Abusing the word "Reliability" was an annoying thing for me, it's not linked to submission date of a document nor the training programs, yes these procedure can help in undirect way to improve the reliability, but when you consider your reliability program sole on it, then you are not doing reliability anymore.
So i decided to express my anger in peaceful way and i hope it can be a postive too.
for that i'll start to write a post and i'll call it "Real Reliability" to bust the myth around reliability, and i'll start with my first enemy "MTBF".
This for all the fed up guys from the wrong usage of "Reliability"
2. Measurement can help us to answer the followings
question:
Are we doing good or bad ?
Is our performance increasing or decreasing ?
Which unit is performing better ? (Benchmarking)
What/How to improve ?
“You can’t improve what you can’t measure”
Why are we measuring things ?
3. 1. MTBF is a misleading indicator.
Example: 1000 Units, one unit fail @ 1 Hour, MTBF = 1000 Hours
1 Unit fail @ 1000 hours, MTBF = 1000 Hours
Is it the same ?
First of all:-
How you shall not measure reliability!!!
𝑴𝑻𝑩𝑭 = 𝜽 =
𝑶𝒑𝒆𝒓𝒂𝒕𝒊𝒏𝒈 𝑯𝒐𝒖𝒓𝒔
# 𝑭𝒂𝒊𝒍𝒖𝒓𝒆𝒔
How to measure reliability ?
4. 2. MTBF Can’t be used for benchmarking.
Example:
SYSTEM #2 seems to be performing better
First of all:-
How you shall not measure reliability!!!
𝑴𝑻𝑩𝑭 = 𝜽 =
𝑶𝒑𝒆𝒓𝒂𝒕𝒊𝒏𝒈 𝑯𝒐𝒖𝒓𝒔
# 𝑭𝒂𝒊𝒍𝒖𝒓𝒆𝒔
How to measure reliability ?
SYSTEM January February
SYSTEM #1 150 𝐻𝑜𝑢𝑟𝑠
6 𝐹𝑎𝑖𝑙𝑢𝑟𝑒𝑠
MTBF = 25 690 𝐻𝑜𝑢𝑟𝑠
15 𝐹𝑎𝑖𝑙𝑢𝑟𝑒𝑠
MTBF = 46
SYSTEM #2 540 𝐻𝑜𝑢𝑟𝑠
18 𝐹𝑎𝑖𝑙𝑢𝑟𝑒𝑠
MTBF = 30 300 𝐻𝑜𝑢𝑟𝑠
6 𝐹𝑎𝑖𝑙𝑢𝑟𝑒𝑠
MTBF = 50
5. 2. MTBF Can’t be used for benchmarking.
Example:
But not really.
Any sense ?
First of all:-
How you shall not measure reliability!!!
𝑴𝑻𝑩𝑭 = 𝜽 =
𝑶𝒑𝒆𝒓𝒂𝒕𝒊𝒏𝒈 𝑯𝒐𝒖𝒓𝒔
# 𝑭𝒂𝒊𝒍𝒖𝒓𝒆𝒔
How to measure reliability ?
SYSTEM January February TOTAL
SYSTEM #1 150 𝐻𝑜𝑢𝑟𝑠
6 𝐹𝑎𝑖𝑙𝑢𝑟𝑒𝑠
MTBF = 25 690 𝐻𝑜𝑢𝑟𝑠
15 𝐹𝑎𝑖𝑙𝑢𝑟𝑒𝑠
MTBF = 46 840 𝐻𝑜𝑢𝑟𝑠
21 𝐹𝑎𝑖𝑙𝑢𝑟𝑒𝑠
MTBF = 40
SYSTEM #2 540 𝐻𝑜𝑢𝑟𝑠
18 𝐹𝑎𝑖𝑙𝑢𝑟𝑒𝑠
MTBF = 30 300 𝐻𝑜𝑢𝑟𝑠
6 𝐹𝑎𝑖𝑙𝑢𝑟𝑒𝑠
MTBF = 50 840 𝐻𝑜𝑢𝑟𝑠
24 𝐹𝑎𝑖𝑙𝑢𝑟𝑒𝑠
MTBF = 35
6. 3. MTBF is time independent.
Example: 12 failures over 12 months, MTBF = (365/12) = 30.4
MTBF = 30.4
First of all:-
How you shall not measure reliability!!!
𝑴𝑻𝑩𝑭 = 𝜽 =
𝑶𝒑𝒆𝒓𝒂𝒕𝒊𝒏𝒈 𝑯𝒐𝒖𝒓𝒔
# 𝑭𝒂𝒊𝒍𝒖𝒓𝒆𝒔
How to measure reliability ?
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12
F1 F2 F3 F4 F5 F7F6 F8 F9 F10 F11 F12
7. 3. MTBF is time independent.
Example: 12 failures over 12 months, MTBF = (365/12) = 30.4
MTBF= 30.4, But the failure rate is
increasing?
First of all:-
How you shall not measure reliability!!!
