The document discusses reliability in mechanical engineering, defining it as the probability that a product can perform its function without failure over time. It highlights the importance of reliability engineering in design, testing, and maintenance to enhance longevity and minimize costs. Various parameters such as mean time to first failure (MTTFF), mean time between failures (MTBF), and mean time to failure (MTTF) are explained, along with probability distributions that model uncertainties affecting reliability.

1. LECTURE#5
“RELAIBILITY IN
MECHANICAL
ENGINEERING
DESIGN”
COURSE TITLE:
TECHNICAL ELECTIVE-II
MECHANICAL ENGINEERING
DESIGN
SEMESTER: VII
ENGR. RABIYA JAMIL
LECTURER
DEPARTMENT OF MECHANICAL ENGINEERING

2. WHAT IS RELAIBILITY?
• Reliability is the probability that a product does
not fail under given functional and
environmental conditions during a defined
period of time.
• Reliability includes the failure behavior of a
product and is therefore an important criterion
for product evaluation.

3. WHAT IS RELIABILITY ENGINEERING?
Reliability Engineering is a discipline within
engineering that focuses on ensuring a system or
product is dependable and performs its intended
function over time.
It involves designing, testing, and maintaining
systems to prevent failures, improve performance,
and increase lifespan.
The goal is to maximize reliability while minimizing
risks and costs associated with failures.

4. FACTORS WHICH INFLUENCE RELIABILITY

8. RELIABILITY METHODS IN PRODUCT LIFE CYCLE
 To achieve a high customer’s satisfaction, system
reliability must be examined during the complete product
development cycle from the viewpoint of the customer,
who treats reliability as a major topic.
 It is advantageous that all departments along the
development chain are integrated, since failures can
occur in each development stage.
 Methodological reliability tools, both quantitative and
qualitative, already exist in abundance and when
necessary, can be corrected for a specific situation.

9. RELIABILITY METHODS IN PRODUCT LIFE CYCLE

11. RELAIBILITY PARAMETERS
• MEAN TIME TO FIRST FAILURE:
• Definition:
MTTFF is the average time until the first failure occurs in a system,
often used to gauge the initial reliability of a system during its early
operational period. It is especially useful for evaluating newly installed
or recently manufactured systems.
• Key Characteristics:
• Evaluates the time to the first failure.
• Often used in reliability engineering during testing or commissioning
phases.
• Helps identify weaknesses in design, manufacturing, or initial
operations.

12. RELAIBILITY PARAMETERS
EXAMPLE:
Consider a new hydraulic press machine in a factory. During its
commissioning phase, the first failures are recorded as occurring
after 800, 900, 750, and 850 hours for four machines. The MTTFF
is:

13. RELIABILITY PARAMETERS
• Mean Time Between Failures (MTBF)
• Definition:
MTBF is the average time between failures for a repairable system. It
reflects how long a system operates in a fully functional state
between breakdowns. This metric is critical for scheduling
maintenance and improving system reliability.
• Key Characteristics:
• Applies to repairable systems (e.g., engines, CNC machines,
elevators).
• Accounts for operational uptime but excludes downtime for repairs.
• A higher MTBF indicates better system reliability.

14. RELAIBILITY PARAMETERS
EXAMPLE:
A CNC machine operates for 2000 hours before failure, is repaired,
and then runs for another 2500 hours before the next failure. If this
pattern is repeated across three cycles, the MTBF is:

15. RELAIBILITY PARAMETERS
• Mean Time to Failure (MTTF)
• Definition:
MTTF is the average time a non-repairable system or component is
expected to function before failing. It is a statistical measure used
primarily for components or devices that are replaced rather than
repaired when they fail.
• Key Characteristics:
• Applies to non-repairable components (e.g., bearings, seals, light bulbs).
• Indicates the reliability and lifespan of a product.
• Higher MTTF values imply better reliability and longer expected life.
• Measured in operational hours, cycles, or uses depending on the
application.

16. RELAIBILITY PARAMETERS
EXAMPLE:
Consider a disposable bearing used in a conveyor system. The bearing
is replaced once it fails. Suppose we test 5 bearings, and they fail after
1000, 1200, 1100, 1300, and 1400 hours of operation. The MTTF is:
MTTF helps designers choose reliable materials and components for
machines.

17. PROBABLITY DISTRIBUTIONS IN RELAIBILITY
• Probability distributions play a critical role in mechanical
engineering design by enabling engineers to model
uncertainties, predict system behavior, and make informed
decisions under varying conditions.
• In mechanical systems, factors like material properties,
loads, environmental conditions, and manufacturing
tolerances are inherently uncertain. Probability
distributions help model these uncertainties accurately.

18. NORMAL DISTRIBUTION
• Manufacturing tolerances are not uniform. For
instance, dimensional deviations of parts
typically follow a normal distribution, which
helps in statistical tolerance stack-up analysis
for assemblies.
• μ: Mean (center of the distribution).
• σ: Standard deviation (controls the spread of
the distribution).

19. EXPONENTIAL DISRTRIBUTION
• The exponential distribution models the time
between independent events occurring at a
constant rate. It is frequently used to describe
the time to failure of systems with a constant
failure rate, meaning the failure likelihood does
not change over time.
• Suitable for systems that don’t "age" or
degrade, such as electronics or software.

20. EXPONENTIAL DISTRIBUTION

21. WEIBULL DISTRIBUTION
• The Weibull distribution is a flexible
distribution used to model systems with varying
failure rates. It can represent early-life failures,
constant failure rates, and wear-out periods by
adjusting its shape parameter.
• Can model systems with increasing, decreasing,
or constant failure rates.
• Suitable for analyzing mechanical components
or systems with age-dependent reliability

22. WEIBULL DISTRIBUTION

23. RELAIBILITY GROWTH
• Reliability Growth refers to the systematic
improvement of a system's reliability over time
through design enhancements, testing, and
corrective actions. This process identifies and
rectifies failure modes in the system during the
development phase, thereby increasing its
reliability before deployment.

24. A TYPICAL RELAIBILITY GROWTH CURVE

25. RELAIBILITY OF SERIES SYSTEMS
• In a series system, all components must
function for the system to work. A failure in any
one component causes the entire system to fail.

26. RELAIBILITY OF PARALLEL SYSTEMS
• In a parallel system, the system functions as
long as at least one component works. A failure
in one component does not cause the entire
system to fail.

27. • Adding components in series reduces overall
system reliability.
• Series system is suitable for applications where all
parts must work for functionality, such as
pipelines or wiring.
• Adding components in parallel increases overall
system reliability.
• Parallel system is suitable for redundancy in
critical systems, such as power supplies or safety
mechanisms.