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Industrial Projects
ADASE: Model based predictive control and recommendations

Model based predictive control and recommendations

Examples:

Optimize efficiency of sugar extraction (joint with I. Oseledets)

Input X: Beet chips shape, quality, temperature, sugar content and flux; wash water temperature, pH and flux; the temperature inside the diffuser, etc.
Output Y: Costs, losses, efficiency of sugar extraction

Minimize fuel consumption of a cargo vessel, detect frauds with fuel, optimize expedition route

Input X: Dimensions (length, height, width), load capacity, type (ferry, barge, freighter, etc.), number of engines, etc.; Route data; information about weather and sea currents (historical, predictive and real-time); controls (vessel speed, etc.)
Output Y: Fuel consumption

Some challenges are the presence of heterogeneous data and noise, large volumes of high-dimensional stream data, missing values, outliers/incorrect values, etc.

mountains

Large-Scale Shape Retrieval and Classification via 3D Deep Neural Networks

Examples:

3D data is widespread, e.g.

  • 3D CAD models
  • Remote sensing data from satellites
  • 3D medical images, etc.

For applications it is necessary to

  • Recognize/categorize 3D shapes (e.g. CAD models)
  • Retrieve similar shapes
  • Predict characteristics of 3D objects

Used methods

  • Voxelization
  • Sparse 3D convolutional deep neural networks
  • Local features based on differential geometry

Some challenges are the presence of heterogeneous data and noise, large volumes of high-dimensional stream data, missing values, outliers/incorrect values, etc.

Applications of massive data processing and predictive maintenance technology

  1. Prediction of failures in auxiliary power units
  2. Aerodynamic design of efficient layouts for passenger aircraft
  3. Design of the side panel of F1 car

Applications of deep learning technology

  1. 3D data processing
  2. 3D shape recognition

Development & implementation of software libaries

pSeven Core (MACROS Library)

The library was developed for optimization and modeling of surrogate functions in collaboration with DATADVANCE

Quality assurance:

  • Technology Readiness Level 6 (NASA classification)
  • According to Airbus experts , application of MACROS “provides the reduction of up to 10% of lead time and cost in several areas of the aircraft design process”
  • Several joint projects with industry partners have been successfully completed

Use cases:

  • Structural analysis of composite stiffened panels on aircrafts
  • Aerodynamic design of layouts for passenger aircrafts

Library for predictive maintenance (PHM core)

Quality assurance:

  • Technology Readiness Level 5 (NASA classification)

Use cases:

  • Airplanes leakage detection
  • APU failures prediction
  • Oil filter clogging detection
  • Software-intensive systems: detection of outages of internet user services