#
Reliability Engineering: the R of RAMS

Nowadays, it is
essential that the assets of any company, organisation or institution are
reliable in order to compete in an increasingly competitive and hostile market,
where costs are often the most important variable for success of the company.
For this reason, it is necessary to know and study in depth the elements
related to the **R of RAMS,
Reliability.**

To this end, we
define the concept of **Reliability**
as the capacity to correctly execute a function sustained over time. The **reliability **is related to
production, cost, human safety, and environmental prevention. Monitoring **reliability **is particularly
important to efficiently manage any service or system.

Today's assets are
increasingly complex, which is why it is essential to be able to assess and
calculate **reliability **from
the design stage and, thus, to maintain technical specifications in the best
operating conditions, throughout the life cycle.

Knowing **reliability **of an asset
allows us to quantify its "quality of service". Knowing **reliability **requires
information. However, it is a common problem not to have the operation data;
either because there is no experience in operating a system or because the data
are generally deficient, weak and, in some cases, non-existent. This situation
makes it difficult to know the **reliability **as
its calculation is practically impossible.

In mathematical
terms, we will say that reliability is the **probability **of an asset functioning **without failures **for a set
period of **time **and under
specific conditions.

It must be understood
that **reliability **will
adopt values between zero (0) and one (1). When **reliability **takes a value equal to zero (0), it verifies
the impossibility of that product performing its function correctly at that
moment. On the other hand, when **reliability **adopts
a value equal to one (1), it is certainly expected that this product will
perform its function properly.

It is important to
note that the **reliability (R****)** also depends on time (t), that
is, it varies over time. In addition, we must consider that its value decreases
over time. Thus, reliability models consist of probability functions, whose
random variable is time and usually presents a downward curve, starting from
its maximum value (1), when time is zero, and vice versa.

On the contrary, we
highlight the function of **Failure probability**
or **unreliability**, as
precisely the opposite meaning to the function of **Reliability**. The concept of **unreliability **is defined as
the probability of the occurrence of a **failure **in
a time interval.

If we know the real
behaviour of an asset, as a function of time, we can model **reliability**. Thanks to the
degree of technological advance in computer applications, it has become easier
to obtain practically real data of reliability, by means of automatic computer
modelling.

In practice, the two best known models are the following ones:

- Firstly, the
**Exponential Reliability Model**corresponds to the simplest model of**reliability**and assumes a**constant failure rate**. It is usually accurate for electronic components, and is generally accepted as the most widely used model, for obtaining a first approximation of the reliability value of any asset.

- Secondly,
**Weibull model reliability**is a more complete model than the**exponential model**, as it takes into account a**variable failure rate**. In data processing, this is more precise than exponential model since its parametric allows us to adapt to different trends of**failure**, working reasonably well without a large amount of data. Its versatility and ability to reflect circumstances relating to the assets under analysis has given rise to what is known as "**Weibull Analysis**". However, we stress that it entails a greater mathematical burden than the exponential model.

**Strategies
for reliability calculation**

There are a number of strategies and methodologies that allow us to make an efficient reliability calculation.

In the first
instance, **qualitative
methods** evaluate in a subjective way the characteristics of the
system, related to its behaviour. For example, the effect that the failure mode
of a particular element can have on a larger system.

Secondly, the **quantitative
methods** evaluate the numerical characteristics of the system
related to the probability of occurrence.

The most outstanding techniques and methodologies are the following ones:

**Failure Mode, Effects, and Criticality Analysis (FMECA)**provides very detailed qualitative information on the system.- The
**Fault Tree Analysis (FTA)**provides an overview of the system by relying on a graphical representation with fault models of all components, as well as faults or combinations of faults causing a breakdown in the system. - Event Tree Analysis (ETA).
**Markov**models.**Petri**nets.**Monte Carlo**analysis.

**At Leedeo Engineering, we are specialists in the development of RAMS Railway projects, applying CENELEC standards EN 50126, EN 50129, EN 50128, EU Implementation Regulation 402/2013 with the application of the Common Safety Methods CSM-RA, supporting any level required to RAM and Safety tasks, in the development and certification of safety products and applications.**

**Are you interested in our articles about RAMS engineering and Technology?**

Sign up for our newsletter and we will keep you informed of the publication of new articles.