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Reliability Engineering Principals

Reliability engineering principals define dependability of a product in its life cycle. Reliability, or dependability, refers to the ability of a system or component of a system to work under certain conditions for a specific period of time.

Reliability Engineering Principals

By definition, reliability is the probability of success, as the frequency of failures. Reliability engineering includes Testability, Maintainability, and Maintenance. Reliability is a key element of cost-effective analysis of a system.

Reliability vs Quality

Some people might confuse the quality with reliability. While they are related to each other, they carry complete separate concepts.

Quality is related to labor-work and manufacturing. Hence, if a product doesn’t work, it means the quality is low.

Reliability is the quality over time. If a product’s parts wear-out before they are expected to, then that would be a reliability issue.

Therefore, the difference between the quality and reliability is associated with time and, to be more specific, with product lifetime.

Reliability engineering contains both aspects of quantitative and qualitative analysis. While only measuring the reliability of a product doesn’t lead to a reliable product, it is necessary to design and produce the product in reliability that makes a reliable product.

What Is the Role of Reliability Engineering?

The main role of the reliability engineering is to identify the risks and manage them. Reliability engineering role includes:

  • Loss elimination
  • Risk management
  • Life Cycle Asset Management (LCAM)

Loss Elimination

  • Identifying the production losses and high maintenance cost assets
  • Searching for the ways to reduce those costs and losses
  • Developing a plan to remove or reduce the losses through root cause analysis
  • Implementing the plan

Risk Management

As stated above, another role of reliability engineering is to identify and reduce the risks that could negatively affect the production or business.

Tools can be used to identity and decrease the risk are:

PHA – Preliminary hazards analysis

FMEA – Failure modes and effects analysis

CA – Criticality analysis

SFMEA – Simplified failure modes and effects analysis

MI – Maintainability information

FTA – Fault tree analysis

ETA – Event tree analysis

Failure rate and mean time between/to failure (MTBF/MTTF)

Quantitative reliability measurements determine the rate of failure relative over time. They simulate the failure rate in a mathematical distribution to understand the quantitative properties of failure.

The most basic building block is the failure rate, which is measured by the following equation:

1

Where:

λ = Failure rate (aka the hazard rate)

T = Total running time/cycles/miles over an investigation period for both failed and non-failed scenarios.

r = The total number of failures occurring in the investigation period.

Another basic concept is the mean time between/to failure (MTBF/MTTF).

MTBF is used when items are repaired after they failed. MTTF is used for items that are thrown away and replaced.

The calculation is the same for both, which is the reciprocal of the failure rate function.

It is calculated using the following equation:

2

Where:

θ = Mean time between/to failure

T = Total running time/cycles/miles over an investigation period for both failed and non-failed items.

r = The total number of failures occurring in the investigation period.

The failure rate is a fundamental element of complex reliability calculations. Whether it is a mechanical/electrical design, operating context, environment and/or maintenance effectiveness, a machine’s failure rate as a function of time may drop, stay constant, increase linearly or geometrically.

The ‘Bathtub’ Curve

Even if you don’t have a strong math and engineering background, you might be familiar with the Gaussian or normal distribution, associated with the bell-shaped probability density curve.

The Gaussian distribution is typically used in data sets where central tendency, mean, and median are almost equal. However, the Gaussian is not the major distribution used in reliability engineering. Although the Gaussian is used to evaluate the failure features of machines with a dominant failure mode, it is not the primary distribution used in reliability engineering. The primary distribution in reliability engineering is the exponential distribution.

The reliability and failure characteristics of a machine are evaluated with the much-maligned “bathtub” curve, demonstrating the failure rate vs. time. In theory, the bathtub curve essentially shows a machine’s three basic failure rate characteristics: declining, constant or increasing. However, the bathtub curve has been bitterly criticized by the maintenance engineering studies, as it is a poor demonstration when it comes to modeling the characteristic failure rate of most industrial machines.

The Exponential Distribution

The exponential distribution is the most fundamental and broadly used prediction formula reliability, models machines with the constant failure rate, or the flat section of the bathtub curve. Following is the basic equation for evaluating the reliability of a machine based on the exponential distribution, where the failure rate is constant as a function of time.

3

Where:

R(t) = Reliability estimate for a period of time

e = Base of the natural logarithms (2.718281828)

λ = Failure rate (1/MTBF, or 1/MTTF)

The probability density function (pdf), or life distribution, is an equation that estimates the failure frequency distribution. It yields the familiar bell-shaped curve in the Gaussian, or normal, distribution. Following is the pdf for the exponential distribution:

 

4

Where:

pdf(t) =Life frequency distribution for a given time (t)

e = Base of the natural logarithms (2.718281828)

λ = Failure rate (1/MTBF, or 1/MTTF)

Weibull Distribution

Although Weibull is called a distribution, it is essentially a tool for reliability engineers to illustrate the probability density function (failure frequency distribution) of a set of failure data. The result is used to recognize the failures as early life, constant (exponential), or wear out (Gaussian or log normal).

β coefficient is used to adjust the failure rate equation as a function of time, which gets us to the following general equation:

5

Where:

h(t) = Failure rate (or hazard rate) for a given time (t)

e = Base of the natural logarithms (2.718281828)

θ = Estimated MTBF/MTTF

β = Weibull shape parameter from plot.

And, the following reliability function:

6

Where:

R(t) =Reliability estimate for a period of time, cycles, miles, etc. (t)

e = Base of the natural logarithms (2.718281828)

θ = Estimated MTBF/MTTF

β = Weibull shape parameter from plot.

And, the following probability density function (pdf):

7

Where:

pdf(t) =Probability density function estimate for a period of time

e = Base of the natural logarithms (2.718281828)

θ = Estimated MTBF/MTTF

β = Weibull shape parameter from plot.

*Note that when the β equals 1.0, the Weibull distribution takes the form of the exponential distribution.

Reliability Engineering Principals’ Topics of Training

  • Reliability principles
  • Mean time between failures indices
  • TPM
  • Reliability data preparation for analysis
  • Corrective actions
  • Models & Monte Carlo simulations
  • Pareto distributions
  • Fault tree analysis
  • Design review
  • Load/strength interactions
  • Software reliability tools
  • Sudden death and simultaneous testing
  • Reliability growth models and displays
  • Reliability policies and specifications
  • Reliability audits
  • Bathtub curves for modes of failure
  • Effectiveness: availability, reliability, maintainability, and capability
  • Weibull, normal, & log-normal probability plots
  • Decision trees merging reliability and costs
  • Critical items significantly affecting safety/costs
  • Failure mode effect analysis
  • Quality function deployment
  • Mechanical components testing for interactions
  • Electronic device screening and de-rating
  • Reliability testing strategies
  • Accelerated testing
  • Failure recording, analysis, and corrective action
  • Contracting for reliability
  • Management’s role in reliability improvements

TONEX Reliability Engineering Training Courses

TONEX offers a variety of training courses in the field of reliability engineering. To find more details, you can click on the name of the course to visit each course’s page:

Reliability Engineering Principals

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