Data-Driven technologies to face complex technological challenges in the fields of control, diagnostic, prediction and data analytics.


Different Approaches

We developed four proprietary approaches to manage non linearity issues in modeling, prediction and automatic control of complex systems

Key Advantages 

  1. Development Time Reduction

2. Accuracy Improvement

3. Easy Integration

4. Fast Tuning

Modelway Proprietary Technologies

Virtual Sensing

Estimate info unavailable on-board

Models and Predictions

Digital Twin building

New Generation Controls STC

Direct calibration from data

Advanced Data Analytics

Info Analysis, Retrieval & Detect

Modelway’s product development is a 4 steps process that integrates Data Analysis and Machine Learning methods

  1. Specification and Data Analysis

2. Solution Concept

3. Algorithm Testing and Validation

4. Custom Algorithm Engineering


Technology Overview

Often in industrial systems/process prohibitive physical conditions or high cost of sensors can cause difficulties in performing physical measurements. Applying the DVS® technology, it is possible to retrieve the desired information from others measures available in the system/process. DVS® technology application has given successful results in: sensors cost reduction, measurement robustness, fault detection, recovery of physical sensor breakdowns, measure availability in different operating conditions.


Technology Overview

Thanks to the innovative methodology named NOSEM® (NOnlinear SEt membership Modeling), Modelway is able to design and develop mathematical models of complex and non linear systems directly from experimental data. In case of systems where physical laws are unknown or too complex to be modeled, NOSEM® technology reduces time and costs of design.


Technology Overview

The innovative STC® technology allows to design directly from the experimental data the parameters for automatic controls software applied to complex and non linear systems. Several application of internal model control, robust controls, model predictive controls accounting for non linearities and constraints have been realized in numerous projects in the automotive, aerospace and energy fields.