To what extent is it possible to predict the failure of machinery? Can the industry entrust its fate to artificial intelligence? These questions arise around the implementation of AI-based technology in a sector such as industrial, and the answer is more than positive.
Predictive maintenance is a goal that has always been the utopia of any company: to know when a part of the production process will fail to solve the problem before it occurs. Two similar procedures can be distinguished in terms of objectives but far in terms of precision, which are predictive maintenance and preventive maintenance.
This is a work methodology that has been carried out since the industrial beginnings. It is based on establishing a schedule to perform maintenance tasks on the machinery and thus continuously ensure that it does not fail. Preventive maintenance does not take into account whether or not this task is necessary, so staff can perform unnecessary work or do it when it is too late.
“Predicting” in itself is one of the most difficult acts for human beings that, however, throughout history he has always tried to carry out (with little success). Fortunately, artificial intelligence can set accurate predictions and tell the company when it is time to do maintenance tasks.
This technology is capable of determining the state of a type of machinery by analyzing all the data it gives off. The advantages for the company are to have a smaller inventory of spare parts, prevent any unexpected failure at crucial moments, have the facilities less time stopped and, above all, to be more effective in terms of maintenance costs saving hours of work.
How to achieve predictive maintenance with AI
The fundamental part of predictive maintenance is the accurate analysis of data collected from different sources. In the case of machinery, we know that they have dozens of sensors, processors and warnings. The key is to combine all this data under the same system, which intelligent algorithms are in charge of analyzing to show where the problem may be.
Support for the Internet of Things (IOT)
Probably the best feature of Artificial Intelligence is that of being able to “mix” different types of data and sources and process them together to draw common conclusions. For example, following the example of predictive maintenance, let’s analyze the environment of a food processing tape. This machine can be affected by:
- Operating system problems (software)
- Physical problems in your gears
- Problems with the environment (too cold or hot)
- Problems due to human misuse
All these different sources of information can be processed and analyzed by Artificial Intelligence, which facilitates a common response on their maintenance in a predictive way, that is, before the failure occurs.
Phases within predictive maintenance
Get the “clean” data and work on it
Two same predictive maintenance technologies cannot be designed. Depending on the industry, the machinery or the conditions that surround it, one data or another will be more important. Here, correct communication between the company and the developers of the project comes into play so that it has a good adaptation to the real environment.
Teach how to predict
Once we have made it clear what the system needs, it must be given a function, in short, to give real use to that data. In this case, it is to predict. To do this, the system has to learn based on experience and this is achieved with:
- History of repairs and maintenance tasks: Both when and why that machine has needed a repair.
- What conditions is the machine in? Whether or not it is exposed to a lot of work, it can mark aging patterns necessary to know the maximum life of it.
- Human analysis: Digital data is as important as the empirical data collected by people to realize the full potential of artificial intelligence.
- Technical elements of the machine: When it was manufactured, when it began to be used, etc.
Visualize the analysis and act accordingly
The process we have just described needs the most important part that is to turn complexity into simplicity, so that the person in charge of maintenance ultimately knows what he has to do. This is achieved through interfaces that show results and conclusions drawn from the machinery that indicated what fails and how to solve it.
Are we facing the fourth industrial revolution?
At the historical level, industrial revolutions have always marked the evolution of society as a whole. They have brought with them important changes that have undoubtedly improved people’s lives. In this case, we can be facing a fourth revolution where human labor (and the cost it entails for the company) has been reduced as purely necessary.