Nowadays, it is very common to use Big Data in the maintenance of infrastructure. This is due to the fact that in recent years, the proliferation of information technologies has grown exponentially. This technology is used to obtain and exploit the data obtained from pumping wells, permanent lighting, traffic or atmospheric conditions.
In this article, we’ll show you how this valuable tool has revolutionised the way data is handled in the operation of the M30’s infrastructure.
Big Data is the ability to extract and exploit data in order to obtain information. This information can be very valuable for the M30 and its exploitation has become even more productive ever since this method is now being used.
Three phases must be differentiated within this process. We’ll go into more detail below.
This phase collects everything that has do with reality, such as temperatures, times when engines are started, status of fires, or the condition of traffic lights.
In this phase, the most complicated part has to do with the reliability of the collected data. Maintenance teams play a huge role in making sure this data is reliable. If it so happens that this data is in fact not reliable, then the next two phases would be carried out using false data and would lose all usefulness.
This is the most innovative part of the system. This phase creates a model that accurately reflects the real situation, that is, it establishes the causes and effects of all the data that has been collected in the previous phase in a simulated way. The ultimate goal is to establish patterns that are realistic. This allows forecasts to be implemented with a high probability of them occurring in real life. An example would be the amount of time it takes to get from A to B, or the expected electricity consumption within a specific period of time.
Something that is extremely important in the third phase is the detection in real-time of anomalous behaviour in the systems that are present on the M30. This makes it possible to create previous alarms in order to act far enough in advance in the event of any unforeseen event.
This aspect completely changes the way maintenance work is carried out. Reducing intrusive preventive maintenance and evolving towards a predictive type of maintenance that eliminates a large part of the emergency corrective actions.
The conclusions obtained in the second phase are recorded in other databases. This enables said information to be accessible to the rest of the organisation, to other companies or to the public through web services or internal applications.
There are several phases within this process:
The first is the establishment of information with field data. Situations can be included within this phase such as the opening of a door, a call from a SOS button, or the start of a fan’s engine.
The second is the establishment of algorithms that can deal with the automatic management of equipment. This requires data engineering. Some examples are ventilation algorithms or warning algorithms by fire alarms.
The third and last situation is the establishment of behaviour patterns based off the analysis of historical data. Comparing this data with real-time data is essential when it comes to the operator’s maintennance work or the use of the infrastructure.
In short, the Big Data process represents a breakthrough for the operation and maintenance of the M30. It offers the ability to optimise the mobilised resources and improve the infrastructure on an ongoing basis.
