil leak stopped
Seal replaced
Temperature OK
1200
500
0
2,440.00
400.16
512.08
No
No
No
Yes
No
No
Leak stopped
0
511.06
440.6
176.24
No
No
No
No
Wear rings were
replaced
Pump won't start
Replace discharge
pressure gage
Replace leaking seal
Table 1
4,525.00
Yes
120
0
No
No
