You don't need to be a data scientist to model artificial intelligence. With the information you already have on the maintenance of your company, we integrate the CMMS with a powerful cloud platform capable of creating predictive models that facilitate planning and decision-making in maintenance.
We integrate your CMMS software with data analysis,
giving you the best of both worlds
Create Machine Learning models in a few clicks based on the data of your processes.
Anticipate the reliability value of your assets and discover when the failure will occur.
Take one step further in maintenance management using reliability in decision making to determine failure risk
We provide relevant information to carry out the following analyzes and make the best decisions.
Risk-based management
Reliability-focused maintenance
Failure modes and analysis effects
Reliability
Financial planning
Operation management
With Predictto you can synchronize your data from Fracttal or other external sources, visualize it quickly, create predictive models that suit you the best, and obtain simple and easy-to-understand forecasts.
Visualize the information of your assets synchronized with Fracttal and create predictive models with a simple and friendly interface
Degradation models
Live the Predictto experience in our interactive demo.
Let's suppose that you have an electric motor and you are interested in monitoring its vibration levels. With Predictto, it is possible
to calculate degradation models that will learn the vibration behavior of your engine and provide you with a forecast of the
future status of your asset. The results of your models will be presented as you see in the following graphs.
In the first graph, it is possible to observe the behavior of the vibration speed over time.
Yellow dots
They represent the vibration values measured in the electric motor, which our algorithm used to be able to learn how the asset was behaving until the moment of training the model.
Blue line
It corresponds to the adjustment that our algorithm found for the variable you are studying. It extends beyond the data so it is used to estimate how the variable will behave in the future.
Light blue shadow
The light blue shadow covers other probable values that the variable studied can take over time.
Green points
They represent mesured values of vibrations in the electric motor that were not used for the generation of the model. These points help to check if our predictions coincide with what actually happens.
There are different international regulations that establish limits for the vibration speed of different machinery,
depending on their type and use. It is possible that, according to your own experience, you determine that the vibrations
of your equipment must not exceed certain limits in order to function satisfactorily. This is why Predictto allows you
to enter the limits that you determine as most convenient for your particular asset. In the case of the example,
a limit of 7 (mm/s) was set, and it is possible to quickly visually assess when the engine will reach that limit.
In addition, by setting limits for your variable, you can obtain information regarding the reliability of the motor,
as seen in the second graph, since as the vibration gets closer to the established operating limit
the reliability of the motor decreases.
Know the useful life of your assets, allowing you to carry out an even more planned and intelligent maintenance management.