Heavy and industrial machineries play a fundamental role in operational and manufacturing processes as they perform critical tasks in continuous working mode.
Advanced real time analyses allow to understand their dynamics, capture patterns and ensure functional integrity in order to safeguard and optimize operations.
Monitoring machineries is very complex due to the challenges involved in acquiring the right data, which leads to falling back on extrapolating them from those that can be acquired in the real world.
Furthermore, data acquisition is carried out in a production or harsh environment and the signals result in being very noisy, which requires heavy data cleaning before attempting any kind of analysis.
Technical challenges include:
- Manual data cleaning requires a lot of experience and time;
- Developing models describing relations between hundreds signals is complex;
- The development of real-time methods to automatically detect machineries operational statuses requires several different technologies and expertise, including IoT, signal processing, artificial neural networks and web applications.
Our platform allows to easily create real-time systems capable of autonomously cleaning the data coming from machineries sensors and of translating their signals into the operating states.
This allows not only for a real-time overview about what is happening inside the machineries and a complete control over their operations, but also to get early warnings as soon as things start to change.
Technical solutions include:
- A software agent to easily connect several different sensor types, acquisition systems or databases to our platform;
- A centralised cloud platform to manage all data, analyses and reports;
- Automatic data validation methods to check incoming data quality (e.g. periodicity and cross-correlation analysis);
- Easy to use graphical interface to train complex models (no coding required);
- Real-time classification and anomaly detection algorithms based on complex AI models.