“The RELIABILITY of
an item, a part, a subsystem or a system is the probability that the item/part/subsystem/system performs a specified
function under specified operational and environmental conditions at and
throughout a specified time.” Quantitatively, this means that the reliability is the probability of
success. Usually expressed as Mean Time Between Failures (MTBF).
Reliability Engineering Training, once perceived as a practitioner or manufacturing issue, reliability and maintainability engineering is now considered a business issue of urgent priority. Reliability engineering is engineering that emphasizes dependability in the lifecycle management of a product. Dependability, or reliability, describes the ability of a system or component to function under stated conditions for a specified period of time.
Reliability engineering can be done by reliability engineers, design engineers, quality engineers, or system engineers.
The overall goal of reliability engineering is to make your product more reliable in order to reduce repairs, lower costs, and to maintain your company’s reputation. To best meet this goal, reliability engineering should be done at all levels of design and production, with all engineers involved.
Reliability and Maintainability Engineering training provides the concepts of reliability engineering, practical issues and methodology in reliability prediction, design and optimization.
This multidisciplinary training program focuses on the use of management systems, analysis techniques and advanced condition-based and preventive technologies to identify, manage and eliminate failures leading to losses in system function.
Maintainability is expressed as Mean Time To Repair (MTTR), where the ability of an item, under stated conditions of use, to be retained in, or restored to, a state in which it can perform its required function(s), when maintenance is performed under stated conditions and using prescribed procedures and resources.
Availability is the probability that a system is available for use at a given time- a function of reliability and maintainability.
Example of Reliability Models Include:
- Monte Carlo models
- Crow/AMSAA plots
- Electronic System Reliability
- Mechanical Components
- Systems Reliability
- Software Reliability
Reliability
engineering is engineering that emphasizes dependability in the
lifecycle management of a product. “Probability that a system will perform its intended
function for a specific period of time under a given set of conditions”
R = 1 – Pf Pf=
Probability of failure
Reliability is the probability that unsatisfactory
performance or failure will not occur
Example: Probability of an airplane engine failure working 16 hours a
day for 5 years is 0.000001 (it will perform its intended function for a 1
month under given set of conditions (working for 16 hours a day) before it is
due to the maintenance and inspection.
What is the reliability of the airplane engine? (probability
that unsatisfactory performance or failure will not occur within 1 month of
operation)
R = 1 – Pf
Pf= Probability of failure=0.000001
R=1-0.000001=0.999999
This is the probability that unsatisfactory performance or
failure will not occur within 1 month of operation. That means that airplane
engine has a 0.999999 probability of
working under given conditions.
Reliability engineers address 3 basic questions:
- When does something fail?
- failure rate
- mean time to failure
Why does it fail?
- Failure Modes and Effects Analysis (FMEA)
- Fault Tree Analysis (FTA)
- Reliability Block Diagrams (RBD)
- Mean Time to Failure (MTTF)
How can the likelihood of failure be reduced?
- redesign
- improved manufacturing processes
- maintenance & inspection
- training
In reliability engineering and reliability studies, it is
the general convention to deal with unreliability and unavailability values
rather than reliability and availability.
The numerical value of both availability and unavailability
are also expressed as a probability from 0 to 1 with no units.
The Availability, A(t), of a component or system is defined
as the probability that the component or system is operating at time t, given
that it was operating at time zero.
Predicting with some degree of confidence is very dependant
on correctly defining a number of parameters.
For instance, choosing the distribution that matches the
data is of primary importance. If a correct distribution is not chosen, the
results will not be reliable
Calculated failure rates for assemblies are a sum of the
individual failure rates for components within the assembly.
There are three common basic categories of failure rates:
- Mean Time Between Failures (MTBF)
-
- Mean Time To Failure (MTTF)
-
- Mean Time To Repair (MTTR)
Measuring Availability
Mean time to failure (MTTF)
Mean time to repair (MTTR)
MTBF = MTTF + MTTR
Availability = MTTF / (MTTF + MTTR)
Suppose a system crashes once per month, takes 10 min to
reboot.
MTTF = 720 hours = 43,200 minutes
MTTR = 10 minutes
Availability = 43200 / 43210 = 0.997 (~“3 nines”)
|
Availability %
|
Downtime per year
|
Downtime per month*
|
Downtime per week
|
|
90% ("one nine")
|
36.5 days
|
72 hours
|
16.8 hours
|
|
95%
|
18.25 days
|
36 hours
|
8.4 hours
|
|
97%
|
10.96 days
|
21.6 hours
|
5.04 hours
|
|
98%
|
7.30 days
|
14.4 hours
|
3.36 hours
|
|
99% ("two nines")
|
3.65 days
|
7.20 hours
|
1.68 hours
|
|
99.50%
|
1.83 days
|
3.60 hours
|
50.4 minutes
|
|
99.80%
|
17.52 hours
|
86.23 minutes
|
20.16 minutes
|
|
99.9% ("three nines")
|
8.76 hours
|
43.8 minutes
|
10.1 minutes
|
|
99.95%
|
4.38 hours
|
21.56 minutes
|
5.04 minutes
|
|
99.99% ("four nines")
|
52.56 minutes
|
4.32 minutes
|
1.01 minutes
|
|
99.999% ("five nines")
|
5.26 minutes
|
25.9 seconds
|
6.05 seconds
|
|
99.9999% ("six nines")
|
31.5 seconds
|
2.59 seconds
|
0.605 seconds
|
|
99.99999% ("seven nines")
|
3.15 seconds
|
0.259 seconds
|
0.0605 seconds
|
