RAMS and Energy Sector

RAMS, Big Data and AI in the Energy Sector

RAMS is the acronym from the English translation: reliability, availability, maintainability and safety. It is used for assess the non-functional quality of a system.

The complexity of railway systems and society's increased expectations regarding the level of risk accepted have been reflected in the need for further action to ensure, in the first place, the Safety, of both people and infrastructure.

A bit of history...

RAMS was used for the first time in the aeronautical industry. Subsequently, it began to be used in the transport industry, in particular in the automotive and railway industries. Since then, it has continued to be used in many other industries.

The RAMS of a system can be described as a indicator qualitative and quantitative confidence of a system, or the subsystems that make it up. An analysis is carried out with the objective of ensuring that the systems operate safely and with high availability. These analyses consist of determining the failure and operational rates available during the complete life cycle of the project.

In most projects, the evaluation system only includes reliability, availability and maintainability (RAM). However, safety (S) must be fully integrated into the assessment. 

In critical systems, from a security or availability point of view, trust is the most important property of a system, as it implies that such a system will not fail in normal operation. This concept can therefore be extrapolated to any industry.

The contribution of RAMS analysis to technological developments has grown exponentially, offering methodological innovations which have contributed to providing solutions on multiple axes (human, industrial, etc.).

Research and Development has also grown this termThere have been more than a few publications that show the way to apply RAMS studies towards new types of products, services and applications under various conditions.

At this point, it is important to remember that there is no universal method for such analyses. We advance new methods based on statistical data on the components and using mathematical models to synthesise reliability.

In this context, RAMS faces new challenges and technology, due to the complexity and non-linearity of the systems and processes for which our team is preparing.

Application in the Energy Sector

The extrapolation of this RAMS methodology to the electricity distribution sector, is not yet widespread. By studying the needs of our clients we can highlight among these challenges:

  • Predicting future values in time series (e.g. remaining lifetime of installed electrical equipment or estimating future electricity demand)
  • Large number of connected equipment sending multiple signals from on-board sensors

Data-driven approaches can be based on Artificial Intelligence techniques (neural networks and fuzzy logic). Changes can be treated as a time series. However, the complexity and non-linearity of the process pose strong challenges to standard methods of time series analysis.

The creation of useful RAMS models for our clients will involve taking into account a large number of variables, with complex relationships between them:

  • The machinery (type, number of machines, age, relative arrangement of machines, arrangement of components in the machine, inherent defects in components)
  • The operating conditions (nominal stress or overstress, varying temperatures, unexpected loads)
  • The human factor (skill level and number of operational staff, working habits, interpersonal relations, absences, safety measures, environmental conditions, hazardousness of assigned tasks, incidents/accidents)
  • The maintenance conditions (the competence and skill of maintenance personnel, attendance, work habits, safety measures, defects introduced by previous maintenance actions, the effectiveness of maintenance planning and control)
  • The infrastructure (spare parts, consumables, common and special tools)

The increase in the number of sensors, as well as their reliability, allows for continuous monitoring of component status, which in turn encourages interest in data-driven analysis techniques.

Finally, mention should be made of other aspects where RAMS analyses can play an important role: the safety of people, nature and the environment.


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