𝑴𝑻𝑩𝑭 = 𝜽 =
𝑶𝒑𝒆𝒓𝒂𝒕𝒊𝒏𝒈 𝑯𝒐𝒖𝒓𝒔
# 𝑭𝒂𝒊𝒍𝒖𝒓𝒆𝒔
How to measure reliability ?
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12
F
1
F
2
F
3
F
4
F
5
F
7
F
6
F
8
F
9
F
1
0
F
1
1
F
1
2
8. 3. MTBF is time independent.
Example: 12 failures over 12 months, MTBF = (365/12) = 30.4
MTBF= 30.4, But the failure rate is
decreasing?
First of all:-
How you shall not measure reliability!!!
𝑴𝑻𝑩𝑭 = 𝜽 =
𝑶𝒑𝒆𝒓𝒂𝒕𝒊𝒏𝒈 𝑯𝒐𝒖𝒓𝒔
# 𝑭𝒂𝒊𝒍𝒖𝒓𝒆𝒔
How to measure reliability ?
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12
F
1
F
2
F
3
F
4
F
5
F
7
F
6
F
8
F
9
F
1
0
F
1
1
F
1
2
9. 3. MTBF is time independent.
Example: 12 failures over 12 months, MTBF = (365/12) = 30.4
MTBF= 30.4, But the failure rate is
decreasing? When to plan PMs ?
First of all:-
How you shall not measure reliability!!!
𝑴𝑻𝑩𝑭 = 𝜽 =
𝑶𝒑𝒆𝒓𝒂𝒕𝒊𝒏𝒈 𝑯𝒐𝒖𝒓𝒔
# 𝑭𝒂𝒊𝒍𝒖𝒓𝒆𝒔
How to measure reliability ?
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12
F
1
F
2
F
3
F
4
F
5
F
7
F
6
F
8
F
9
F
1
0
F
1
1
F
1
2
10. 4. MTBF considering normal distribution, is your data so ?
Example:
But first, how different distribution can
make different result/decision ?
First of all:-
How you shall not measure reliability!!!
𝑴𝑻𝑩𝑭 = 𝜽 =
𝑶𝒑𝒆𝒓𝒂𝒕𝒊𝒏𝒈 𝑯𝒐𝒖𝒓𝒔
# 𝑭𝒂𝒊𝒍𝒖𝒓𝒆𝒔
How to measure reliability ?
11. Here is the beautiful normal
distribution AKA Bell shape.
Where MEAN = MEDIAN = MODE
The normal distribution
MEAN TIME BETWEEN FAILURES
So we are talking about the mean, and our
X-axis is time, and Y-Axis is failures
12. Here is the beautiful normal
distribution AKA Bell shape.
Where MEAN = MEDIAN = MODE
IS YOUR DATA FOLLWING THE
NORMAL DISTRBUTION ?
The normal distribution
MEAN TIME BETWEEN FAILURES
So we are talking about the mean, and our
X-axis is time, and Y-Axis is failures
13. Here is the beautiful normal
distribution AKA Bell shape.
Where MEAN = MEDIAN = MODE
IS YOUR DATA FOLLWING THE
NORMAL DISTRBUTION ?
Let’s see
The normal distribution
MEAN TIME BETWEEN FAILURES
So we are talking about the mean, and our
X-axis is time, and Y-Axis is failures
14. Let’s say we are studding
the failure of lightbulb,
we have a group of 100
bulb, and we are running
in the constant failure
rate part of the bath
curve (Phase 2)
The normal distribution
15. Let’s say we are studding
the failure of lightbulb,
we have a group of 100
bulb, and we are running
in the constant failure
rate part of the bath
curve (Phase 2), we’ll
assume this rate = 1%,
Remember
MTBF = 1/failure rate
MTBF = 1/1% = 100
MTBF = 100
The normal distribution
16. Let’s say we are studding
the failure of lightbulb,
we have a group of 100
bulb, and we are running
in the constant failure
rate part of the bath
curve (Phase 2), we’ll
assume this rate = 1%,
Remember
MTBF = 1/failure rate
MTBF = 1/1% = 100
MTBF = 100
So half of the population
should be failed by the @
100 hours
Let’s try it
The normal distribution
17. The data points will
followings:-
100 – 1% = 99
99 – 1% = 98.01
98.01-1%= 97.02
97.02 – 1% = 96.05
And so on…
@ 100 hours we left with
37 units…
But why ? We suppose to
get MEAN=50 unit ???
Simply because the failure
pattern unfirming an
exponential distribution.
For exponential :
MEAN ≠ MEDIAN ≠ MODE
But is everything followings
exponential pattern ?
NO
EVERY FAILURE MODE HAVE
IT’S UNIQUE DISTRBUTION
SHAPE.
The normal distribution
0
20
40
60
80
100
120 1
13
25
37
49
61
73
85
97
109
121
133
145
157
169
181
193
205
217
229
241
253
265
Units
Time in Hours
@ 100 hours only 37 units survives
18. If you think MTBF is not the wright way
to measure reliability then stay toned
for the upcoming post.
Salam
Ammar Alkhaldi, CSSBB
So how to measure reliability then ?